esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Welding defects must be inspected to verify that the welds meet the requirements of ship welded joints in order for them to support their own weight and the cargo weight during the lifetime of ships and perform their role without breakage by having resistance to stress, corrosion, and fatigue cracking. Destructive and nondestructive inspection methods have been applied to welding joints. Destructive inspections have high reliability; however, performing them directly on welding joints is unreasonable. Further, performing destructive inspections on all joints is difficult. Nondestructive inspections are widely applied during the production process because they can determine pass or fail based on quality standards, by measuring and detecting physical properties that change because of defects without damaging the weld zone. To perform nondestructive inspections, the completed weldment must be transported to the nondestructive inspection station or the inspection equipment must be installed on site; however, this process is expensive. Therefore, methods to instantly inspect welding defects on site, while taking into consideration the locations that are difficult for field workers to access, are necessary [1].  
Thus, the automation of welding defect detection is required. Recently, machine vision [2] technology has been developed because the use of vision-based defect detectorsusing cameras has become universalized. Accordingly, artificial intelligence has been employed for defect detection using image processing, and at several processing sites of companies, continuous attempts are being made to combine machine vision and deep learning [3] to detect defects more accurately [4]. The data preprocessing step performed before training a deep learning model involves changing the existing data to data suitable for the learning algorithm. Such a preprocessing step is also applied to new data to be predicted after the model is created, and the more the deep learning algorithm goes through the preprocessing step, the better the learning performance [5]. Therefore, in the study of creating a welding defect detection algorithm using deep learning, the preprocessing of welding defects in radiographic images before algorithm creation has a great effect on improving the accuracy of the welding defect detection algorithm [6; 7]. However, the data preprocessing step has not been consistent, and a technique optimized for welding defects has not been proposed. In general, Contrast Limited Adaptive Histogram Equalization (CLAHE) and image denoising are used in the preprocessing step for generating a radiographic detection algorithm [8; 9].  
This study aimed to express the welding defect part more conspicuously than in the raw image. The preprocessing algorithm using CLAHE, image denoising, thresholding, and concatenation was applied to analyze the defect characteristics according to the purpose and reflect the defect characteristics. Then, the mean average precision (mAP) was checked by comparing the typical preprocessing, the non-preprocessing, and the preprocessing algorithm we proposed.
###### Abstract  
Researchers are interested in unmanned aerial vehicles (UAVs) because they have many uses in current 5G and future 6G networks and are safer than human-operated vehicles. Terahertz (THz)-band communications are a possible option in 6G for fast communication. THz wireless communication between UAVs is taken into consideration in this work. Different fading channels, which are significant influencing factors in THz communication channel modeling, are used to analyze the performance of UAV communications in THz networks. Consideration must be made to the structure, wireless channel parameters, and transmission characteristics when evaluating the performance of wireless technology. Nakagami-m, Rician, Weibull, and Rayleigh fading channels are all taken into consideration, along with log-normal fading. Moreover, an optimization algorithm for the THz channel is presented, which is meant to minimize transmission power by optimizing the trajectory of uplink and downlink transmissions between the UAV and users. The equations of the UAV locations and the transmission power optimization of each user are derived. Analytical formulations regarding capacity, outage probability, and bit error rate (BER) are generated when performance-influencing factors are taken into account. The analytical analysis is supported by the numerical results that are provided.  
BER; channel fading; optimization; outage probability; UAV; THz Article
# The Heat Transfer Analysis of an Acting-type Heat Retention Panel used in a Hot Rolling Process
## 2 Mathematical Analysis
### Numerical Methods and Grid Independence  
In this study, the commercial software ANSYS-Fluent is adopted to solve the governing equations. The finite volume method (FVM) incorporates a second order upwind scheme and a first order implicit scheme for transient formulation. From CSC research, the convective heat transfer effect of air inside the panel is usually neglected, and the heat transfer is dominated by the radiation. Thus, a surface to surface (S2S) radiation model in fluent is adopted to solve the heat transfer problem. Figure 5 shows the computational grids of the three-dimensional traditional passive heat retention panel model, which is composed of 3,873,445 cells, and Figure 6 shows the computational grids of the three-dimensional acting-type heat retention panel model, which is composed of 3,445,396 cells.  
