esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-\(\omega\)
## 4 Results and Discussion  
The QPGPSO-\(\omega\) algorithm proposed in this study was utilized to tackle environmentally significant EDPs. The EDP scenarios considered include power systems comprising solely RESs, as well as thermal systems incorporating CEED. The simulation parameters employed for the algorithm throughout the paper are specified in Table 1, unless mentioned otherwise.  
Figure 1: Flowchart of the proposed QPGPSO-\(\omega\).  
The values of the unique parameter \(r\) and cognitive and social components \(c_{1}\) and \(c_{2}\) are given in Table 1. The simulation for the accomplishment of the optimum cost values for the considered EDPs was performed in MATLAB with versatile characteristics [63].  
QPGPSO-\(\omega\) was employed for solving various EDPs of RESs, along with the CEED problem. The combinations of sources for the test cases are given below:  
* Only RE-based EDPs solved through QPGPSO-\(\omega\) (15 units);
* Only RE-based EDPs with time-varying loads solved through QPGPSO-\(\omega\) (hourly variation);
* Only RE-based EDPs with MAED/RLSD solved through QPGPSO-\(\omega\);
* TPPs of ten units with CEED, losses, and VPL solved through QPGPSO-\(\omega\).  
To obtain the optimal cost values and identify the best solution among the compared optimization techniques, the algorithm was utilized to solve the mentioned EDPs. Multiple trials were conducted, amounting to up to thirty iterations, with varying values of the parameters listed in Table 1.  
Throughout these trials, the algorithm aimed to minimize costs while considering the operational constraints imposed by the EDPs. The algorithm's performance was evaluated based on the achieved cost and emission values, ensuring that both economic and environmental factors were taken into account.
# Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5
###### Abstract  
The aircraft engine is a core component of an airplane, and its critical components work in harsh environments, making it susceptible to a variety of surface defects. To achieve efficient and accurate defect detection, this paper establishes a dataset of surface defects on aircraft engine components and proposes an optimized object detection algorithm based on YOLOv5 according to the features of these defects. By adding a dual-path routing attention mechanism in the Biformer model, the detection accuracy is improved; by replacing the C3 module with C3-Faster based on the FasterNet network, robustness is enhanced, accuracy is maintained, and lightweight modeling is achieved. The NWD detection metric is introduced, and the normalized Gaussian Wasserstein distance is used to enhance the detection accuracy of small targets. The lightweight upsampling operator CARAFE is added to expand the model's receptive field, reorganize local information features, and enhance content awareness performance. The experimental results show that, compared with the original YOLOv5 model, the improved YOLOv5 model's overall average precision on the aircraft engine component surface defect dataset is improved by 10.6%, the parameter quantity is reduced by 11.7%, and the weight volume is reduced by 11.3%. The detection performance is higher than mainstream object detection algorithms such as SSD, RetinaNet, FCOS, YOLOv3, YOLOv4, and YOLOv7. Moreover, the detection performance on the public dataset (NEU-DETI) has also been improved, providing a new method for the rapid defect detection of aircraft engines and having high application value in various practical detection scenarios.  
