esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Field Experiment
### Verification of Proposed Image Processing Method
The proposed image processing method is expected to enhance the effective management of ladder installations on the climbing formworks by externally enabling the observation of ladder installation defects using UAVs. A work platform should be placed in the climbing formwork to guarantee the safety and movement of workers. It should be correctly installed without bending or deformation in accordance with the regulations. Moreover, the incorrect installation of a work platform can potentially lead to accidents, including falls or tripping incidents, when workers navigate the platform. When inspecting a work platform in a climbing formwork using UAVs, it is difficult to observe whether it is properly installed according to regulations owing to the construction safety net (Figure 15a). When the proposed image processing was applied to the original image, the work platform in the region delineated by a red circle was apparently inadequately installed (Figure 15b). Therefore, the proposed image processing method can easily be employed for safety management by observing the installation state of a climbing formwork plate from an external view. Figure 14: Installation status of climbing formwork ladder: (**a**) Original image; (**b**) After improvement. Figure 15: Installation status of work platforms: (**a**) Original image; (**b**) After improvement.
# Design and Simulation of On-Orbit Assembly System Based on Insect-Inspired Transportation
## 2 Scheme Design of Robot System
This ant behavior is of great significance for robots engaged in space assembly, as they require similar mechanical stability and flexibility in order to manipulate and bear various different objects in zero-gravity environments. Robots in space can leverage the characteristics of zero-gravity and free-floating to perform assembly, maintenance, upgrades, and other tasks more efficiently, safely, and flexibly. Therefore, by drawing on the principles of ant-carrying behavior, new ideas and methods can be provided for the assembly and maintenance of robots in space. The design of the robot was inspired by the way ants carry heavy objects in nature. Although ants are small in size, they can carry objects that are several times their own weight. Normally, ants use their front legs to grip objects and their hind legs to propel themselves backward. However, in narrow and elevated areas, they can lift and carry objects, as shown in Figure 2a. Based on this biological behavior, a robot system for on-orbit assembly was designed. The overall structure of the system consists of the robot structure and the assembly unit. Considering that the assembly unit has a hexagonal structure with the hexagon vertices as nodes and three assembly units on each node, the in-orbit assembly robot adopts a tripedal configuration, with three legs symmetrically distributed around the center. Each leg is equipped with a gripping mechanism at the end. The configuration diagram is shown in Figure 2b. Since the three legs are symmetrically distributed around the center, any two legs can perform a patrol motion on the assembly unit. The robot's degree of freedom distribution is shown in Figure 2c. When the in-orbit assembly robot performs a patrol motion, its structure is similar to that of a bipedal robot, with the lower part of the robot consisting of two legs, including the thigh, calf, and foot.
## 3 Conventional Frequency Control Support Techniques
### Technique I Employing the Fixed Gain  
Figure 6 illustrates a typical frequency regulation technique of a DFIG system that applies a droop gain. The RSC controller is indicated on the right side. In [23], the sum of the maximum power point tracking power and the output power of the frequency deviation loop (\(\Delta P_{a}\)) results in the reference power (\(P_{\textit{ref}}\)) of the frequency control loop, as seen below:  
\[P_{\textit{ref}}=P_{MPPT}+\Delta P_{a} \tag{7}\]  
When a frequency disturbance occurs, \(\Delta P_{a}\) sets a positive value and it leads to increasing \(P_{\textit{ref}}\). \(\Delta P_{a}\) is defined as follows:  
\[\Delta P_{a}=-\frac{1}{R_{0}}(f_{\textit{sys}}-f_{nom}) \tag{8}\]  
where \(f_{\textit{sys}}\) and \(f_{nom}\) are the current frequency of the system and the nominal frequency, respectively; \(R_{0}\) is the fixed gain, set to 25 in this investigation to attain the operational stability of the DFIG near the minimum rotor speed [35]. After obtaining \(P_{\textit{ref}}\), the rate limiter and power limiter set the order of the RSC controller considering the DFIG performance. In Figure 6, the rate limiter considers the mechanical stress on a drivetrain of a DFIG and is set to 0.45 pu/s, and the limiter is set to the minimum value of the power limit and torque limit of a DFIG with the rotor speed. This typical strategy inevitably adopts a small gain for this reason. Thus, Technique I provides a limited contribution to the frequency control support due to the relatively small value assigned to the frequency deviation loop gain despite the sufficient kinetic energy stored up in the rotating masses. Technique II modifies the frequency deviation loop gain with kinetic energy. The next subsection explains the characteristics of Technique II.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 2 A Planar Antenna for LSA Bands
### Radiation Performance  
The antenna gain variation within the reflection coefficient bandwidth is 2.3 dB and the minimum and maximum antenna gains are 1.3 and 3.6 dBi, respectively. The minimum and maximum efficiencies are 66% and 89%, respectively. The efficiency and gain variation within the reflection coefficient bandwidth are shown in Figure 4.  
