esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Study on Seismic Failure Modes of Cable-Stayed Pipe Bridge Oil and Gas Pipeline Structure
#### 3.2.1 Cable-Stayed Pipeline Bridge: Types of Failure Modes of Oil and Gas Pipelines  
According to the seismic failure types of highway cable-stayed bridges, although the entire structure does not completely collapse and become destroyed under strong earthquakes, localized components within the structural system often experience failures, leading to overall structural failure. The tower, support, and cable anchor pier are frequently damaged components in highway cable-stayed bridges. Drawing an analogy from the damage observed in local components of highway cable-stayed bridges and based on the damage mechanism of pipe bridge structures mentioned earlier, in this study, we further analyzed the localized components of a cable-stayed pipe bridge oil and gas pipeline system. Specifically, we investigated the interaction between the support and the pipeline as a potential cause for pipeline damage and explored additional failure modes resulting from bridge tower deformation in order to gain insights into the failure mechanisms specific to cable-stayed pipe bridge oil and gas pipeline structures.  
The influence of the interaction between the support and the pipeline on the structural system  
The analysis of the overall deformation of the pipeline also considers the support located beneath it as a force component. According to seismic technical specifications for oil and gas transmission pipelines, if a span structure is utilized as a support element, longitudinal earthquake effects necessitate considering the longitudinal slip of the pipeline on the support. Therefore, further refinement of the model enables analyzing the interaction between the support and the pipeline in pipe bridge systems under longitudinal seismic waves, leading to potential failure modes of oil and gas pipelines.  
The influence of tower deformation on the structural system  
The bridge tower is the crucial load-bearing component of the cable-stayed pipe bridge, responsible for supporting the truss beam, pipeline, and other structural elements. A failure in the bridge tower would result in a significant reduction in the overall bearing capacity of the structure and may even render the entire pipe bridge system non-functional. Previous studies have indicated that excessive displacement at the top of the bridge tower can lead to structural overturning: however, there is currently no clear criterion for tower collapse. Based on existing research, when the displacement at the top of a cable-stayed pipe bridge's tower exceeds 1/100 th of its height, it is considered to have lost its carrying capacity. The structural system fails to function effectively when the bridge tower exceeds the serviceability limit state (SLS). The height of the bridge tower in this paper is 72 m; that is, when the displacement of the top of the bridge tower exceeds 0.72 m, the structure is deemed to pose a hazardous undertaking.
# Control of Flexible Robot by Harmonic Functions
## 4 Conclusions  
The control of a flexible robot as a representative of underactuated system with zero dynamics is performed using invariant control method. The basis of invariant control are harmonic functions, which are used for feedforward control of particular flexible system. The harmonic functions provide smooth motion as is needed for vibration cancellation when reaching the desired position. Presented here is an invariant control of flexible underactuated structure in connection with observing the desired trajectory.  
The control strategy uses motion frequencies inspired by so-called natural motion. The residual oscillations provide the natural motion frequency, which is used to cancel out unwanted vibrations. The control input is separated into two parts. The arbitrary part of the control input (\(\mathbf{w}_{A}\)) is designed to control the directly actuated part of the dynamic system. Theinvariant part of the control (\(\mathbf{w}_{I}\)) is selected to steer the system zero dynamics in the desired way. The invariant part of the control fulfils the invariant conditions with respect to overall control input behaviour. The amplitudes of chosen periodic functions are obtained by optimization process with the final position in objective function. The achieved accuracy depends on the optimization process inputs, the trajectory length, and the number of used frequencies. The control parameters are predicted using the optimization process and resulting tracking accuracy is the same for the same trajectory.  
The main aim of this work was to present the functionality of an invariant control approach to a system with flexible elements. Further research will focus on the extension of the invariant control by feedback to avoid the optimization process. The proposed concept will be enhanced by the model predictive, wave-based or sliding mode control to secure missing feedback. To fulfil the invariance conditions (16) the control strategy will  
Figure 7: System input torque \(u_{2}\) behaviour for two types of control—computed torques and invariant control and their difference.  
be formulated in discrete form.. The presented control can be practically applied in serial robotics, i.e., robotic manipulators or machine tools.  
