esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Thermographic Techniques for Skin Cancer Diagnosis
### Dynamic Infrared Thermography  
As mentioned previously, dynamic IR thermography involves a cold stimulus to enhance the thermal contrast between healthy and unhealthy skin tissues. However, in the last few years, several authors have suggested other dynamic, well-known techniques in the field of non-destructive testing, such as lock-in thermography and thermal wave imaging. Compared to classical dynamic thermography, these techniques applied for skin cancer diagnosis promise to precisely localize the lesion margins or identify the different development stages of the tumor.
# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 6 Conclusions  
The models were trained on a dataset consisting of four classes: pneumonia, COVID-19, lung opacity, and normal in a study on the classification of CXR images for pneumonia and COVID-19 using convolutional neural networks with three different architectures, namely ResNet-50, ResNet-101, and ResNet-152. The performance of each architecture was measured by recall, precision, and F1-scores. ResNet-50 achieved the best precision with a value of 92% in the normal and lung opacity classes, the highest recall value of 96% in the pneumonia class, and the F1-score value of 91%. The ResNet-101 architecture produced the highest precision value in the COVID-19 class of 95%, the highest recall value in the pneumonia class of 96%, and a f1-score of 93%. The ResNet-152 architecture produced the highest precision value in the COVID-19 class of 97%, the highest recall value in the pneumonia class of 97%, and a F1-score of 94%. Because ResNet-152 outperformed all other ResNet models in classifying CXR images in pneumonia and the COVID-19 disease, the ResNet-152 is the best model for classifying CXR images in pneumonia and the COVID-19 disease.  
To avoid data imbalance, the research data for each class should be improved in the future, particularly for classes with fewer data points than other classes. A larger dataset is expected to be used in future research to identify other lung diseases in a more specific manner.  
Future research should result in a more adaptive model that can continuously update during the training process by incorporating new data. Other methods can be combined with the ResNet architecture to produce improved methods and architectures in the future. The study's findings, which are currently in the form of a model, are suggested to be used in an application to help medical professionals effectively diagnose lung diseases under the supervision of radiology experts.  
[PERSON], [PERSON], [PERSON], I.N.Y. and M.H.A. understood and designed the research; [PERSON] analyzed the data and drafted the paper. All authors critically read and revised the draft and approved the final paper. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the Research Center for AI and Big Data Universitas Padjadaran and the Directorate for Research and Community Service (DRPM) Ministry of Research, Technology, and Higher Education Indonesia which supports this research under Higher Education Research Grant no. 094/E5/PG.02.00.PT/2022.  
Not applicable.  
Data Availability Statement: Data is available at [34;35].  
The authors declare no conflict of interest.
## 4 Semi-Active Vibration Control of the Thin Plate Using the ERF Embedded ATVA
### Semi-Active Vibration Controller with a Switching-Mode Rule for ATVA  
Because of the nonlinear dynamic properties of the ERF embedded ATVA with response to the vibration at different frequencies, a semi-active vibration control with a switching-mode rule is proposed to determine the optimal electric field for the ATVA. There are two reasons why the switching action is necessary. The first reason is that the location with the maximum displacement may vary for the different vibration mode. Therefore, in Section 4.1, we proposed to use two displacement sensors to better measure the response of the plate for the different vibration mode. The second reason is that the electric field of the ATVA should be adaptively tuned for the different vibration mode (as shown in Figures 16 and 17) to obtain the superior ability of absorbing vibration for the ATVA. Therefore, we proposed a fuzzy controller of the ATVA for attenuating the undesired vibration by using the frequency-dependent control strategy [7].  
Figure 18 shows the proposed switching algorithm of the fuzzy controller according to the frequency response to tune the electric field adaptively and Figure 19 is the real-time embedded code for the NI CRIO 9074. First, we established the type-2 fuzzy controllers for the three vibration modes using the look-up tables as shown in Figures 16 and 17. Second, the displacements of the structure are captured by the CRIO 9074. Third, the vibration frequency is obtained from the FFT of the displacements. Then, the real-time program uses the resulting frequency to distinguish which the vibration mode is and switch the input to the suitable controller to produce the control voltage.  
Figure 17: Main inference structure of the proposed type-2 fuzzy controller of the second and third mode. (**a**) the second mode’s inference structure; (**b**) the third mode’s inference structure.