Figure 3: Surface current distributions of the planar antenna (Ant#1) operating in the LSA bands at (**a**) 1.8 and (**b**) 2.6 GHz.  
Figure 5 shows the normalized \(E_{\phi}\) and \(E_{\theta}\) radiation patterns of Ant#1 in the three principal planes at 1.8 and 2.6 GHz. Here, the patterns are normalized with respect to the maximum of the corresponding principal plane. From the presented results, no nulls can be observed at 1.8 GHz but a null can be observed at 2.6 GHz in the x-y plane radiation patterns. The \(E_{\phi}\) radiation in the y-z plane at 1.8 GHz is more uniform (i.e., somewhat omnidirectional) compared to the \(E_{\phi}\) radiation in the same plane at 2.6 GHz. An almost directed \(E_{\phi}\) radiation pattern can be observed in the x-z plane at 2.6 GHz.
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 3 Evolution of Industrial Maintenance  
As explained in the previous section, the concept of Industry 4.0 leads to a data-driven connected factory. Based on this concept, the dominant trend in maintenance is to move away from costly reactive maintenance to preventive and predictive maintenance, called also smart maintenance. Over the course of industrial revolutions, as shown in Figure 3, maintenance strategies have undergone a gradual evolution and it is currently a continuous process [23]. Indeed, the philosophy of reactive or corrective maintenance is more common in manufacturing plants that are understaffed and unable to retain skilled maintenance personnel. Industries have struggled for years to abandon this maintenance philosophy because it is costly and often results in significant unplanned downtime and increased costs [24]. Preventive maintenance is a set of actions aimed at preventing failures, reducing the risk of failure and the number and duration of unscheduled shutdowns, which implies an extension of the equipment's life and consists of intervening in a system before a failure occurs [25].
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 1 Introduction
In Section 6, results using the ECADLL are shown, demonstrating the spoofing detection capabilities of the algorithm and comparing them to another signal processing anti-spoofing technique. Finally, in Section 7, the conclusions of the work are drawn.
# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
## 5 Simulation Results and Analysis
### Data Preparation  
In this work, we collected 800 aerial images of electric towers. These images are acquired from Zhaotong power supply bureau of Yunnan province. We use these images for annotating bird's nest data for training and testing purposes. We use 5-fold cross-validation to evaluate the stability and detection performance of the models. In the 5-fold cross-validation experiment, the original dataset is randomly divided into five non-coincident sub-datasets, and then the models are trained and validated five times. Each time, four sub-datasets are selected as the training sets and one sub-dataset as the validation set. In the five training and validation processes, the sub-dataset used to validate the model is different each time. Finally, the average value of the five results is selected as the index representing the performance of the model. In addition, the training dataset images are subject to horizontal flip, vertical flip, and random rotation [24]. The images are randomly stretched within a certain range. Gaussian blur is applied, and salt-and-pepper noise are added to simulate the real-world environment. The uniform resource location (URL) of the dataset is shown in Supplementary Materials.  
After the application of aforementioned preprocessing, we obtain 3000 images for training bird's nest. Figure 7 presents a schematic diagram of annotated pictures of a training set after data enhancement. Similarly, Figure 7a is an original image, and the rest of the images are partially enlarged from the original image using data processing.