Conceptualization, M.V. and M.N.; software experiment, Z.N.; writing--original draft preparation, Z.N. and M.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Regional Development Fund under the project Robotics for Industry 4.0 (reg. no. CZ.02.1.01/0.0/0.0/15 003/0000470).  
The authors declare no conflict of interest.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 3 IoT-over-Satellite Performance Modeling  
In this section, we describe the proposed modeling approach that will be utilized to evaluate the IoT-over-Satellite performance. We start by using the Simplified General Perturbations (SGP4)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Company** & **Satellite Constellation** & **Frequency Band** & **Coverage** & **Transmit Power [dBm]** & **Targeting User** \\ \hline \hline Iridium & constellation & 1616–1626.5 MHz & worldwide & less than 32 & commercial \\ Globalstar & constellation & UHF, SHF & worldwide & 18, 25 & commercial \\ OrboComm & constellation & L-Band & worldwide & less than 37 & commercial \\ Myriota & 15 satellites & VHF, UHF, ISM & worldwide & 27, 27, 14 & commercial \\ Fosssast & single satellite & ISM 436.7 MHz & an hour per day & 16 & educational \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different practical Internet of Things (IoT)-over-Satellite systems.  
orbital predictor [10] to obtain the radio channel performance of both the desired IoT signal and the underlying interference. Without loss of generality, we take Australia as a typical simulation example where measurement points, that is, simulated IoT devices and interferes, are uniformly distributed on a grid. The received power by the satellite for all measurement points is calculated by applying a detailed channel model that takes into consideration several parameters; air absorption [20], free path loss, excessive path loss caused by urban structures [21], and the probability of line-of-sight [22].  
The average interference power of the observation points is calculated as the sum of all interference terms generated from the co-channel transmitters existing in the selected ISM band (915-928 MHz). Accordingly, we can obtain the uplink SINR for all measurement points within a given simulation time period. We evaluate the performances of practical IoT technology (namely LoRaWAN) by applying a mapping method that predicts the packet error rate based on the SINR value. This prediction is based on empirical measurements from the literature [23], in which the packet error rate can be estimated by the fitting table in the following subsection.  
An overview of the proposed workflow of the performance evaluation model is shown in Figure 1 as a block diagram, while all evaluation parameters are listed in Table 3. In the following subsection, we further elaborate on the performance modeling approach.
## 5 Results Evaluation and Discussion
### Scenario Development  
In order to evaluate the CSFSA's performance, it is divided into two simulation scenarios i.e., CoAP with proposed CSFSA and CoAP-DTLS implementation scenarios. CoAP with proposed CSFSA scenario comprised of 5 motes and 1 border router. Here motes are CoAP servers which are developed on proposed IoT protocol stack and deployed in agriculture field for control and monitoring of various agriculture parameters. The mote 1 is the border router while motes 2-6 running CoAP servers and all the motes are placed in the transmission range of border router. The border router is used for connectivity with external network (internet) through tunslip utility. The purpose of CoAP servers are monitoring and controlling of environmental conditions of agriculture field and send it to remote server i.e., CoAP client application. The CoAP client is Cupper based browser which is used for browsing and direct interaction with CoAP resources. The CoAP with proposed CSFSA scenario is emulated in Cooja as shown in Figure 10. While in CoAP-DTLS scenario, mote 1 is border router and motes 2-6 are CoAP servers and all the motes are placed in the transmission range of border router as shown in Figure 11. The CoAP-DTLS scenario is used for results comparison with proposed CSFSA framework based CoAP.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Operating System & Contiki-NG \\ Simulator & Cooja \\ Application Layer Protocol & CoAP \\ Network Layer Protocol & 6 LoWPAN \\ Link Layer Protocol & IEEE 802.15.4 \\ MAC Layer Protocol & CSMA \\ Routing Protocol & RPL \\ Radio Propagation Model & UDGM Distance Loss \\ CoAP Client & Cupper based browser \\ Compiler & Msp430-gcc (in Ubuntu) \\ Sensor mote & Wismote \\ Number of motes & 5 \\ Data rate & 250 kbps \\ Tx/Rx Ratio & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm
## 1 Introduction
Conversely, the RRT algorithm is better suited for non-linear dynamic scenes, but it does not guarantee the optimal path [24]. Multiple enhancements have been suggested for the fundamental RRT framework, such as lazy-RRT, RRT-connect, and fixed-node RRT (FN-RRT) [25]. Nonetheless, nearly all RRT-based approaches merely produce non-optimal path solutions. To address this issue, the RRT* algorithm is a variant of the original method, which updates neighboring node routes by adding new nodes to the tree. This approach ensures asymptotic optimization results and is more likely to achieve optimal outcomes [26]. Nevertheless, the RRT* approach exhibits high computational complexity and low efficiency, making it challenging to apply in underwater environments. In recent years, reinforcement learning and deep reinforcement learning methods have been gradually applied to underwater vehicle path planning, but they require a certain amount of computing power [27, 28, 29]. Due to the distinctive terrain undulations, relatively sparse obstacles, and the heightened likelihood of novel impediments inherent in marine environments, the aforementioned methods may prove unsuitable for underwater conditions. Path-planning methodologies for underwater environments should be swifter and more adaptive. In response to this challenge, this paper proposes an enhanced RRT* algorithm tailored for underwater path planning. This algorithm expedites the search process and bolsters the adaptability of the algorithm to dynamics. * This paper presents a novel approach to path planning, leveraging the principles of bidirectional path planning and cached RRT* algorithm. To expedite the search process, we employ a bidirectional search strategy using extended random trees, facilitating the swift identification of both start and target points as well as efficient path connectivity. * In our proposed methodology, the path computed during the preceding step serves as a cached representation of the optimal route. Subsequently, when novel obstacles emerge, we efficiently modify and sever the invalid portions of the cached path, thereby minimizing computational efforts while simultaneously enhancing the dynamic adaptability of the algorithm. * A certain amount of simulation experiments have been carried out. This reflects that my proposed method is currently the best method, such as the fast exploration random tree star algorithm, in several critical metrics including path node density, planning time, and dynamic adaptability.
## 3 Results  
### Characterization of Spatial and Temporal Variations of WUEc  
Analyzing the interannual trend, Xinjiang's WUEc showed an increasing trend from 2002 to 2007, followed by a decreasing trend from 2008 to 2022 (Figure 3a). From 2002 to 2007, Xinjiang's WUEc increased by an average of 0.05 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\) per year, with a total increase of 0.3 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\). From 2008 to 2022, Xinjiang's WUEc decreased by an average of 0.01 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\) per year, with a total decrease of 0.15 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\).  
Intra-annual variation analysis shows that Xinjiang's WUEc exhibits a distinct bimodal pattern, with peaks in May and September or October (multiyear mean values consistently  
Figure 3: Inter- and intra-annual variations of WUEc in Xinjiang from 2002 to 2022, **(a)** the slope of the interannual trend component of WUEc, **(b)** intra-annual variations of WUEc, The color gradient from blue to red represents the change in years from 2002 to 2022.  
exceeding 2.5 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)). Xinjiang's WUEc was lower in winter and early spring, generally below 0.5 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\). Over time, Xinjiang's WUEc not only showed an overall decrease during the growing season (April-October), but also saw the maximum WUEc shift from September to October (Figure 3b).  