## 4 Limitations  
In addition to an extensive examination of the validity of Equation (1), another important goal of this study is to evaluate the performance of the SWM. The experiments described in Section 2.2 may also be viewed in the context of validation experiments, although they are restricted to a one-layer silicone model. The effects of different vocal fold shapes and dynamics as well as prephonatory settings of the laryngeal cartilages cannot be analyzed with this experimental model. Furthermore, the model also includes a straight rectangular flow channel as a simplified vocal tract. The effects of supraglottal structures as ventricular folds or characteristic geometrical conditions associated with specific vowels (pharyngeal constrictions and partially obstructed channel exits) were neglected. Previous studies have shown that the presence of the ventricular folds significantly reduces the phonation threshold pressure as long as their positions and the gap in between is optimally selected [35; 44; 45]. Furthermore, the acoustical driving effect of vocal tract resonances was minimized. By considering the subglottal and supraglottal channel as quarter-wave resonators, the first resonance occurs at 451 Hz, which is significantly higher than the fundamental frequency of the vocal fold oscillation [40].  
Beside these limitations and restrictions, the experimental model provides periodic vocal fold motion with corresponding sound production that shows characteristic features of human phonation process. Therefore, it is well applicable for the validation of the SVM model owing to its simplicity, and the ease with which the independent variables are controlled.
# Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
## 2 Methodology
### Original YOLOv8s Network  
YOLOv8 is the YOLO series of object detection models released by Ultralytics, which builds on previous versions by optimizing the structure of the backbone and neck network to maintain the best balance between accuracy and speed for object detection tasks in all areas [45]. In addition, during the design and optimization process of YOLOv8, considering the ease of use and deployment of the model, it is suitable for deployment to the mobile terminal for underwater defect detection. According to the network depth and width, YOLOv8 can be divided into 5 sizes of n, s, m, l, and x [46]. Among these, the n-version network model is the smallest but has the worst detection performance; the x-version offers the best detection performance but has a larger model size, making it less suitable for later export and deployment. Based on the number of images in the dataset, which consists of several thousand images, the s or m versions are more appropriate. Considering that larger models require higher computational resources, this study decided to use YOLOv8s as the baseline network for research. The network structure is shown in Figure 3.  
The structure of YOLOv8s consists of backbone, neck, and head parts. The backbone part uses the CSPDarkNet structure, including Conv, C2f, and Spatial Pyramid Pooling-Fast (SPPF) modules, which are mainly used for feature extraction of images. The neck part is a crucial component that connects the network backbone and the detection head, primarily responsible for feature fusion and processing to improve detection accuracy and efficiency. The neck part upsamples and fuses channel information from the backbone, sending the detailed information to the detection head for loss calculation and result prediction. The head part of the network adopts a decoupled head structure, which assigns  
Figure 3: Architecture of the YOLOv8s network.  
the classification and regression tasks to different branches to improve the accuracy and stability of the prediction.
# Study on Impact-Echo Response of Concrete Column near the Edge
## 2 Background
### Experimental Verification  
This section examines the impact-echo test of the concrete column carried out via a field test to verify the phenomenon of the deviation of the predominant frequency and determines the influence range of the boundary to provide experimental support for the subsequent numerical simulation.  
The boundary region was detected using SPC-MATS and impact-echo instruments, as shown in Figure 1, respectively. The test was based on the theory of stress wave propagation. During the test, the excitation signal was generated at the excitation point of the steel ball with a diameter of 17 mm. The sensor received the signal of the surface particle vibration, amplified it in the signal amplifier, and transmitted it to the instrument host. The analog signal was converted into a digital signal. The received signal was processed by analytical software to obtain the predominant frequency of the impact signal at different distances from the boundary. During the test, the acquisition time of the instrument was 8.192 ms, the sampling interval was 2 \(\upmu\)s, the number of acquisitions was 4096, and the acquisition frequency was set to 500 kHz. The test instruments used the data acquisition system to receive the voltage signal generated by the vibration of the small steel ball on the surface of the component to be tested by the sensor. Therefore, the sensor should be in complete and stable contact with the surface of the component to be tested.  
In this paper, in order to facilitate the study of the frequency offset phenomenon of concrete columns under boundary conditions, the band tape was used to detect the line at the edge. A survey line was arranged at the edge every 1 cm from the middle of the column to the edge. The direction of the survey line was perpendicular to the ground, and each survey line was numbered in turn using numbers 1, 2, and so on, as shown in Figure 2. The concrete density was 2400 kg/m\({}^{3}\), the elastic modulus was 3.25 \(\times\) 10\({}^{10}\) Pa, and the Poisson's ratio was 0.2 (The concrete strength grade is C40). Since the test site was at a construction site, both ends of the column were fixed between the floor and the foundation.
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 4 Multi-Target Detection and Localization  
In this section, the ATML scheme that detects the signals from MSNs and locates their positions in a SIWSN is presented. The MT-SIMO signal transmission/detection system is first designed; then, the propagation parameter (especially the 2D AOA) estimation based on the spreading-sequence response using the MLE is proposed, and finally the relative 3D position of an MSN is deduced.
