esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Example for Damper Design
### Targeting for Two Modes  
The first two modes are taken into account for the dampers' design in this section. An HDR damper is equipped near to the cable end, where \(x_{1}/L=0.02\), and the stiffness parameter is 1140 kN/m. Let the HDR damper and the TM-HDR-D have the same loss factor \(\varphi=0.5\), and the TM-HDR-D's mass is set to \(M=70\) kg. Figure 11 depicts the system modal damping ratio of the first two modes versus the TM-HDR-D location for various frequency ratios (\(\rho\)). Figure 11a-c correspond to the first two modal damping curves when the TM-HDR-D tuning frequency ratio \(\rho=0.7,0.8,\) and \(0.9\), respectively.  
As mentioned in the above section, the TM-HDR-D device is sensitive to the cable vibration frequency, and the value of the tuning frequency ratio \(\rho\) affects the distribution of the system's modal damping in the first two cable modes. When \(\rho\) is close to 1, its damping to the first cable mode is relatively large, and when the tuning frequency ratio is close to 0.5, its contribution to the system's second modal damping ratio is relatively large, as depicted in Figure 11. More specifically, when the value of \(\rho\) is 0.7 and 0.9, it is difficult for the system's modal damping ratio to meet the requirements at the same time in first two modes--especially when the tuning frequency ratio is 0.9 (see Figure 11c). To make the first two modal damping ratios reach the design value, the tuning frequency ratio \(\rho\) is suggested to be set to the intermediate value, e.g., 0.8, as depicted in Figure 11b. When the \(\rho\) of the TM-HDR-D is 0.8, to control the cable's wind- and rain-induced vibration, the system's damping level should be more than 0.005, and the TM-HDR-D should be attached to the cable at \(x_{2}=38\)-48 m or 108-117 m.  
Figure 10: Dampers’ design targeting the cable’s first vibration mode (\(\varphi=0.5,x_{1}/L=0.02,\rho=1\)): (a) parameter optimization of the HDR damper; (b) location parameter optimization of the TM-HDR-D.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 2 Related Work
### Using Multimodal Feature Fall Detection System  
Multimodal fall detection systems combine different data modality-based features to address individual limitations. [PERSON] et al. utilized CNN for spatial information from images and LSTM for temporal information from signals, achieving 98.31% accuracy on the UP-Fall detection dataset, with a 10% improvement in other metrics [47]. [PERSON] et al. employed a CNN for visual patterns from images and a ConvLSTM for time-series sensor data, achieving 97.61% accuracy on the UP-Fall detection dataset for classifying 11 activities [48]. [PERSON] et al. proposed a system that combined a hybrid descriptor to recognize human activities [49] by removing noise from sensor signals and video frames, extracting movement and skeletal model features, and using logistic regression for classification. Their system achieved 91.51%, 92.98%, and 90.23% success rates on the UP fall, UR, and Sisfall datasets, respectively. This paper proposes a multimodal fall detection system that integrates the GSTCAN model for skeleton and motion data with the Bi-LSTM-CA model for sensor data to enhance detection accuracy. However, skeleton-based systems also face challenges in achieving satisfactory performance due to interclass similarity and lack of effective features [12]. To overcome the problem, researchers have recently focused on combining the features from the multi-modal dataset [13; 14; 15; 16; 17]. However, their system still struggles with accuracy and efficiency due to weak long- and short-range feature relationships. To address these limitations, we propose a multimodal fall detection system using skeleton and sensor data, enhancing robustness, efficiency, and environmental adaptability.
## 5 Simulation Results
### Unsw-Nb15 Dataset  
We utilized the UNSW-NB15 dataset, developed by the Cyber Range Lab at the Australian Centre for Cyber Security (ACCS), University of New South Wales (UNSW) [42]. This dataset consists of nominal, numeric, and timestamp values for various features. The raw network traffic was captured using the IXIA Perfect Storm tool, which contains both normal and attack traffic packets. The features were generated using the Argus and Bro-IDS tools, along with various algorithms, including the class labels. Each sample was labeled as either normal traffic (class 0) or attack traffic (class 1), providing a binary classification of the output features. In our simulation, the UNSW-NB15 dataset included 100,574 instances with 43 features. For training, we used 76,500 instances (70% of the dataset), leaving 24,074 instances for testing. The training set consisted of 42 input parameters, with the 43 rd feature serving as the output parameter.