Figure 4: Boundary conditions of the numerical models.  
_Appl. Sci._**2019**, 9, 189  
Figure 5: Computational grid systems of the traditional passive heat retention panel.  
As shown in Figure 7, for the three-dimensional traditional passive heat retention panel model, four different grid numbers, 3,070,833 cells, 3,435,267 cells, 3,873,445 cells, and 4,242,534 cells, were tested for the time step of 0.5 s. The deviations in the transfer bar head for the four grid numbers were 1.22%, 1.04%, 0.91%, and 0.89%, respectively. Moreover, the deviations in the transfer bar tail for the four grid numbers were 1.31%, 1.13%, 1.06%, and 0.94%, respectively. The predicted results for the third grid and the fourth grid were almost the same. Thus, the third grid was adopted. It could also satisfy the requirements related to grid independence, computational accuracy, and  
Figure 6: Computational grid systems of the acting-type heat retention panel.  
computational time needed. The discretized system was solved iteratively until it satisfied the residual convergence criterion as follow:  
\[R=\Big{|}\Big{(}Q_{c}^{n+1}-Q_{c}^{n}\Big{)}+\Big{(}Q_{d}^{n+1}-Q_{d}^{n}\Big{)}+ \Big{(}Q_{s}^{n+1}-Q_{s}^{n}\Big{)}\Big{|}, \tag{12}\]  
where \(Q_{c}\) is the value of convection term in difference equation, \(Q_{d}\) is the value of diffusion term in difference equation, and \(Q_{s}\) is the value of source term in difference equation. In this study, the criterion for numerical convergence judgment is to make the sum of the residual values of all grids satisfy the maximum relative error less than \(10^{-3}\) and \(10^{-6}\), respectively.  
\[\begin{array}{l}\sum\big{|}R_{\varphi}\big{|}<1\times 10^{-3},\ \varphi=u,v,w,P,\\ \sum\big{|}R_{\varphi}\big{|}<1\times 10^{-6},\ \varphi=T,\end{array} \tag{13}\]  
The simulations were performed as a parallel calculation with sixteen core central processing units for both the traditional passive heat retention panel and the acting-type heat retention panel models. The computer calculation time was around 6 h and 7 h for the traditional passive and the acting-type models, respectively.
# Diverse Feature-Level Guidance Adjustments for Unsupervised Domain Adapative Object Detection
## 4 Experiments
### Datasets  
Our experiments set up three main adaptative scenarios, including _weather adaptation_, _cross-camera adaptation_, and _synthetic-to-real adaptation_, corresponding to four benchmark datasets: Cityscapes [39], Foggy Cityscapes [38], KITTI [40], and Sim10k [41].  
**Weather Adaptation.** The Cityscapes [39] and Foggy Cityscapes [38] datasets were employed to study domain shift caused by weather conditions. The Cityscapes dataset is a dataset of urban scenes captured under dry weather conditions. The Foggy Cityscapes dataset was created by adding artificial fog to Cityscapes. For our detection tasks, we focused on eight specific categories: _car, train, motorcycle, bus, bicycle, cycle, person_, and _truck_, to evaluate the accuracy of the proposed algorithm. A total of 2965 images from the Cityscapes and Foggy Cityscapes datasets were used as the source and target domain input data, respectively. An additional 492 images were selected from the Foggy Cityscapes dataset to create the validation set.  
**Cross-camera Adaptation.** The Cityscapes [39] and KITTI [40] datasets were utilized to evaluate the model's cross-camera adaptation capability. These two datasets have different camera configurations. The KITTI dataset, which includes urban, rural, and highway images in real scenarios, comprises 7481 images. The _car_ category was primarily analyzedto assess our method's superiority. The training set input comprised 2965 Cityscapes images and all KITTI images. A total of 500 images were selected from different training domain datasets as the validation set.  
