esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 6 Conclusions  
This paper transfers the target detection model in the visible light domain to the infrared environment by transfer learning. In our work, we creatively proposed the method of feature fusion and multiscale feature extraction with two shared features. We obtained the network architecture of CMF Net that makes full use of multiscale feature fusion information for target detection. Through multiscale feature fusion, rich visual and semantic features can be obtained to improve the accuracy of target detection and adapt to the multiscale characteristics of the target to be detected. To improve the target detection performance of the model in complex scenes such as mutual occlusion and overlapping of multiple targets, we constructed a 3D long- and short-term memory network based on CMF Net to extract context information and finally realized the CMF-3 DLSTM model. Compared with Faster R-CNN, YOLO3, and SSD, CMF-3 DLSTM achieves higher target detection performance on infrared image dataset FLIR. This proves the importance of constructing an infrared image target detection model based on multiscale feature fusion and context analysis.  
However, we still need to make further improvements work. The target detection model proposed in this paper improves the accuracy, reduces the number of parameters, and decreases the spatial complexity compared with models such as Faster-RCNN, but it increases the time complexity, making it challenging to meet the requirements of real-time detection. We need to optimize the network structure further to improve the model's  
Figure 11: Performance comparison between CMF-3 DLSTM and other target detection models.  
Figure 12: Comparison of target detection results between CMF-3 DLSTM and CMF Net.  
real-time detection capability. At the same time, we need to train and evaluate the model on more infrared image datasets with different scenes to meet the requirements of other scenes in practical applications.  
Data curation, L.Y.; Investigation, S.L.; Methodology, L.Y. and Y.Z.; Resources, S.L.; Supervision, S.L.; Validation, Y.Z.; Writing--review & editing, L.Y., S.L. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental and Discussion
### Performance of the Construction Algorithm
In order to demonstrate the performance of the proposed method, the results of the classical area-equaling triangulation method [4] and the Delaunay mesh construction algorithm [13] are performed with the same model and shown in Figure 9c and Figure 9d, respectively. The selected area-equaling triangulation method is a basic algorithm for model meshing of finite element method (FEM), and [PERSON]'s method [13] is a typical representation for the construction of the Delaunay mesh. The number of vertices using their methods are 7003 and 7907, respectively. It is obvious that the construction algorithm proposed in this paper has better performance in the aspects of simulation accuracy with less vertex numbers.
## 3 The Test Results and Discussion
### Variation of Cyclic Stress Amplitude  
As seen in Figure 8, obtained through Figures 6 and 7 are curves about the variation of the cyclic stress amplitudes with increases to the cyclic cycles of the welding materials and steels, in which the non-dimensional parameter \(\eta\) was obtained from the numbers of semi-cycles in a certain time divided by the numbers of semi-cycles at the end of the test. Each picture in Figure 8 contains three stress amplitude-\(\eta\) curves that were obtained from three loading conditions with different strain amplitudes, which can be realized from Table 1 and Figure 4. Therefore, the influence of weld form, base material type, and loading mode on the damage accumulation of the welding materials will be compared and analyzed.  
As shown in Figure 8a-f, the degradation of the cyclic stress amplitude is basically divided into three stages, corresponding to the three stages of crack development in a low-cycle fatigue test. The stress amplitude degenerated rapidly in the first stage, which has a number of cycles less than 20%, corresponding to the initial stage of fatigue crack development. When the numbers of cycle reached about 20%, the stress amplitude decreased steadily, and corresponded to the stable stage of fatigue crack development, which is the longest stage of stress amplitude degradation. Finally, the stress amplitude degenerated sharply and the specimens were fractured rapidly when the number of cycles reached about 80% or 90%. Loading strain amplitude has a certain effect on the damage degradation rate. As we can see, there are three curves in each picture of Figure 8a-f, and the loading train amplitude of the curves increased in turn. It indicates that the cyclic stress amplitude increased with the increasing of the controlled strain amplitude. Moreover, the specimens with larger controlled strain amplitude degenerated faster in the failure stage. All of the above indicate that the larger the controlled loading strain amplitude, the faster the rate of damage accumulation.  
The influence of type of specimens also should be discussed. As shown in Figure 8g-h, the stress amplitude-\(\eta\) curves of WA and WB welding specimens are compared at the same time. It can be concluded that the stress amplitude of the welding materials with Q235 base metal degenerated faster than that of the Q345 base metal, and the curves of WB specimens are more concentrated than those of WA specimens, indicating that the influence of the loading strain amplitude in the rate of damage accumulation is more significant for welding materials with a Q235 base steel. For the influence of the welding mode, the welded form of No. (1) specimens with the Q235 and Q345 base metal is a butt-weld (the dotted line in Figure 8g-h), whose stress amplitudes are lower than that of the fillet weld specimens under the same working conditions. This can be attributed to the fact that the structure and welding process of the fillet welds are more complex than the butt welds, resulting in more damage accumulation than the fillet welds.