## 3 Results and Discussion
Using a laser diode produced significant statistical differences in germination, as well as in performance, for maize seeds [47]. In the present study, the pre-sowing seeds irradiated with blue (410 nm), red (632.8 nm), and green (532 nm) laser lights had significant effects on the seed yield and the harvesting index characteristics of the plant. The results of seed yield indicated that the blue laser light had a better influence on maize seed than without the light condition (the control experiment), whereas the influence of the blue laser light was higher than the other laser lights used. Long irradiation times and high wavelength can cause considerable damage to the seed structures. Furthermore, laser stimulation is a physical phenomenon that consists of absorbing and storing radiant energy by plant cells and tissues. The same phenomenon can be observed in the case of seeds when they first absorb the radiation energy and then transform it into chemical energy and use it for subsequent growth [48]. The basis of laser stimulation mechanisms in some physiological states of plants is synergism between the monochromatic and polarized laser light beam and the photoreceptors of light. The irradiation effect of the maize sowing seeds stimulates an increase in seed yield. An analysis of the yield structure showed that an increase in maize yield resulted from an increase in row number per ear, which, in maize, is the property that most often changes due to the activity of other yield-generating factors; the number of seeds per ear was the least changeable property. In the bio-stimulation of plants in their different phases of development, three classes of photoreceptors were discovered: phytochromes, phototropins, and cryptochromes. These photoreceptors absorbed different wavelengths, i.e., 600-750, 320-500, and 500-630 nm, respectively [49; 50; 51; 52]. One of the hypotheses about the energy absorption by the seeds is that light is absorbed by different crop stages in their life cycle; the light is then transformed into chemical energy, and the seeds use it for their subsequent growth processes [53]. Evidence of thermal changes due to laser irradiation was demonstrated by [PERSON] et al. [41], where an irradiated maize seed was placed in different containers. They found that the temperature of the seed was modified according to the optical and thermal properties of the seeds; it also depended on the container on which the seed was placed. A laser with a wavelength of 532 nm and 25 mW of power was applied in soybeans (_Glycine max_), resulting in early emergence during germination when compared with the control group [54]. The vegetative growth of soybean seedlings was significantly higher after laser treatment; the magnitude of the stimulation for seedling growth was a function of the time exposed to laser treatment [17]. [PERSON] et al. [55] stated that laser treatment could be used to control the fungal infection in wheat seeds and improve their growth and development. Table 3 summarizes the progress of studies on the effects of lasers on maize according to recent studies.
## 4 Experimental Results
### Experimental Results on UCSB Dataset  
Figure 6 demonstrates the average time for different feature extraction methods along with proposed PTHOG without TOP-K key frame extraction on UCSB dataset, while Figure 7 illustrates the comparison between the different feature extraction methods and matching time on average with k (Number of Key Frame) = 10 and 15. PTHOG requires 12,329.1 milliseconds without TOP-K key frame extraction and 1761.3 milliseconds with TOP-K (k = 10) key frame extraction. From these experiments, we can see that PTHOG outperforms SIFT [5] and STPM [22] significantly, while it requires almost similar amount of time to LAID [30], LBP [31] and HOG [28].  
Figure 8a demonstrates the confusion Matrix for the proposed method on UCSB dataset for image query while Figure 8b illustrates the confusion Matrix for the proposed method on UCSB dataset for  
Figure 6: Average time for feature extraction methods without TOP-K key frame extraction on UCSB dataset.  
Figure 7: (a) Comparison on the different feature extraction methods and matching time on average when k (Number of Key Frame) = 10 and (b) Comparison on the different feature extraction methods and matching time on average when k (Number of Key Frame) = 15.  
video query. For both the image and video query sunset category shows the best result among all the other categories, while Mission and bricks categories show the lowest mean average precision due to the complexity, noise and motion blur of the videos.  