a Article  
Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5  
1  
YOLOv5
# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Urban Subway Tunnel
#### 3.2.2 Prediction of Settlement Value
[74] used LSTM, deep neural network (DNN), the K-nearest neighbor algorithm (KNN), Gaussian process regression (GPR), support vector machine (SVR), DT, and linear regression (LR) based on three engineering parameters (tunnel width, tunnel depth, and construction method), and three soil parameters (elastic modulus E, friction angle, and cohesion C) were used to study the maximum land subsidence of 300 datasets of urban subway tunnels in Iran. The prediction results were analyzed via the K-fold cross-validation method, which showed that DNN, LSTM, and GPR had the best prediction results 99.37%, 98.96%, and 96%, respectively. In addition to the RNN model, other intelligent algorithms have appeared to solve the problem of land subsidence prediction. [PERSON] et al. [75] proposed an adaptive neural fuzzy inference system (AFIFIS) to solve the problems of weak generalization ability and low correlation between parameters of intelligent algorithms such as neural networks, genetic algorithms, and grey systems. The system integrates the adaptive ability of the deep network model and the expression ability of the fuzzy system, and it comprehensively considers the relationship between the model and the key control parameters, thus providing a new solution for the rapid prediction of ground settlement during subway shield construction. [PERSON] et al. [76] used the finite difference method to build a digital intelligent model for a section of Tehran Metro Line 7. Gene expression programming (GEP) was chosen to express the mathematical equation. The mathematical equation derived from the GEP model is written in visual basic language to estimate the land subsidence caused by tunnel construction. Currently, urban subway tunnel construction aims for the integrated and coordinated development of underground spaces and rail transport, driven by the initial promotion of \"dual-carbon strategy\" and \"resilient city\" concepts. To construct an evaluation system for future urban metro tunnel development, it comprehensively applies non-linear theories, including deep learning, fuzzy analysis, and the analytic hierarchy process, aiming to achieve low-carbon, intelligent, and sustainable urban and rail transport development. However, existing neural network models mainly rely on monitoring data for calculations during the training process and fail to incorporate the underlying physical laws of engineering problems, leading to poor model interpretability and a lack of generalization in analysis results. Therefore, there is a need to establish a physics-informed neural network (PINN) model [77] that integrates both a physics model and a data-driven model. By deeply analyzing the relationship between data laws and physical mechanisms in conjunction with known physics mechanisms and the deep learning framework, this approach aims to facilitate applied research in complex engineering scenarios.
## 4 Architecture Distribution Characteristics
### Sedimentary Model  
By refining and summarizing the research findings on the distribution characteristics, quantitative scale, and stacking patterns of single sand body architecture in the study area, this paper establishes a sedimentary architecture model of the alluvial fan fringe derived from southern provenance. The sand bodies in the study area are widely distributed, with fine-grained, well-sorted sandstones predominantly consisting of small-scale, high-energy channel-prograded lobe deposits, which conform to the gentle-slope alluvial fan model. The alluvial fan fringe primarily develops architectural elements such as channels, lobes, lobe margins, and mudstone.  
The channels in the study area are small in scale and extend over long distances along the sediment transport direction. Channel development involves multiple stages of in situ incision, with few channel bifurcations. The combination of channels and lobes appears elongated and sinuous, with weak lateral amalgamation and insignificant vertical aggradation. Under the established sedimentary model of this area, as rivers exit the mountain front, the slope of the foreland hills is relatively gentle. The main channel continuously advances away from the sediment source, and due to the gentle slope, channel flow directions become variable, gradually forming branches. The lobes advance in a divergent pattern around the channel. Once a lobe is formed, new lobes can be generated by channel branching, cutting into previously formed lobes.  
In terms of lobe dimensions, lobes closer to the sediment source are wider and thicker, with a lower width-to-thickness ratio. As the channel advances and hydrodynamic energy decreases, lobe thickness decreases, connectivity increases, and the width-to-thickness ratio gradually increases. Near the source, lobes are relatively isolated, but as channels branch, they become increasingly interconnected and stacked. The river network in this model is highly developed, with small-scale channels. Successive periods of channel advancement result in complex spatial arrangements of channel-lobe combinations. Over time, the channel-lobe assemblages undergo frequent lateral migration across the alluvial fan fringe, but the degree of lateral overlap remains low, and the recurrence interval of channels at the same location is relatively long (Figure 7).  
Figure 6: Width-thickness relationship of lobes in the study area(WTR, Width-Thickness Ratio).
# Interdisciplinary Urban Tunnel Control within Smart Cities
## 2 Systemic Approach to Urban Road Tunnel
### Sustainable Target Functions of an Urban Tunnel System  
Tunnels are a specific part of the road network. Often, they are considered a closed system. We will demonstrate the need for their better integration not only into the road traffic network, but also to other fields such as energy management and environmental impacts.  