The average WUEc value in Xinjiang from 2002 to 2022 was 2.02 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\), exhibiting an overall distribution pattern with higher values in the southwest and lower values in the northeast (Figure 4a). Specifically, Kashi and Hetian have higher WUEc (with values concentrated between 1.96 and 2.26 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)), while Changji and Karamay have lower WUEc (with values concentrated between 0.33 and 1.66 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)). The overall WUEc in Xinjiang exhibited a decreasing trend. Specifically, 52.26% of the region showed a decreasing trend, covering most of the study area. A significant decreasing trend was observed in 20.81% of the region, mainly in the northern part of Yili and Kelamayi. In contrast, 19.86% of the region exhibited an increasing trend, mainly in the southern part of Aksu, Yili Prefecture, and Kashi. Only 3.7% of the region showed a significant increasing trend in WUEc, primarily in Kashi (Figure 4b). The overall trend of WUEc in Xinjiang from 2002 to 2022 was a decrease, indicating a decline in the efficiency of the carbon and water cycle in cropland during this period.
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 6 Experimental Results and Discussion
### Baseline Architectures  
We consider a single-branch DL architecture and a multi-branch DL architecture shown in Figure 5 as our baseline architectures for the performance comparison. The classifiers in these two architectures are similar to those used in the proposed MSE feature fusion as shown in Figure 1 and explained in Section 4.3.  
* For a single-branch DL architecture, all available sensor data will be combined first before we extract a set of features [13]. Here, all sensor data \(\mathbf{X}^{(n)}\) (from \(N\) IMUs) are concatenated together along the channel dimension. We denote this new array as \(\mathbf{X}\in\mathbb{R}^{1\times L\times NM}\). Thereafter, a 1D CNN model extracts a set of feature maps \(\mathbf{A}\in\mathbb{R}^{1\times W\times C}\) which will be used in the classification process.
* A multi-branch DL architecture consists of \(N\) branches to receive the sensor data \(\mathbf{X}^{(n)}\) individually [19; 22; 23]. Each branch extracts a set of feature maps \(\mathbf{A}^{(n)}\) using a 1D CNN model. Here, we concatenate these \(N\) sets of feature maps together along the channel dimension and obtain a new array \(\mathbf{Y}^{mb}\in\mathbb{R}^{1\times W\times NC}\), which will be sent to the classifier.  
Note that the sensor data \(\mathbf{X}^{(n)}\) and feature map \(\mathbf{A}^{(n)}\) were defined in Section 4. The values \(W\) and \(C\) were shown in Table 7.  
Figure 5: Baseline architectures. (**a**) Single-branch architecture. (**b**) Multi-branch architecture.  
The performances of these architectures are evaluated by classifying the PAMAP2, DaLiAc, and DSAD datasets where three CNN models (including LeNet5, AlexNet, and VGG16) are used as feature extraction. The accuracy scores are shown in Table 8, which will be compared to those achieved by the proposed architectures. We observe that the single-branch architectures outperform the multi-branch architectures in many cases. A reason is that the multi-branch architectures have extracted too many features (the output of the GAP in the classifier) and some of them may be ambiguous. The number of features out of the GAP in the multi-branch architectures is equal to \(NC\) while the number of features out of the GAP in the single-branch architectures is equal to \(C\). As seen in Table 8, the single-branch architectures using AlexNet offer the highest accuracy scores of 98.77%, 97.60%, and 97.18% for the PAMAP2, DaLiAc, and DSAD datasets, respectively.
## 1 Introduction  
The intensifying global competition has made it imperative for manufacturing companies to maximize their production volume. The overall equipment efficiency (OEE) proposed by [PERSON] presented a standard for quantitatively measuring the performance of equipment, and numerous manufacturers have been using it to measure and improve productivity [1; 2; 3]. In particular, the number of manufacturers using OEE is increasing in large-scale automation for mass production [4; 5; 6; 7]. Accurate performance data of equipment is the crucial factor for success in maximizing productivity by maintaining and improving manufacturing efficiency. OEE is a macroscopic method that measures how well equipment is theoretically running compared to its full potential in the real world [8]. Even though OEE is an effective way of measuring equipment efficiency, the biggest concern for manufacturers is to instantly detect and improve the causes of efficiency degradation to maintain maximum efficiency. Semiconductor production sites are attempting prediction, classification, association analysis, and clustering using data mining methods. However, the purpose of most of the research is to improve the quality of wafers based on the defect map of the wafers. Very few studies are related to productivity improvement [9; 10; 11; 12].  