Figure 9a represents the comparison between the proposed method and other existing approaches on the UCSB dataset for image query and Figure 9b represents the comparison between the proposed method and existing approach on the UCSB dataset for video query. The Proposed feature descriptor PTHOG shows 88.3% mean Average Precision (mAP) for image queries and 91.17% mean Average Precision (mAP) for video queries on UCSB dataset. From these experiments, we can see that, proposed feature extraction method outperforms LAID [30], LBP [31] and HOG [28] significantly by 7.2%, 11.5% and 6.6% respectively for image queries and 4.07%, 5.37% and 3.87% respectively for video queries, while it shows competitive comparison with SIFT [5] and STPM [22]. However, deep learning based method ImageNet [32] shows the best performance due to its discriminative power. Figure 10 shows the retrieval results for the UCSB dataset on 3 selected categories and in this experiment the query is an image. Based on this query image we retrieved top-4 similar videos from the database.  
Figure 8: (a) Confusion Matrix for proposed method on UCSB dataset for image query and (b) Confusion Matrix for proposed method on UCSB dataset for video query.  
Figure 9: (a) Comparison with existing approach on the UCSB dataset for image query and (b) Comparison with existing approach on the UCSB dataset for video query.
## 5 Numerical Simulations  
To evaluate the effectiveness and efficiency of the proposed control method, two simulation tests are conducted in the MATLAB/SIMULINK platform. Specifically, the first simulation is presented to examine the tracking control performance of the proposed approach for the single-link FJM with matched and mismatched disturbances as well as measurement noises. In the second simulation, a comparison study between the proposed method and the existing approaches are presented to show the superior performance of the proposed controller.  
The physical parameters of the single-link FJM in (1) are given as: \(M=0.25\) kg, \(g=9.8\) m/s\({}^{2}\), \(L=0.45\) m, \(K=5\) N \(\cdot\) m/rad, \(I=0.05\) m/s\({}^{2}\), \(J=0.0005\) m/s\({}^{2}\). The gains of the controller (38) are chosen as \(k_{1}=7\), \(k_{2}=25\), \(k_{3}=8\), \(k_{4}=8\). Meanwhile, the parameters of the SCFs (4) are chosen as \(\omega_{n}=1100\), \(\zeta=0.8\), and the parameters of the proposed RESOs in (8) and (9) are: \(\beta_{1}=50,\beta_{2}=50\).  
The initial condition of the FJM is set as \(x(0)=[0.5,0,0,0]^{\top}\). The initial states of the error compensation system and the SCFs are all zeros. The desired reference trajectory is chosen as \(y_{\rm d}=0.5(\sin t+\sin 0.5t)\), and the lumped mismatched and matched disturbances added for the two simulations are given as:  
\[\left\{\begin{array}{l}w_{1}=0.005\cos\dot{q}+0.3\sin(2\pi t)\\ w_{2}=0.1\dot{\theta}+0.3\cos(2\pi t)\end{array}\right. \tag{51}\]
# Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model
## 5 Conclusions  
This paper proposes a prediction model for the PM2.5 concentration: the CE-AGA-LSTM model. Taking the meteorological data and air pollutant data of Beijing in 2016 as an example, the CE-AGA-LSTM prediction model and eight other models were compared based on the MAE, RMSE and R\({}^{2}\) indexes. The CE-AGA-LSTM prediction model used five randomly selected time series data samples as input data to predict PM2.5 concentration. Its average results in terms of MAE, RMSE and R\({}^{2}\) were 14.49826, 21.88822 and 0.9508, respectively. Compared with the results of other models, the results showed that the CE-AGA-LSTM prediction model is feasible. The prediction accuracy of the model on the test set reached 92.8%.  