It is more expensive to reach the same safety level in tunnels than on open roads. In case of an accident, the emergency response is far more difficult and human lives are in danger more often [5].  
The primarily objective of road tunnels is to enhance the urban road network and thus help to realize transport performance between two points (origin-destination) expressed in vehicle kilometers travelled (VKT).  
The transport performance however cannot be the only measure of quality. The tunnel (actually similarly to the rest of the road network), must follow the principles of sustainable development [6] as depicted in Figure 1.  
Our target function of the tunnel system \(\Psi\) seeks a minimum of the qualitative index \(\Pi\)**I**  
\[\Psi=\mathbf{min}\Pi \tag{1}\]  
The performance Index (\(PI\)) has three parts  
\[\Pi=\left(\mathbf{C_{e}}\ +\mathbf{C_{p}}+\mathbf{C_{n}}\right) \tag{2}\]  
where \(\mathbf{C_{p}}\) is the operating costs. The decisive item is usually the price for energy for ventilation and lighting, but it also includes service costs and costs for replacement of equipment after the end of its life, labor costs of staff, and others. \(\mathbf{C_{e}}\) is the cost for environmental impacts, including the cost for protection from the air pollution and others. This has also clear link to traffic performance, as congestions have clear negative impact on the produced emissions (for example CO2 emissions [7]). \(\mathbf{C_{n}}\) is the cost of traffic accidents (in general the safety impact), which consists of the material damage, cost of the emergency response, but also the cost for personal injury or death.
## 3 Numerical Parametric Study
### Cable with HDR Damper and TM-HDR-D
The damping effect is closely related to the installation mass and location of the TM-HDR-D. Specifically, the increase in the system's modal damping ratio is proportional to the damper's mass. Meanwhile, with the TM-HDR-D close to the cable's middle point, the damping is increased the most in the first mode, as depicted in Figure 8.  
Figure 9 shows the system damping ratio of a cable with two dampers versus the TM-HDR-D location. Let the TM-HDR-D loss factor \(\varphi\) be 0.5 and the frequency tuning ratio \(\rho=0.5\). In addition, let the HDR damper's loss factor \(\varphi\) be 0.5, and the location parameter \(x_{1}/L\) is 0.02 and 0.05 in Figure 9a,b, respectively. From Figure 9, it can be seen that the optimal TM-HDR-D installation location for the cable's second modal damping is near the location \(L/4\)--especially closer to the quarter point of the HDR damper. The maximum point is marked with a solid circle in Figure 9. It should be noted that when the TM-HDR-D is close to the cable's midpoint, the damping contribution of the TM-HDR-D is almost zero. Therefore, the TM-HDR-D should not be installed at the node of the cable's modal shape in practical applications. Figure 6: Modal shapes of cables with different dampers. Figure 7: Second modal damping of a cable with two dampers versus the HDR damper stiffness \(\overline{K}_{1}\) (\(\varphi=0.5\), \(\rho=0.5\), \(x_{2}/L=0.25\)), with (**a**) \(x_{1}/L=0.02\) and (**b**) \(x_{1}/L=0.05\).
# Exploring the Impact of Ambient and Character Sounds on Player Experience in Video Games
## 2 Experiment 1
### Method
#### 2.1.3 Procedure and Design  
The study followed a 2 \(\times\) 2 within-subject design with character sounds (on/off) and ambient sounds (on/off) as independent variables and was conducted in a soundproof booth (Studiobox premium) using a Lenovo Ideapad Flex 5 laptop (Lenovo, Hong Kong) and Valco VMK20 headphones (Valco, Tampere, Finland). The experiment was split into two parts for each participant, each part containing two gaming phases lasting ten minutes each. Character sounds varied across the two parts, being turned on in one and off in the other, while ambient sounds varied within each part but across gaming phases, being turned on in one and off in the other phase. The order of sound variations was counterbalanced across all participants. Due to the high obtrusiveness of the character sound variation, participants listened to a piece of classical music (3 % minutes of [PERSON]'s Nocturne op.9 no.1 in B-flat minor) in the break between the first and second parts, making it less likely that they would remember the earlier sound conditions. The music piece was chosen because it does not contain lyrics or display a mood or theme similar to the game. The sound volume was kept at a constant and comfortable level for all participants.  