**Synthetic-to-real Adaptation.** To assess the adaptation ability from real to synthetic images, the transition from Cityscapes [39] to Sim10k [41] was utilized for evaluation. Similarly, _car_ category bounding boxes were introduced as the primary objects in our experiments. A total of 2965 images from the Cityscapes dataset and all images from the Sim10k dataset were used as the target and source domain input, respectively. To create the validation set, 500 images were selected from different training sets of the Cityscapes dataset.
## 3 Model Construction
### Model Parameters and Comparison Scheme  
In this paper, we refer to past representative deep learning methods and training parameters studies, and B stands for Batch Size, which indicates the number of samples input into a neural network at one time for training. A model's size affects the degree of optimization and its speed, which also directly affect the memory usage. The number of epochs is represented by E, indicating that all data inputs in a network perform forward propagation and back propagation calculation processes. Drawing on previous studies [24], an epoch takes 50 s, with excellent results when using a CNN with an LSTM or a GRU hybrid model. Since different settings of the number of training samples can cause overfitting of the data, B is set to 64. It is possible to guarantee the training speed while obtaining  
Figure 3: CNN–GRU model framework diagram.  
a relatively low training and testing error. Optimizer uses the Adam gradient descent algorithm. It uses gradient moment estimation to dynamically adjust the learning rate of the parameters. Using bias correction, the iterative learning rate is within a certain range and the parameters are relatively stable. This is conducive to fully extracting traffic flow characteristics [25]. To test the validity of the proposed model, it is preferable to use a learning rate equal to 0.01.  
According to the characteristics of traffic volume extraction by a convolutional neural network, this paper intends to set up two convolutional layers. The convolution kernel size is \(1\times 1\), the step size is 1, the number of convolution kernels is 32 and 16, respectively, and the number of pooling layers is 2. The maximum pooling is used. The activation function is the rule function. For the gated circulation unit layer, a GRU is applied for extracting the traffic volume time characteristics. The GRU sets up 2 layers with 20 and 10 neurons in each layer, the time step is taken as 8, and 8 historical data are used to predict the next moment's traffic volume.The specific data flow is shown in Figure 4.
# A Wideband Power Amplifier in 65 nm CMOS Covering 25.8 GHz-36.9 GHz by Staggering Tuned MCRs
## 2 Analysis and Design
### Wideband Output Matching Network Design
The test circuit for the power stage with the whole output matching network is shown in \[\omega_{1}=\omega_{2}=\omega_{0} \tag{3}\]1 With ideal LC input-matching networks at every single-frequency point, the large-signal performance of the power stage is tested from 26 GHz to 36 GHz and the results are shown in Figure 11a,b. For the large-signal performance, the power stage achieves a \(P_{sat}\) of 17.87 dBm at 31 GHz, a \(PAE_{max}\) of 33.55% at 26 GHz, and a 14.4 dBm \(OP_{1 dB}\) at 31 GHz and 32 GHz. From Figure 11b, the simulated results reveal over a 17.3 dBm \(P_{sat}\), over a 25% \(PAE_{max}\), and over a 13.8 dBm \(OP_{1 dB}\) from 26 to 36 GHz, which explains the wideband load-pull matching achieved by the whole output matching network. \[\omega_{1}=\omega_{2}=\omega_{0} \tag{3}\]2 (a) The coupling coefficient between \(L_{1}\) and \(L_{2}\) for the output transformer (TF4) in MCR4 and (b) the 3D view of the whole output matching network. \[\omega_{1}=\omega_{2}=\omega_{0} \tag{3}\]3 (a) The insertion loss of the whole output matching network and (b) the Z21 of MCR4. \[\omega_{1}=\omega_{2}=\omega_{0} \tag{3}\]4 The Z21 test schematic for MCR4. \[\omega_{1}=\omega_{2}=\omega_{0} \tag{3}\]5 (a) The power gain (Gp) of power stage with the whole output matching network and (b) the input impedance of the output symmetrical MCR4 (including \(C_{11}+C_{12}\) of the power stage). \[\omega_{1}=\omega_{2}=\omega_{0} \tag{3}\]6 (**a**) Pout and PAE of power stage with ideal LC input-matching network at 32 GHz and (**b**) Pout and PAE of power stage with ideal LC input-matching network from 26 GHz to 36 GHz. \[\omega_{1}=\omega_{2}=\omega_{0} \tag{3}\]7 Circuit schematic of power stage with ideal LC input matching.