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 4 Integrating Electric Vehicles into Smart Cities: Challenges and Opportunities
### Interoperability Challenges  
The integration of electric vehicles (EVs) into smart cities requires the development of intelligent transportation systems (ITS) that enable seamless communication between EVs, charging infrastructure, and the city's transportation network. However, several challenges arise in the implementation of ITS solutions for EVs in smart cities for interoperability. One of the most significant challenges is the lack of standardization in the communication protocols used by EVs and charging infrastructure, which impedes the interoperability of different systems. To overcome this challenge, stakeholders must develop and adopt common standards and protocols for data exchange, communication, and interoperability.  
Another challenge is the need for a robust and reliable infrastructure to support the charging needs of EVs. To address this challenge, smart cities must invest in the deployment of charging infrastructure that is easily accessible, reliable, and efficient. Additionally, the charging infrastructure must be integrated with the smart grid to enable the optimal use of renewable energy sources and avoid overloading the grid. To achieve this, stakeholders must collaborate to develop a comprehensive plan for the deployment of charging infrastructure that takes into account the needs of different types of EVs and the expected growth in demand.  
The integration of EVs into smart cities also requires the development of intelligent transportation systems that can optimize traffic flow and reduce congestion. However, this requires the collection and analysis of a large number of data from various sensors and sources, such as traffic cameras, GPS devices, and weather sensors. To overcome this challenge, stakeholders must develop data analytics tools and techniques that can process and analyze a large number of data in real-time to provide actionable insights for traffic management and optimization.  
Finally, the integration of EVs into smart cities requires the development of user-friendly interfaces that can facilitate seamless interaction between EVs and the city's transportation network. This requires the development of intuitive and easy-to-use interfaces that can provide drivers with real-time information on traffic conditions, available charging stations, and optimal routes. To achieve this, stakeholders must collaborate to develop user-centered design principles and best practices for the development of interfaces for EVs in smart cities.
## 3 Results and Discussion  
### Kinetic Study  
The maximum time required to reach sorption equilibrium was examined by shaking pharmaceutical solutions with three initial pharmaceutical concentrations (5, 15, and 25 mg/L) for 11 different time periods. The TiO\({}_{2}\) suspension reached equilibrium with both pharmaceuticals after 24 h. IMT and CRZ were not significantly sorbed on immobilized TiO\({}_{2}\) after 4 h, whereas CRZ was required to remain in contact with TiO\({}_{2}\)/CNT-GF for 24 h. Review of the literature revealed similar results for multi-walled CNTs as sorbents in the case of sulfapyridine and sulfamethoxazole, which also reached equilibrium after 4 h, while ciprofloxacin moved even longer than CRZ (72 h) [21; 37]. For all three types of sorbents, sorption in the initial phase is a dominant and fast process, while sorption after reaching equilibrium was a slow process over time. The initial concentrations did not affect the contact time between pharmaceutical and TiO\({}_{2}\).  
Tables 2 and 3 show the parameters from the kinetic study in which the pseudo-first- and pseudo-second-order models were tested for IMT and CRZ, respectively. For the pseudo-first-order model, the linear correlation coefficient \(R^{2}\) does not show good agreement, while the \(R^{2}\) for pseudo-second-order kinetic model was very high (\(R^{2}>0.99\)). The theoretical values \(q_{e,cal}\) were closer to the experimental values \(q_{e,exp}\) obtained at all concentrations used. Thus, the pseudo-second-order model is the better model for describing the sorption kinetics of the two pharmaceuticals on all three types of TiO\({}_{2}\) photocatalyst.  
The intraparticle diffusion constants obtained and values related to the boundary layer thickness indicate that the sorption process occurs mainly in three stages. After contact with an aqueous solution of pharmaceutical and the solid phase, the compound migrates into the sorbent boundary layer. The second stage involves intraparticle diffusion between the porous material and the IMT/CRZ. Sorption at the active sites of the sorbents and equilibrium state are usually reached in the third stage, which is confirmed by the smallest slope, \(k_{\rm p3}\) (Tables 4 and 5) [33; 38].  
This step is very fast and does not control the rate of the whole sorption process. From the obtained results, it can be concluded that the sorption of IMT and CRZ on TiO\({}_{2}\) samples is a complex process controlled mainly by external and intraparticle diffusion. The \(C\) values increased from the first to the third stage, with values greater than 0 confirming the first step of surface sorption as the rate-limiting step [39].