Statistical process control (SPC) methods, and the widely used analytical methods therein, have been commonly used with fault detection and classification data. SPC refers to a statistical methodology that examines critical variables that must be placed within certain specifications. However, the SPC has the limitation that only a few of the manufacturing processes affected by hundreds of factors can be investigated and controlled. In addition, since the SPC does not have clear criteria for distinguishing fault detection and classification data, it may obtain results that are not related to factors, and it is impossible to analyze in real-time. In this work, we created a time analytics model that classifies the causes of efficiency degradation in real-time based on the ideal time required for each process and the data provided by the equipment in the actual industry. The process time is divided into approximately four hundred detailed steps, and the causes are classified through normalizing and clustering processes based on a k-means algorithm [13; 14; 15; 16]. Further, true/false measures of performance were employed based on the confusion matrix. The existing equipment required one decade to analyze the cause of increased process time, but when applying the time analytics model, the cause of modern extreme ultraviolet (EUV) equipment, which is more complex than the existing equipment, up to 98.6% can be classified in real-time. Accurate and detailed classification of time will be the basis for continuously maintaining maximum efficiency by reducing the time and effort spent on detecting the cause of productivity degradation.
## 4 Experimentation
### Target Tracking Method Comparison Experiment  
In order to compare the tracking performance of IGPF, GPF and PF, the TB-50 sequences in the OTB2015 dataset were used. These 50 video images contained 11 typical  
Figure 2: Dynamic tracking crawling experiment platform.  
cases. They are occlusion (OCC), illumination variation (IV), low resolution (LR), motion blur (MB), fast motion (FM), out-of-view (OV), deformation (DEF), background clutter (BC), scale variation (SV), in-plane rotation (IPR) and out-of-plane rotation (OPR).  
The evaluation indicators used are the distance precision (DP) and overlap success (OS) of the OTB platform. The time and space robustness of the algorithm are also key indicators. Hence, there are three different evaluation methods for DP and OS, respectively. They are one-time pass evaluation (OPE), time robustness evaluation (TRE) and space robustness evaluation (SRE). The DP refers to the difference between the center position of the target obtained by the tracking algorithm and the real position of the human mark, expressed in Euclidean distance, and its unit is a pixel. The OS is the area ratio of the intersection of the target bounding box obtained by the value tracking algorithm and the manually marked target bounding box of the data set and the area of the union set. It can be expressed by the following formula:  
\[OS=\frac{|S_{C}\cap\ S_{T}|}{|S_{C}\cup\ S_{T}|} \tag{31}\]  
Among them, CS represents the target bounding box obtained by the tracking algorithm, TS represents the true value of the manually marked target bounding box, the calculation of the intersection of the two bounding boxes, and the calculation of the union of the two bounding boxes.  
The three algorithms are compared according to the above evaluation method on the OTB2015-TB50 dataset. Screenshots of some video frames are selected to perform the comparison, as shown in Figure 3. From the figure, it can be seen that the IGPF algorithm represented by the red box has better tracking performance than GPF (blue box) and PF (green box) in grayscale and color images with occlusion and insufficient light.  
Figure 3: OTB experimentation screenshot of parts of video frames in some datasets (TB-50 Sequences, [PERSON] et al. [28]).  
Figure 8: OS SRE curve.  
Figure 6: OS TRE curve.  
Figure 7: DP TRE curve.  
The DP rate average and OS average of the three algorithms are calculated. The performance improvement rate of the IGPF algorithm relative to the GPF algorithm is also calculated. The results are shown in Tables 1 and 2. It can be seen that the evaluation (OPE), robustness (TRE) and space robustness (SRE) are improved by more than 10%. Moreover, the differences between the TRE average and the OPE average of the DP and OS of the IGPF algorithm are significantly smaller. It indicates that IGPF is less affected by the start frame and initial position settings.