# State of Industry 5.0--Analysis and Identification of Current Research Trends
## 3 Data Analysis and Discussion  
Text mining, which is also referred to intelligent text analytics, text data mining, and text knowledge discovery, is defined as the discovery of either new or previously unknown information through the extraction of information from various written resources. Text mining help to uncover new information and knowledge by identifying patterns in documents from several sources [11]. Text mining may involve several other methods such as Natural Language Progression (NLP), Information retrieval, Clustering, Document Classification, Web mining, Information Extraction, and Concept Extraction [12]. It has been widely recognized among researchers that text mining's feasibility for exploring published literature and discovering concepts and trends across a given domain has seen tremendous growth. For example, [PERSON] et al. discuss the advantage of using text mining in the financial sector for stock market predictions [13], [PERSON] portrays the applicability of text mining for studying organizations' social and environmental reports [14], [PERSON] et al. use text mining to study the social media usage of traditional media houses in Uganda to understand the topics these media houses discuss and determine if they are positively or negatively correlated [15], and use of text mining tools and techniques to analyze the landscape of Model based Systems Engineering [16].  
In this paper the text mining framework as illustrated in Figure 1[17] is used for exploring and analyzing the published articles on Industry 5.0 to identify key terms often used and the themes into which Industry 5.0 research can be classified into using the text data extracted.
## 2 System Model
### Non-Line-of-Sight Identification of Ultra-Wideband  
A double-sided two-way ranging method was used in this study to obtain the relative range measurements among the firefighters. However, the ranging error may show different error characteristics according to the environment [38; 39]. In the LOS conditions, the range measurements were accurate, and the standard deviation of the ranging errors was small. Hence, the ranging errors could be easily calibrated. In the NLOS conditions, the error distribution models of range measurements were complicated, and there were no unified models.  
Inaccurate range measurements caused by NLOS conditions lead to poor localization results in CL; here, we utilized the received power and signal power in the first path to determine the LOS and NLOS conditions.  
The received power was:  
\[RX_{L}=10\times\log_{10}\bigg{(}\frac{C\times 2^{17}}{N_{L}^{2}}\bigg{)}-A \tag{20}\]where \(C\) is the channel impulse response power in the register, \(N_{r}\) is the preamble accumulation count value in the register, and \(A\) is the constant related to the register configuration.  
The signal power in the first path was:  
\[FP_{L}=10\times\log_{10}\left(\frac{{F_{1}}^{2}+{F_{2}}^{2}+{F_{3}}^{2}}{N_{r}^{ 2}}\right)-A \tag{21}\]  
where \(F_{1}\), \(F_{2}\) and \(F_{3}\) are the first path amplitude magnitude values in the registers.  
The difference values between the \(RX_{L}\) and \(FP_{L}\) were used as the criterion for LOS and NLOS conditions in this study [40], and the threshold was selected as 10 dB in Figure 3. The determined NLOS values were subsequently used in the adaptive estimation algorithm to enhance the localization accuracy.
## 4 Factors Affecting Wound Healing Process
### Systemic Factors That Influence Healing
#### 4.2.4 Diabetes and Other Autoimmune Diseases  
Diabetes induces a hypergly environment, disrupting the normal phases of wound repair. This disruption prolongs inflammation, compromises angiogenesis, and hinders collagen synthesis, resulting in chronic non-healing wounds [53]. Hyperglycemia exacerbates oxidative stress, contributing to the sustained activation of pro-inflammatory cytokines and impaired function of critical wound-healing cells, including macrophages, neutrophils, fibroblasts, and keratinocytes. Specifically, diabetic wounds exhibit dysfunctional macrophage polarization, the attenuated production of growth factors like VEGF, PDGF, and TGF-\(\beta\), and elevated matrix metalloproteinase (MMP) levels. Elevated MMPs degrade extracellular matrix proteins excessively, impairing wound closure [54]. Microvascular complications due to diabetes cause local ischemia and hypoxia, further delaying wound repair by reducing nutrient and oxygen supply. Advanced glycation end-products (AGEs) formed during chronic hyperglycemia interfere with collagen deposition and induce inflammatory responses, amplifying tissue damage and delaying healing [55].  
Another systemic factor affecting wound healing is autoimmune diseases. In individuals with immune system disorders, the wound healing process is significantly prolonged [56]. Ulceration and recurrent leg ulcers are commonly seen in autoimmune diseases such as rheumatoid arthritis and lupus. The prolonged healing time in these patients increases the risk of infection [57].