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 5 Repair of Point Cloud
### Point Cloud Repairing of FPFH + ICP Algorithm  
The fast point feature histogram (FPFH) is an improved algorithm based on point feature histogram (PFH) [31] which refines the original algorithm and improves its computational efficiency. The process of the algorithm is to provide a fixed neighborhood interval for a certain geometric feature in the point cloud, calculate the geometric eigenvalues of the point cloud through the neighborhood interval, establish a histogram of the eigenvalues for statistics, and then obtain a statistical feature. After the corresponding point pairs of the target point cloud and the source point cloud are obtained according to the FPFH algorithm, the optimal transformation matrix is obtained through iterative calculation so that the pose of the target point cloud and the source point cloud is relatively accurate. The ICP algorithm is used for fine registration and the source point cloud data is repaired. The RGB-D roadway point cloud and the 3D laser scanning point cloud were repaired by FPFH + ICP algorithm; the RMSE value was 19.0657 mm and the repair results are shown in Figure 14a,b.
# Design of a Planar Cable-Driven Parallel Robot for Non-Contact Tasks
## 2 Design Strategies  
In most cases, cables are driven by actuators that are placed on a fixed base. Sometimes, if deployability is a requirement, they can be attached to mobile vehicles [24] or placed on the mobile platform [29]. A guidance system is used to convey cables from the actuators to the mobile platform. Many authors analyzed the design process of \(CDPR\)s and identified several methods for optimizing their structure and dimensions [1,30]. For a given criterion (i.e., workspace dimension, accuracy, stiffness, orientation capability), optimization procedures help determine the geometry of the \(CDPR\), including the number of cables, position of cable anchor points, and platform and frame shapes [2,15,31]. Once the architecture is known, the hardware design requires developing three distinct systems: the actuation unit, the guidance system, and the mobile platform. These systems can be equipped with suitable sensors, to monitor the robot performance and obtain feedback on relevant control variables. The two most notable examples are cable lengths, which are usually estimated by using angular sensors mounted on the actuation unit motors, and cable tensions, whose measurement involves integrating force sensors in the actuation unit, guidance system, or \(EE\)[1].  
This section shows how specific design techniques can help keep the robot model and control as simple as possible without introducing geometrical approximations. Usually, a simplified kinematic model, called standard in the following, is adopted. This model considers cables inextensible and massless and, thus, treated as line segments between two points, one on the mobile platform and the other on the frame (distal and proximal anchor points, respectively). The former is usually assumed to be fixed with respect to the moving platform, whereas the latter is fixed with respect to the robot frame. These assumptions neglect not only all effects caused by the elastic nature of cables, which may be negligible in some small-to-medium-scale applications, but also the kinematics of the guidance system. Geometrical-model errors can be limited by accounting for the pulley geometry [4,32], but the model and control complexity is usually increased. As an alternative, if the standard kinematic model is to be used without inherent geometrical errors, special design techniques of the actuation units and guidance systems can be used, as discussed in the following.
###### Abstract  
Aiming to address the currently low accuracy of domestic industrial defect detection, this paper proposes a Two-Stage Industrial Defect Detection Framework based on Improved-YOLOv5 and Optimized-Inception-ResnetV2, which completes positioning and classification tasks through two specific models. In order to make the first-stage recognition more effective at locating insignificant small defects with high similarity on the steel surface, we improve YOLOv5 from the backbone network, the feature scales of the feature fusion layer, and the multiscale detection layer. In order to enable second-stage recognition to better extract defect features and achieve accurate classification, we embed the convolutional block attention module (CBAM) attention mechanism module into the Inception-ResnetV2 model, then optimize the network architecture and loss function of the accurate model. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset, the public dataset NEU-DET, and the optimized dataset Enriched-NEU-DET, we conducted multiple sets of comparative experiments on the Improved-YOLOv5 and Inception-ResnetV2. The testing results show that the improvement is obvious. In order to verify the superiority and adaptability of the two-stage framework, we first test based on the Enriched-NEU-DET dataset, and further use AUBO-i5 robot, Intel RealSense D435 camera, and other industrial steel equipment to build actual industrial scenes. In experiments, a two-stage framework achieves the best performance of 83.3% mean average precision (mAP), evaluated on the Enriched-NEU-DET dataset, and 91.0% on our built industrial defect environment.  
two-stage; multiscale detection layer; Enriched-NEU-DET; AUBO-i5 robot Article  
A Two-Stage Industrial Defect Detection Framework Based on Improved-YOLOv5 and Optimized-Inception-ResnetV2 Models  
Zhuangdue to the lack of obvious rules for the distribution of defects on the steel image, it is difficult to extract the features, which leads to difficulty in using the recognition algorithm and low recognition accuracy. Deep learning detection is an algorithm for the classification of steel surface defect images based on a convolutional neural network [4], which can use the image as the direct input of the network to automatically extract features. Compared with traditional methods, it has higher accuracy and faster speed, as well as stronger adaptability. In actual production scenarios, the abovementioned defect detection procedures are usually deployed to detect steel products and classify them according to their performance.