Despite its excellent performance, the model proposed in this paper also has some limitations: (1) the method in this paper only predicted the PM2.5 concentration of a single station, without considering the relationship between multiple stations in the whole city. Data of all stations in a single city can be integrated to further mine the spatial relationship between stations, so to achieve an accurate prediction of PM2.5 concentration. Data of a single site are more suitable for predicting the regional PM2.5 concentration of the site in the future. Selecting the primary site, mining the correlation between the primary site and each site and selecting the attributes would be more conducive to realizing the prediction of PM2.5 concentration of the whole city. (2) The prediction by the LSTM on stationary time series data was better than that of non-stationary data. Therefore, Empirical Mode Decomposition (EMD) was adopted to decompose and stabilize the input data, and better prediction results could be obtained. (3) In this paper, the data of the first seven days were used to predict the PM2.5 concentration of the next 24 h. We can also predict the PM2.5 concentration of the next month every day. However, for large-scale predictions, the results of the method described in this paper may not be good, so further research is necessary.  
Conceptualization, X.W.; methodology, X.W.; software, X.W. and C.Z.; validation, X.W. and J.Z.; formal analysis, X.W. and C.Z.; resources, X.W. and X.Z.; data curation, X.W. and J.Z.; writing--original draft preparation, X.W.; writing--review and editing, X.W.; project administration, C.Z.; funding acquisition, C.Z. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Universities Natural Science Research Project of Anhui Provincial, grant number KJ2021 ZD0118, and the University Humanities and Social Sciences Research Project of Anhui Provincial, grant number KJ2021A0993.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Methodological Characteristics  
The data analysis process was performed for all 15 studies at the individual level. Data used to compute effects sizes by CMA included means and standard deviations of non-wedge and wedge conditions for 13 papers [5; 9; 10; 12; 13; 15; 16; 17; 18; 19; 20; 27; 32] and combinations of the mean difference, T, and P values in the two remaining studies [11; 14]. The weight of lifting was 0 kg (free weight) [9; 10; 12; 13; 15; 17; 18; 19; 20], 1.36 kg [5], 10 kg [11], 20 kg [27], and 80% 1-RM [16; 32] during double-leg [5; 9; 11; 12; 13; 16; 27; 32] or single-leg squats [10; 17; 18; 19; 20], performed either statically [9; 10; 17] or dynamically [5; 11; 12; 13; 16; 18; 19; 20; 32]. With the exception of one study [17], no specific abnormalities in the lower extremities or foot region were reported [5; 9; 10; 11; 12; 13; 16; 18; 19; 20; 27; 32]. Knee flexion angles of 45\({}^{\circ}\)[10], 60\({}^{\circ}\)[17], and 90\({}^{\circ}\)[9] were implemented for static squats, while for dynamic squats, the knee flexion range varied from zero to <45\({}^{\circ}\) (shallow) [18; 20], <90\({}^{\circ}\) (medium) [5; 12; 14; 19], or >90\({}^{\circ}\) (deep) [11; 13; 15; 16; 27; 32]. The thigh muscles under investigation included the vastus medialis [5; 9; 11; 12; 13; 14; 17; 18; 19; 20; 32], vastus lateralis [5; 9; 12; 14; 17; 18; 19; 20; 32], vetus materials [5; 9; 10; 15; 18; 19; 27; 32], vetus femoris [9; 10; 15; 18; 19; 27; 32], biceps femoris [9; 10; 11; 13; 14; 18; 19; 27; 32], and semitendinosus [19]. Twelve of the fifteen eligible studies assessed the effects of the posterior wedge [5; 9; 11; 14; 15; 16; 17; 18; 19; 27; 32], while six assessed the anterior [5; 9; 12; 13; 14; 18], three assessed the medial [5; 18; 20], and two studies assessed the lateral wedge [18; 20].
# Current Context and Research Trends in Linear DC-DC Converters
## 1 Introduction
### Evolution of Power Supply Design Technology  
As shown in Figure 2, in the 1960s and early 1970s, power supplies were linear regulator designs having efficiencies in the range of 30-50% [2]. With the introduction of switching techniques in the 1980s, efficiency rose to 60-80%. In the mid-1980s, power densities were about 50 W/in\({}^{3}\)[3]. With the introduction of resonant converter techniques in the 1990s, this increased to 100 W/in\({}^{3}\). When high-speed and power-hungry processors were introduced during the mid-1990s, much attention was focused on transient response, and industry trends were to mix linear and switching systems to obtain the best of both technologies [2]. Low-dropout (LDO) regulators were introduced in 1977 to power noise-sensitive and fast transient loads in many portable applications [4]. In the late 1990s, power management and digital control concepts and many advanced approaches were introduced into power supply and overall power management solutions [3]. In addition to the two main technologies, SCCs (also known as charge pumps) are used. Charge pumps are lightweight, small converters that offer some attractive features such as ease of use, low cost, compactness, inductor-less architecture, and ruggedness. The voltage gain of a charge pump is fixed, and it depends on the number of switching capacitors and switches. Therefore, the desired output voltage is achieved by manipulating the capacitor connections. The output voltage can be either regulated or unregulated based on the architecture [5,6]. Charge pumps can drive output currents typically up to 250 mA due to their considerable output impedance. Table 1 provides a characteristic comparison of the performance of SMPS, charge pumps, and linear regulators.  