## 3 Results and Discussion
### Effects of Twist Coefficient on Air Permeability of Functional Antibacterialarial Knitted Fabrics  
The air permeability of knitted fabrics serves an important index for the comfort that the users feel. Figure 5 shows the air permeability of functional antibacterial knitted fabrics as related to the twist coefficient, which is 188-265 cm\({}^{3}\)/cm\({}^{2}\)/s for the 2 Zn groups and 141-186 cm\({}^{3}\)/cm\({}^{2}\)/s for the 3 Zn groups. Regardless of whether it is 2 Zn groups or 3 Zn groups, an increase in the twist coefficient of the constituent zinc ion twisted yarns has a positive influence on the air permeability of the resulting knitted fabrics. This result is ascribed to the enhanced cohesion that strengthens the entanglement level among yarns firmly, while improving the air permeability concurrently. In particular, the optimal air permeability is 265 cm\({}^{3}\)/cm\({}^{2}\)/s for 2 Zn-3C-K and 186 cm\({}^{3}\)/cm\({}^{2}\)/s for 3 Zn-3C-K. Because of a greater twist count, knitted fabrics have a higher density that decreases the porosity and then reduces the air permeability [31; 32].  
Figure 4: Water vapor transmission rate of functional antibacterial knitted fabrics as related to twist coefficient. Note: data shown as mean standard deviation (SD) (\(n=3\)). \(\mathbf{\ast\ast}\)\(p<0.01\).  
Figure 5: Air permeability of functional antibacterial knitted fabrics as related to the twist coefficient. Note: data shown as mean standard deviation (SD) (\(n=12\)). \(\mathbf{\ast\ast}\)\(p<0.01\).  
Effects of Twist Coefficient on Antimicrobial Efficacy of Functional Antibacterialar Knitted Fabrics Based on UV-Vis Analysis  
Figure 6 shows that the OD ultraviolet/visible spectrophotometer (UV-Vis) value is 1.62 for 2 Zn-0C-K but is 0.56 for 2 Zn-5C-K, and likewise, the OD value is 1.5 and 0.45 for 3 Zn-0C-K and 3 Zn-5C-K, respectively. To sum up, regardless of whether the knitted fabrics are composed of two or three plies of twisted zinc ion yarns, the greater the twist coefficient, the lower the OD value, which means that the knitted fabrics demonstrate better antimicrobial efficacy. On the other hand, when bacteria grow in a nutrient solution, they consume glucose as energy, and their metabolites generate acids. Hence, a decrease in glucose quantity as well as a raise in the metabolic acid could be used as the reference for corresponding bacterial growth. This method is, however, vicarious and unable to obtain a total viable count, but only serves an antibacterial index.  
The OD value after washing of 2 Zn-0C-K, 2 Zn-5C-K, and 3 Zn-0C-K is 1.68, 0.58, and 1.58, respectively (Figure 6). It is worth noting that the OD value of the 2 Zn-5C-K sample before washing is lower than that of the fabric after washing. However, for the 3 Zn-0C-K sample, the OD value has a slight decrease after five cycles of washing. In fact, there was no large difference for the antimicrobial efficacy on the above samples before and after five cycles of washing in the statistical analysis method. The samples before and after washing showed outstanding antimicrobial efficacy.
## 2 Materials and Methods
### Fatigue Crack Growth Experiments  
Fatigue crack growth (FCG) experiments were performed in a servo hydraulic testing machine (Instron Model 1343). In this test set-up the specimens were inductively heated. Before testing, the CT samples were pre-cracked to a starter crack length to width ratio (a/W) of about 0.4 by cyclic loading at ambient temperature in an Instron Model 1603 resonance tester. The direct current potential drop (PD) technique was used to monitor crack length during the FCG experiments. Furthermore, FCG experiments were conducted through applying a sinusoidal waveform at constant load ratio (R = 0.1) until the termination criterion (a/W = 0.7) was reached. The cyclic stress intensity factor \(\Delta\)K was determined according to ASTM E647 [21], and the evaluation of the cyclic crack propagation rate was accomplished by a seven-point polynomial method according to ASTM E647 [21]. The performed experiments are summarized in Table 5.
# Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach
## 1 Introduction
For instance, prompt-based methods leveraging pre-trained language models may be confined to surface-level semantics, lacking the ability to fully reason about the implicit attitudes underlying the text, which constrains model performance in complex contexts. These challenges underscore the need for ZSSD to incorporate stronger semantic understanding and reasoning mechanisms to address the dual shortcomings of data scarcity and contextual complexity [11; 13]. To address these challenges, this work proposes a zero-shot stance detection method based on multi-agent debate (ZSMD). Our approach leverages the reasoning capabilities of multi-agent debate to enhance the model's inference and judgment in complex contexts. By simulating viewpoint conflicts and argumentation processes among multiple agents, multi-agent debate effectively facilitates stance identification and classification. Within this framework, each agent represents a potential perspective or stance, and through their interactions and debates, they not only aid in distinguishing various stances but also uncover subtle, latent information embedded in the text, thereby improving detection accuracy. Compared to traditional stance detection methods, multi-agent debate offers a dynamic reasoning mechanism, enabling the model to better adapt to unknown targets and unstructured text. Furthermore, existing stance detection methods often overlook the supplementation of target-related background knowledge, which is critical for accurately determiningstance [14]. For instance, when assessing an individual's stance toward a specific policy, the absence of background information about the policy's content may prevent the model from correctly interpreting references or implicit meanings within the text. To address this, we propose a multi-task learning strategy that enhances the model's semantic understanding by integrating debate data and target-relevant background knowledge. During the multi-agent debate process, we dynamically incorporate external knowledge--such as event contexts and entity relationships--into the model, enabling it to reference more comprehensive contextual information during reasoning. This approach not only compensates for the shortcomings of traditional ZSSD in terms of data scarcity and contextual inadequacy but also improves the model's reasoning capabilities and cross-task robustness through multi-task training. In summary, the primary contributions of this work are as follows:  
* We propose a stance detection method based on multi-agent debate, integrating stance analysis and reasoning processes, which significantly improves model performance in complex contexts. * By incorporating background knowledge and debate data, we enhance the model's semantic understanding, addressing performance bottlenecks in zero-shot stance detection. * Experimental validation on two public datasets demonstrates that the method proposed in this paper significantly outperforms existing stance detection approaches, achieving state-of-the-art (SOTA) results.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 3 Fire Detection Approach
### IoT Devices and Hardware Requirements  
The device we used to perform our test was a Raspberry Pi 4 due to the size, portability, and accessibility to all the pins that are available for the sensors required for input. Furthermore, Raspberry has wire and wireless extensive variety for network accessibility. Accessing the network gave us a huge advantage by quickly sending data to the cloud for our main computer to identify whether it is a real fire or not. However, before the fire was detected, our MQ-2 flammable gas and smoke sensor was used to detect gas. The MQ-2 flammable gas and smoke sensor was connected to the Raspberry Pi, which was also connected to the breadboard. We connected the Raspberry Pi 4 pins to the breadboard and routed to the MQ-2 flammable gas and smoke sensor. Lastly, we connected the camera to detect the fire visually, which was easily accomplished with a USB cable. This unit was placed in a casing to protect it from being weathered. This unit would be installed on a fabricated tree. In Korea, you can see these trees when climbing a mountain, etc. These are cellphone towers hidden in the mountains. We can use these fabricated cellphone tree towers to install all of our electronics and power them as well. Once this unit detects the smoke via the MQ-2 flammable gas and smoke sensor, it then uses the camera to take multiple shots. These data are thensent off to the cloud for the main computer to analyze using AI.  
Figure 1: Overall flow chart of the system.  
Working with devices and sensors and testing out beta products, Raspberry Pi 4 or Arduino are used due to the easy accessibility to the pins and sensors. However, this can be bypassed by using a desktop Windows 10 Pro and its GPU to render video quality, and using the Raspberry Pi 4 or Arduino just for the smoke sensors.  
Listed below are the hardware requirements:  
* Raspberry Pi 4 or Arduino;
* Raspberry Pi 4 power cable;
* Raspberry Pi 4 Internet cable;
* Solid-state drive (SSD) or hard drive;
* Internet access;
* Desktop Windows 10 Pro (optional/recommended);
* MQ-2 flammable gas & smoke sensor;
* Pin wires;
* Breadboard;
* Camera.
# Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network
## 4 Experimental Results
### Experimental Setup
#### 4.1.4 Model  
Suppose we input a picture of size \(C\times H\times W\); \(C\) is the channel size; and \(H\times W\) is the channel information size. In this article, the number of attention cores \(k\) was 4, and we used RCR and CGR to extract useful spatial information. Since the number of cores \(k\) was 4, we extracted the useful information of the four channels. Then, the adaptive average pool will compress the spatial information of these four channels with the \(5\times 5\) size of the compressed output. The output passes through two fully connected layers (there is a ReLU activation function between the two layers), where the output of the first fully connected layer is 10, and the output of the second fully connected layer is the number of attention cores \(k\). Then, the input to the softmax function with temperature, where the temperature \(\tau\) was set to 30, the four attention weights were output as \(\alpha_{i}(x)\), and then after the \(\sum\limits_{i=0}^{k-1}(\alpha_{i}(x)\times\text{kernel}_{i})\) weighting operation to obtain the required weight of the perceptron during convolution. After that, the output of the perceptron is processed by the Batch-normal and ReLU activation function to obtain our improved convolution output.  
Since the mid-channel in MobilenetV2 is very large, when using the CGR method to reduce the channel, if a channel is randomly selected from each group, some useful information will be lost and the experimental results will be affected. Therefore, the channel with the largest tensor is extracted from each group, so that the extracted information is more useful and the accuracy rate is higher. We marked this method as CGR-MAX in the table.
## 2 Mitochondrial (mtDNA) and Nuclear DNA Analysis  
Genetic markers based on mtDNA analysis are simple to use and inexpensive. Mitochondria contain most of the genes that code for cellular energy production and electron transfer (the NADH dehydrogenase subunit, the cytochrome oxidase subunit, ATPases 6 and 8, cytochrome b, rRNA, RNA, 12S and 16S) [19; 20; 21]. Compared to nuclear DNA, mtDNA markers show a higher rate of nucleotide substitution (by 5 to 10 times), no (or little) recombination, maternal inheritance and neutral (or nearly neutral) evolution [7]. The choice of the sequence used for genetic analysis depends on the phylogenetic hypothesis to be tested--the D-loop is used in the case of rapid evolution; cytochrome b is used in the case of moderate evolution; and 12S or 16S rRNA and cytochrome oxidase I are used in the case of slow evolution. mtDNA is used to study the direction of hybridization and the incidence of introgression [22; 23]. Note that in the case of hybridization, the use of mitochondrial markers alone is not sufficient and may generate erroneous inferences. For this reason, other analyses (e.g., based on nuclear genes) are also considered in phylogenetic and phylogeographic studies [24]. Recently, [PERSON] et al. [25] suggested a Bayesian coalescence-based method for mitochondrial DNA analysis.  
Traditionally, the nuclear genes most often used in phylogenetic analyses are histone (H3) and ribosomal (18S and 28S) genes. At the population level, introns or noncoding regions are usually used, as their evolutionary rate is higher, and they are not affected by selection [26]. To resolve the simplest phylogeny cases, ITS (internal transcribed spacer) regions offer sufficient variability, although in some cases, paralogy effects and recombination events can complicate analyses [18].  
Electrophoretic analyses of proteins and the LDH-C1 allozyme (lactate dehydrogenase-C1 locus) were used in early studies of brown trout [12]. Genetic differences between the AT and ME evolutionary lineages were confirmed using mtDNA and restriction fragment length polymorphism (RFLP) analyses. This method facilitated the distinction between the AT and ME lineages by amplifying the mitochondrial 16S rDNA and control region (D-loop) genes using the restriction enzyme Rsal and amplifying the nuclear LDH-C1* gene using the restriction enzyme BsII [27]. The LDH-C1* 90 allele is specific to the AT line. Several authors have used mtDNA markers for genetic analysis of brown trout [14; 28; 29]. The control region sequence is the marker most widely used to identify main evolutionary lineages [14]. This gene is characterized by the absence of introns and recombination. _S. marmoratus_ is characterized by the mtDNA MA and LDH-C1*(120) alleles [30; 31]. [PERSON] et al. [32], using the complete mtDNA control region in _S. trutta_, identified the MA, AT and DA lineages. In a recent study, ancient DNA was used for phylogeographic analysis of the _S. trutta_ complex in Italy [31], highlighting that the AD and ME evolutionary lines are also present in museum-preserved samples.