At the beginning of the study, participants first gave informed consent and supplied sociodemographic information as well as information about their gaming experience. Before the first gaming phase, participants received a short explanation of the game, explaining the controls and reminding them of the goal to kill as many enemy minions as possible with a _last hit_. Each gaming phase started anew at the starting point of the map, and after each of the four gaming phases, participants filled in the questionnaires on immersion, flow, avatar identification, fun, and perceived competence. The questionnaires were laid out in front of the laboratory so participants would not notice the experimenter changing the sound settings for the next gaming phase. In the short break between the two parts, participants listened to a piece of classical music. At the end of the experiment, participants were additionally asked how important sound was for them in general and in video games specifically.
# Image-Based Analysis of Plate Separation and Clearance Distribution in Wet Clutches
###### Abstract  
A fundamental understanding of wet clutches' drag loss behavior is essential for designing efficient clutch systems. It has been widely recognized that the separation behavior immediately after disengaging the clutch and the resulting clearance distribution influence the drag loss behavior. However, these influencing factors have yet to be systematically investigated. Therefore, this study aimed to experimentally investigate the effects of plate separation and clearance distribution on drag loss behavior under different operating conditions and modes. For this purpose, image series of an operating clutch system were recorded and subsequently analyzed using image processing algorithms to evaluate the movements of the plates. Based on this, the effects on drag loss behavior were analyzed. The investigations were carried out on a clutch system used in an industrial application. The measurements show that the axial movements of the plates comprise main and superimposed non-periodic movements of much a smaller amplitude. The separation of the plates is primarily driven by the applied differential speed so that the set total clearance is only utilized mainly in the higher differential speed range. The separation behavior, therefore, decisively influences the drag loss behavior. The plates can even remain in contact in the low differential speed range. The investigations also showed that the separation behavior and, thus, the drag loss behavior can be improved by using waved plates, especially in the low differential speed range. It was also found that a high plate number and a large set total clearance support a non-uniform clearance distribution. Based on the investigations conducted, it is possible to expand our fundamental knowledge of separation behavior and clearance distribution, allowing for a reduction in the drag losses of wet clutches. The findings can thus contribute to the development of low-loss and compact clutch systems.  
applied image processing; wet clutches; drag losses; plate separation Article
###### Abstract  
We investigate the optical absorption and scattering properties of four different kinds of seawater as the quantum channel. The models of discrete-modulated continuous-variable quantum key distribution (CV-QKD) in free-space seawater channel are briefly described, and the performance of the four-state protocol and the eight-state protocol in asymptotic and finite-size cases is analyzed in detail. Simulation results illustrate that the more complex is the seawater composition, the worse is the performance of the protocol. For different types of seawater channels, we can improve the performance of the protocol by selecting different optimal modulation variances and controlling the extra noise on the channel. Besides, we can find that the performance of the eight-state protocol is better than that of the four-state protocol, and there is little difference between homodyne detection and heterodyne detection. Although the secret key rate of the protocol that we propose is still relatively low and the maximum transmission distance is only a few hundred meters, the research on CV-QKD over the seawater channel is of great significance, which provides a new idea for the construction of global secure communication network.  