# Exploring Core Knowledge in Interdisciplinary Research: Insights from Topic Modeling Analysis
## 3 Methods
### LDA Topic Model  
LDA [22] is a classic probabilistic topic modeling approach commonly used in text mining to identify hidden thematic information in large-scale, unstructured text collections or corpora. The LDA model consists of a three-layer structure: feature words, topics, and documents. The model assumes that all documents in a corpus are composed of a set of latent topics, and each topic is associated with a weight indicating its relevance to a given document, forming a document-topic distribution. The higher the weight, the stronger the association between the topic and the document. Each topic, in turn, consists of a set of interrelated feature words, with each feature word having a certain probability of being selected for a specific topic, forming a topic-word distribution. The higher the probability, the stronger the association between the feature word and the topic, thus providing more semantic information to explain the meaning of the topic.  
In the LDA model, the generation process of a document's \"document-word\" structure is depicted in Figure 1. First, the topic distribution \(\theta_{m}\) of the document is selected from a Dirichlet distribution with hyperparameter \(\alpha\), meaning that the topic distribution \(\theta_{m}\) is generated from a Dirichlet distribution parameterized by \(\alpha\). Next, a topic \(z_{m,n}\) for the \(n\)-th word in the document is sampled from \(\theta_{m}\). Then, a multinomial word distribution \(\varphi_{k}\) corresponding to the topic \(z_{m,n}\) is sampled from a Dirichlet distribution with parameter \(\beta\), Finally, the word \(w_{m,n}\) is sampled from \(\varphi_{k}\). This process is repeated \(n\) times to generate the \"document-term\" structure for a document.  
Figure 1: The generation process of “document-word” for a document in LDA model.
# A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects
## 3 Numerical Modeling
### Analysis of Simulation Results  
#### 3.2.1 Ultrasonic Wave Propagation in the Medium  
Figure 3 shows the propagation of ultrasonic waves in different periods in the medium when detecting a circular cavity. It can be seen from the figure that the longitudinal wave and Rayleigh wave are generated and propagated together when ultrasonic wave occurs, and the longitudinal wave propagates along the normal direction with the fastest velocity. At the interface between water and the part to be tested, the ultrasonic wave is partially reflected. Because the pressure acoustic physical field interface is used to deal with the ultrasonic wave propagation in the solid, the shear wave generated by waveform conversion cannot be seen. While the longitudinal wave continues to propagate forward, the echo on the upper surface of the aluminum alloy block also oscillates back and forth in the coupled water layer and partially enters the aluminum alloy block. These waveforms will lead to the detection of different strong and weak waveforms at different times when the transmitted wave is detected. The wavelength of ultrasonic waves in aluminum alloy medium is 2.5 mm. Figure 3d shows that when it meets the circular hole defect with a diameter of 0.8 mm during its propagation, part of the reflection occurs, which leads to the reduction of the transmitted wave energy compared with that without the defect.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 6 Discussion
### Comparison with State-of-the-Art Methods
#### 6.1.2 Types of Anomalies Detected  
Adaptive-DecayRank effectively captures both **structural anomalies** and **micro changes** in node influence. Structural anomalies arise when nodes exhibit sudden, unexpected changes in connectivity patterns, as seen in **botnet behavior** in CTU-13 [42] or **network intrusion** patterns in DARPA [41]. These are particularly challenging for methods relying on static graph snapshots, as they fail to adapt to rapidly evolving structures.  
Additionally, the model detects **micro-changes in node influence**, where nodes subtly shift their interaction patterns over time. Such anomalies occur in stealthy cyber-intrusions, where attackers gradually increase their influence within a network without abrupt changes [2]. The adaptive decay factor in our model ensures that these incremental deviations accumulate and get flagged as anomalies, making it particularly well-suited for cyber-threat detection.
# Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments
## 5 Conclusions  
In this article, we have discussed the implementation and performance of an NDN solution over a real smart city communication infrastructure. Our implementation, a container-based solution to be deployed in the network nodes, either mobile nodes or those belonging to the non-mobile infrastructure, was designed to allow the simultaneous operation of NDN and TCP/IP communications, offering facilities for on-the-fly network upgrades without compromising the network operation, and it allows the use and the performance evaluation of both communication paradigms simultaneously.  
Evaluation tests, using real hardware equipment and mobility, have shown that our NDN solution, a modified version of the original NDN implementation with new heuristics about the forwarding, caching and support for publish-subscribe communications, is able to deliver the Content to the requested mobile users, directly from the infrastructure, or from the cache of other mobile nodes. In addition, we have shown that the NDN architecture can successfully explore the use of multiple communication interfaces for a faster and reliable Content retrieval. In the end, we analyzed how the performance of the NDN architecture is impacted by the network characteristics, experimenting different MTUs, that consequently lead to different chunk sizes.  
As a future work, we aim to develop new heuristics for the management of the multiple communication interfaces by selecting the best interface for a given application according to the status of each interface. Another line of research includes the adaptation of the NDN solution to SDN-based environments and its assessment in the SDN-capable ATCLL communication infrastructure. Finally, to improve security in packet exchange, authentication mechanisms for NDN should be explored.  
Data curation, L.G.; Investigation, L.G., C.S. and M.L.; Supervision, C.S. and M.L.; Validation, L.G., C.S. and M.L.; Visualization, C.S. and M.L.; Writing--original draft preparation, L.G.; Writing--review and editing, C.S. and M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020, Regional Operational Program of Lisbon (FEDER) and Foundation for Science and Technology, project InfoCent-IoT (POCI-01-0145-FEDER-030433), and by FCT/MEC through national funds and when applicable co-funded by FEDER - PT2020 partnership agreement under the project UIDB/50008/2020-UIDP/50008/2020.  
**Institutional Review Board Statement:** Not applicable.  
Figure 11: Download durations and number of timeout events for different MTUs and Interest timeouts. (**a**) Time to download file. (**b**) Timeouts.
## 5 Group Communication Using Unicasts  
We aim to create an intermediate level of aggregation to be able to easily manipulate a group of resources across multiple smart objects. To avoid increasing the _footprint_ of the constrained devices, we use the _same technology_ as used to manipulate individual resources, _i.e._, CoAP, and extend it accordingly. Such a group of resources is called an _entity_ and the entity can be used or manipulated through a single CoAP request. Similarly, the creation of an entity by a client is realized via a single CoAP request and includes a complete _validation_ of the entity. Furthermore we introduce the notion of profiles for the created entities. The use of entity profiles allows the client to specify in more detail how the _entity should behave_ (e.g., if it should use confirmable or non-confirmable CoAP messages), and, through updating the profile, allows manipulation of this behavior. As such, we strive to combine ease of creation, ease of usage and _flexibility_ in behavior into a complete solution for interacting with CoAP resources from different objects inside a LLN. By building upon _standardized_ concepts, the impact on the constrained devices is limited. In the following subsections we discuss the details of our approach.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 3 Methodology  
In this section, we first formulate the target of multi-object tracking and give an overview of the proposed FusionTrack model in the first two sections. We then follow the computation order of the model to present details of the proposed joint track-detection decoder, score-guided multi-level query fuser, and the track management and propagation strategy. Finally, we discuss how we train the proposed method.
## 1 Introduction
### Contributions  
Motivated by the aforementioned considerations, we address the problem of palmprint verification using a transfer learning approach. Our approach uses low-resolution images, and the main contributions are summarized as follows:  
* To the best of our knowledge, this is the first study that extracts palmprint ROIs by convolutional neural networks. Lack of sample images and small intra-class variability are addressed using transfer learning.
* We successively apply a pre-trained CNN network to extract discriminative features along with a machine learning classifier to measure the similarity for one-to-one matching.
* We achieve an intersection over union (IoU) score of 93% for palmprint ROI extraction and equal error rate (EER) of 0.0125 for the verification task using the support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. This can be attributed to the superiority of discriminative deeply learned features over hand-crafted features.