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 5 Results and Analysis
### The Impact of \(\gamma\), \(\beta_{0}\) and \(\mu\)
Considering the effects of different parameters illustrated in the last paragraph, among various parameter configurations with \((\gamma,\beta_{0},\mu)\) format, \((2,\ -30\ \mathrm{dB},\ 0.5)\), \((2,\ -30\ \mathrm{dB},\ 1)\) and \((2,\ -20\ \mathrm{dB},\ 1)\) can be chosen for practical applications. This is because relatively better performance can be achieved for four objective metrics. One can observe that the three competing loss functions can get better performance in some objective metrics, while they may suffer much worse performance in others. For example, SI-SDR has the largest value of SDR, while its PESQ score is even lower than the MSE, which is consistent with the study in [19].
# CDA-Net for Automatic Prostate Segmentation in MR Images
## 3 Methods
### RAU-Net for Prostate Segmentation
#### 3.2.2 Self-Attention Gating Mechanism  
To improve the accuracy of network segmentation and better integrate the deep semantic features and shallow detail features, this paper introduces a self-attention gating module before the splicing operation similar to U-Net. As shown in Figure 9b, taking _Input_1 : \(d_{l}\in R^{H_{l}\times W_{l}\times F_{l+1}}\), _Input_2 : \(u_{l}\in R^{H_{l-1}\times W_{l-1}\times C_{l}}\) as input and \(Output:o_{l}\in R^{H_{l-1}\times W_{l-1}\times C_{l}}\) as output, the attention gating module \(\alpha_{l}\) can be formulated as:  
\[\varphi_{2}(conv_{1\times 1,1}(\varphi_{1}(conv_{1\times 1,N_{1}}(d_{l})+ conv_{1\times 1,N_{1}}(u_{l})))), \tag{4}\]  
where \(\varphi_{1}\) and \(\varphi_{2}\) denote the ReLU function and Sigmoid function, respectively.  
_Input_1 in the decoding path is added to the low-level feature map _Input_2 to generate the attention weight map. The weight map is multiplied by _Input_2 to achieve the activation of different degrees of importance to different regions in _Input_2. As the feature map in the decoding stage integrates more image semantic information and contextual information, the soft region suggestion generated implicitly in combination with it is closely related to the segmentation task. The attention distribution acts as a gating signal on the shallow feature map generated in the encoding stage, which can filter out the noise information of the background area in the low-level features that are not related to the segmentation task, reduce the feature activation of the background area, and achieve more efficient fusion of encoding and decoding features to improve the accuracy of label prediction.
# Low Power FPGA Implementation of a Smart Building Free Space Optical Communication System
## 4 Results  
The experimentation has been staged to test each part of the system individually. First of all, the UART communication between FPGA and the PC via the application of the RISC-V processor data management technique was tested before indulging the laser communication part into the system. The laser module used had a 5 mW maximum power. Second of all, the optical communication line was fully tested using the OOK modulation technique at a reached data rate of 115 kbps and a distance of 12 m. Lastly, the IM/PWM technique was applied in the Quartus software interface controlling the illumination of the laser module to achieve a doubled data rate of around 230 kbps. Initially, Figure 14 shows the UART successful half-duplex channel between the PC and FPGA with the additional processing help of RISC-V via Linux Mint interface before including the hardware optical system components.  
Figure 13: System Flowchart.
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
###### Abstract  
With the development of system-on-chip (SoC) and chiplet technology in the post-Moore era, an increasing number of chiplets are being integrated into a single chip. Consequently, the functions and complexity that can be realized are growing daily. Simultaneously, the volume of test data required for ultra-large-scale integrated circuits (ULSIs) has risen significantly. However, traditional automatic test equipment (ATE) is constrained by its data storage and bandwidth limitations, and its long technology iteration cycle. These cannot keep pace with the rapid development of design technology. This discrepancy leads to challenges in ULSI testing, such as excessively long test time and difficulties in completing the tests. Test compression technology can effectively address these issues by reducing the performance requirements of the test equipment, which in turn can lower test costs. This paper summarizes the classifications of chip test compression technology and, based on their current development, provides a detailed analysis of key technologies. It includes test compression-oriented coding methods, optimization of scan chain structures, and enhancements in coding for compression efficiency. Finally, a forward-looking perspective on the development of chip test compression technology is presented. The aim is to offer a reference for subsequent research in this field and related areas, as well as to provide technical support for the advancement of ULSI testing in the post-Moore era.  
integrated circuits; testing technique; test compression +
Footnote †: journal:  
products. As an effective method to guarantee the reliability of integrated circuits, integrated circuit testing technology is facing unprecedented challenges, mainly encompassing the following four aspects.