discrete modulation; seawater channel; continuous-variable quantum key distribution +
Footnote †: journal: Applied Sciences
## 1 Introduction
It was found that longer growing seasons can eventually enhance vegetation growth [40]. Specifically, a 1-day extension in LOS can result in an increase in annual GPP by 5.8 g C m\({}^{-2}\) yr\({}^{-1}\) across the Northern Hemisphere [33]. This effect is particularly pronounced in shrubland ecosystems, where a 1-day extension leads to an increase of 6.44 g C m\({}^{-2}\) yr\({}^{-1}\) in annual GPP [41]. A more insightful perspective suggested that the combined effects of growing season length (LOS) and maximal photosynthetic capacity (GPPmax) on annual GPP can be expressed by constructing a statistical model. This model integrates plant phenological and physiological properties into a robust index--the product of the CO\({}_{2}\) uptake period's length and the seasonal maximal photosynthesis--which serves to explain the GPP variability across space and time in response to climate extremes and during recovery after disturbance [42; 43]. LOS and GPPmax together account for over 90% of the temporal GPP variability in most regions [44]. However, the impact of phenology changes on the spatiotemporal variations of boreal vegetation productivity is currently ambiguous. The objective of this study was to (1) investigate long-term changes in vegetation phenology and GPP in the boreal territorial ecosystem over the period 1982-2018, (2) assess the impact of phenology variation on GPP changes using a statistical model, and (3) quantify the contribution of phenology extension on GPP increase by applying the Relative Increment Effect (RIE) metric. Understanding the interaction between boreal vegetation phenology and productivity is crucial for insights into climate-vegetation feedback mechanisms in the boreal terrestrial ecosystem.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology
### Dataset Configuration for TinyML Evaluation  
In this study, we have thoroughly assembled a dataset to effectively evaluate our TinyML algorithms within a comprehensive IoT framework. This dataset is characterized by its diversity and volume, mirroring the complexities encountered in large-scale IoT systems. The following are the key aspects of our dataset:  
* **Sensor Array Composition:*
* _Environmental Data:_ Sourced from DHT22 sensors, providing continuous insights into temperature and humidity.
* _Motion and Orientation Data:_ Collected via MPU6050 sensors, capturing detailed information on movement and angular positions.
* _Light Intensity Measurements:_ Obtained from LDR sensors, these readings reflect variations in ambient lighting conditions.
* _Distance and Proximity Data:_ Acquired from HC-SR04 ultrasonic sensors, essential for spatial analysis and object detection.
* _Soil Moisture Levels:_ Recorded by specialized sensors, pivotal for applications in smart agriculture.
* **Data Volume:*
* The dataset encompasses over 1 terabyte of collected raw sensor data, providing a substantial foundation for algorithmic testing and optimization.
* **Data Collection Frequency:*
* Sensor readings are captured at varying intervals, ranging from high-frequency real-time data streams to periodic updates. This variability simulates different real-world operational scenarios, ensuring robust algorithm testing.
* **Data Preparation:*
* Prior to analysis, the data were subjected to essential preprocessing steps, including cleaning and normalization, to ensure consistency and reliability for subsequent TinyML processing.  
This dataset, with its rich variety and significant volume, plays a crucial role in the assessment of our TinyML algorithms. It not only provides a realistic environment for testing but also ensures that the algorithms are evaluated across a range of conditions reflective of real-world IoT systems. The frequency of data collection, in particular, allows us to examine the algorithms' performance under various data flow scenarios, which is critical for their application in diverse IoT settings.
# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 1 Introduction
First, Node Monitoring and Critical Node Detection algorithm checks the entire network to determine the critical node before it fails which greatly improves and balances the network performance; Second, Network Integration and Message Forwarding algorithm mostly focuses on improving the QoS by handling the packet forwarding process that has the capacity to decide the source of the forwarding packet (either from sensor node or actor node). As, this algorithm introduces much easier process to deal with packet forwarding flow. In addition, accurate packet forwarding process reduces the latency and bandwidth consumption; Third, Priority-Based Routing for Node Failure Avoidance algorithm determines the significant information available in each forwarding packet. Based on the nature of the information, the packets are routed to the next node. As, this algorithm particularly handles the redundant data prior to routing the packets; Finally, optimized RSSI model is introduced that selects the different power strengths for each beacon in order to ensure  
Figure 1: WSAN Architecture. the proper delivery of the beacon to each node. This aims to reduce the latency and estimating the prediction of the node energy-level. As a result, QoS provisioning is maintained while extending the network lifetime.