Figure 1: Block diagram of a generic power management system [1].  
Figure 2: Evolution of power supply design technology.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 4 The Proposed Framework for Travel Time Prediction
### Spatiotemporal Data Refinement and Categorization
#### 4.1.1 GPS Outliers  
GPS technology allows for the accurate determination of location, speed, and time. However, in dense urban areas, various factors such as tall buildings, signal interference, satellite geometry, and atmospheric effects can introduce errors and outliers in GPS measurements, leading to decreased accuracy [40]. It is essential to identify and address these outliers to maintain the quality and reliability of the GPS dataset.  
To mitigate the impact of GPS outliers, we employed the \"Select by Location\" tool in ArcGIS Pro, which enabled the selection of GPS entries that fell within the geographical bounds of the New York City (NYC) shapefile, ensuring that only valid and geographically relevant GPS data were included in our dataset. Figure 4 illustrates a sample of GPS  
Figure 3: Methodological framework of the preprocessing phase.  
data collected in February 2014, plotted alongside the NYC shapefile, demonstrating the distribution of the selected GPS entries within the city.
# Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds +
## 2 Literature Review
### Maintenance of IMMs and Their Components  
IMMs are widely used assets for which maintenance is challenging due to the high number of parameters and components involved in the injection process. Research has been carried out on ways to improve maintenance actions for IMMs and their components, particularly by exploiting process parameters monitored by sensors. In [16], the authors used the maximum entropy principle [17] together with a fuzzy technique to deal with only censored data uncertainty while incorporating the knowledge of the maintenance staff to reduce annual maintenance costs of IMMs. On the other hand, [18] used a copulas model that considered the dependence of different failure modes to optimize maintenance actions on an IMM.  
In [19], the authors presented an interesting work in which a statistical method based on kernel density was mixed with outlier detection for PdM in the case of the hydraulic pump of an IMM. Although an interesting path, the proposed approach is in an embryonic stage and lacks results. The process parameters of injection were analyzed by [20], who adopted the Nelson Rules [21] to detect abnormal patterns in the injection process. Other authors have exploited machine learning (ML) techniques to handle IMM maintenance. For instance, [22] employed several data mining techniques to forecast machine-related disruptions on IMMs, while [23] employed reinforcement learning for real-time production planning and scheduling in smart manufacturing of injection molding thermoplastics. The authors of [2] exploited ML classification algorithms to distinguish between optimal and borderline functioning of IMMs in order to trigger maintenance actions when borderline functioning is detected. Other research has focused on creating the necessary apparatus to monitor and collect relevant data for further analysis [24; 25], which is the basis for applying maintenance actions based on the actual condition of the equipment.  
One of the main fields within PdM is prognostics, in which the main focus is determining the number of working cycles a given industrial asset can perform before its failure [7]. One of the main challenges in such approaches is the fact that the sensor data often present stochastic patterns that do not allow for determination of the asset's degradation [26]. As is apparent from this overview of maintenance solutions for IMMs and their components, the majority of research has focused on classifying the working state of the assets or detecting anomalous behaviors, as there are no monotonic sensor signals able to describe the degradation process. In the present work, new features are created through the combination of anomaly detection and GFTs, enabling degradation assessment of injection molds by determining their failure probability. This contributes to the field of prognostics more broadly, as the same approach may be employed in other industrial use cases where the degradation of assets follows a stochastic distribution of abnormal events.