## 4 Overview of DSRC, LTE C-V2X, and 5G NR V2X
### Challenges Faced by DSRC and C-V2X  
Scalability is a major challenge for DSRC as its performance degrades at higher vehicle densities due to packet collisions. However, due to frequency re-using by the C-V2X sidelink mode 4 algorithm over a given geographical area, higher traffic density also results in an increased interference level among C-V2X users. Hence, although C-V2X offers several performance benefits over DSRC, it suffers from the same traffic density issue [13].  
Another challenge for both DSRC and C-V2X is supporting advanced safety applications requiring faster response times. Both technologies can support a basic set of vehicular safety applications that generate driver alerts to indicate potentially dangerous conditions [35]. These day-1 basic safety applications have lower periodicity and higher end-to-end latency requirements. However, advanced vehicular safety applications depend on transmitting messages for maneuver changes, trajectory alignments, platoon formations, and sensor data exchange, as well as sharing information gathered from neighboring vehicles' live camera feeds [48]. This additional information is paramount to ensuring both safer autonomous cars and human-driven vehicles, better traffic management, and the increasing demand for in-vehicle infotainment.
## 4 Analytical Model For Optimal Cache Deployment  
### Problem Statement  
The problem of optimal cache deployment in a hierarchical metro network can be stated as follows. Given a maximum overall amount of storage capacity, a certain metro network topology, the potential locations of caches and the characteristics of the content catalog (catalog size, popularity distribution), we must find the optimal storage capacity distribution such that the overall average resource occupation (\(RO\)) is minimized. To solve this problem, we derive an analytical model which returns the amount of storage to be utilized in the caches at various levels with the objective of minimizing the \(RO\). Similar to previous works (e.g., [16]), we assume the average hop-count as a main metric to estimate the overall RO, where the RO is assumed to be the product of the average hop-count and the average bit-rate.  
For sake of clarity, Figure 3 shows two possible cache deployments for a case in which 160,000 GB are available to be distributed among caches: deployed at AMENs, (i.e., at the access network level), and among the MCENs (i.e., at the metro network level). The example considers eight AMEN caches and one MCEN cache. The content catalog considered has 20,000 items. In the first example (cache deployment #1), AMEN caches are allocated 12,000 GB of storage capacity to store the most popular 1500 VoD items; meanwhile, the MCEN cache is allocated 64,000 GB of storage capacity to store the next most popular 8000 items. Consequently, items ranked from 9500 to 20,000 are not cached; i.e., they remain at the remote video server. In the second example (cache deployment #2), the AMEN caches are allocated more storage capacity than those in cache deployment #1: 15,000 GB to store the most popular 1875 items. Consequently, the MCEN cache is allocated 40,000 GB of storage capacity, less than the MCEN cache in cache deployment #1, and therefore, it stores fewer of items--only 5000. Each cache deployment results in different storing of items in caches at AMENs and at MCEN (or left at the distribution video server), and therefore, in a different network resource utilization; however, it  
Figure 2: Effect of the popularity skew parameter \(\alpha\) on the popularity distribution of a content catalog of 100 items for \(\alpha\) = 0.8, 0.9 and 1 for the most popular 25 items.  
is not obvious which cache deployment results in a lower network resource occupation. For a more detailed description of this example, we refer the reader to [9].
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 4 Proposed SLAM Classification Based on UWSNs
### New Classification Based on UWSNs
#### 4.2.4 Hybrid SLAM Systems  
Hybrid SLAM systems combine standalone and communicative elements, dynamically switching modes based on communication availability. When communication networks are accessible, these systems leverage external data and collaborative opportunities; otherwise, they operate autonomously using onboard resources. This flexibility makes hybrid systems particularly suitable for environments with intermittent communication, such as deep-sea exploration or areas with variable acoustic conditions.  
The proposed UWSN-based classification directly addresses key operational challenges in underwater SLAM deployment. Standalone systems enable missions in communication-denied environments. UWSN-integrated systems enhance monitoring capabilities through collaborative sensing. Communication-aware systems optimize bandwidth usage in dynamic channels, reducing data loss through adaptive compression strategies. Hybrid systems provide mission continuity in variable conditions. This framework empowers practitioners to select optimal architectures based on environmental constraints, network availability, and mission requirements.  
In summary, various classification schemes--based on sensors, computational methods, environmental factors, and communication integration--further clarify the capabilities and limitations of different approaches. These classifications guide the selection and design of SLAM solutions, ensuring practitioners choose methods aligned with their underwater environment and operational constraints.