esa-sceva/satcom-chunk-collection huggingface dataset:
# Body Area Networks in Healthcare: A Brief State of the Art
## 8 Conclusions  
This work has presented a brief state of the art of the body area network technology, with a special emphasis on BAN applications in the healthcare domain. The objective of this paper has been to shortly review the architecture, network topology, energy sources, applications and main challenges for these systems.  
In this regard, the IEEE 802.15 standard provides guidelines for BAN applications and defines different layers that these types of solutions must follow. Security is a really important feature in BAN products as the information generated by the applications must be accessible only to people who are supposed to have access to the data provided. Sustainability is another significant factor in developing BAN solutions as well. A good design leads to a durable and eco-friendly product.  
Different types of wearable sensors have also been presented, highlighting the advantages and weaknesses of each one. Studies from teams around the world working on different wearable applications have been introduced. These studies show the great potential for innovative systems that use these kinds of sensors. In conclusion, we have presented a survey on the state of the art of the latest BAN solutions in healthcare as provided by the leading researchers. Throughout this article, many publications have been referenced with the aim of showing the manifold benefits that BANs bring to the area.  
Regarding future studies, it is our intention to investigate other topics related to BAN solutions apart from the healthcare arena. This will help to obtain a wider view of this technology in other important fields of research.  
Conceptualisation, A.P., A.F.-C. and N.C.; methodology, D.V. and L.R.-S.; investigation, L.R.-S. and T.O.; writing, D.V., L.R.-S. and T.O.; funding acquisition, A.P. and A.F.-C.  
This work was partially supported by FCT - Fundacao para a Ciencia e Tecnologia, I.P., under projects UID/CEC/04524/2016 and UID/CEC/04524/2019. This work was partially supported by Spanish Ministerio de Economia, Industria y Competitividad, Agencia Estatal de Investigacion (AEI)/European Regional Development Fund (FEDER, UE) under DPI2016-80894-R grant.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
BAN Body area network IEEE TG6 IEEE 802.15 Task Group 6 IoT Internet of Things HBC-PHY layer Human body communication physical layer MAC layer Medium access control layer NB-PHY layer Narrow band physical layer PHY layer Physical layer PSKA Physiological-signal-based key agreement QoS Quality of service UWB-PHY layer  
WBAN  
WPA  
Ultra-wide band physical layer  
Wireless body area network  
Wifi protected access
## 3 Parameter Influence and Mesh Sensitivity of the Stress Integration Approach
### Influence of Parameters \(\delta\) and \(w\)  
In Figure 5, the mesh size of the refined weld is 2 mm, and the 20 node reduced integration element is adopted. Figure 5 shows the variation of the structural stress value based on the stress integration approach with two parameters: The distance \(\delta\) between the crack propagation surface and the reference surface, and the width \(w\) of the isolated body. In Figure 5a, \(w\) remains at 2 mm and \(\delta\) changes from 0 to 16 mm. When \(\delta\) is 2 mm, the structural stress takes the minimum value of approximately 264.45 MPa. When \(\delta\) is 16 mm, the structural stress reaches the maximum value of approximately 265.61 MPa. In Figure 5b, \(\delta\) remains at 0, unchanged, and \(w\) changes from 2 mm to 16 mm. The structural stress increases with the width \(w\), with the minimum value of 264.55 MPa, and with the maximum value of 264.84 MPa. Figure 4 shows that the structural stress does not change significantly with the parameters \(\delta\) and \(w\). Therefore, in order to simplify the calculation, \(\delta\) is set as 0 and \(w\) is set as 2 mm, which equals the mesh size along the weld length direction. Then, the structural stress can be calculated by Equation (7).
# GC4 MRec: Generative-Contrastive for Multimodal Recommendation
## 4 Experiment
### Performance Comparison
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{6}{c}{**R010 Ranking**} & \multicolumn{6}{c}{**R020 Ranking**} \\ \hline
**Model** & **Sports** & **Baby** & **Clothing** & **Model** & **Sports** & **Baby** & **Clothing** \\ \hline GC4 MRec (Ours) & 0.0784 & 0.0677 & 0.0645 & GC4 MRec (Ours) & 0.1178 & 0.1074 & 0.0959 \\ DCVAE & 0.0753 4.125 & 0.0636 43.895 & 0.0619 43.295 & DCVAE & 0.1127 43.595 & 0.1009 64.445 & 0.0917 43.995 \\ FREEDOM & 0.0717 5.395 & 0.0627 27.975 & 0.0629 25.945 & FREEDOM & 0.1089 81.749 & 0.0992 82.725 & 0.0941 15.795 \\ SIMRec & 0.0671 18.845 & 0.0684 34.395 & 0.0447 43.955 & SLMRec & 0.0998 8.055 & 0.0768 8.948 & 0.0662 44.848 \\ MMSSL & 0.0652 23.935 & 0.0620 39.995 & 0.0492 31.395 & MMSSL & 0.0981 20.005 & 0.0969 08.055 & 0.0783 22.495 \\ LATTICE & 0.0588 13.335 & 0.0528 38.225 & 0.0483 38.945 & LATTICE & 0.0926 27.215 & 0.0842 27.505 & 0.0726 26.995 \\ VIPR & 0.0542 44.659 & 0.0450 80.447 & 0.0277 132.895 & VBPR & 0.0849 38.755 & 0.0668 40.705 & 0.0405 136.795 \\ LightCC & 0.0527 897.75 & 0.0503 39.995 & 0.0327 193.087 & LightCC & 0.0832 42.956 & 0.0766 42.127 & 0.0547 57.332 \\ MMGCN & 0.0401 53.515 & 0.0390 39.995 & 0.0220 193.148 & MMGCN & 0.0632 83.955 & 0.0624 27.125 & 0.0347 17.395 \\ MF-BPR & 0.0451 73.845 & 0.0346 66.605 & 0.0177 284.447 & MF-BPR & 0.0662 77.955 & 0.0566 69.025 & 0.0252 280.505 \\ \hline \multicolumn{6}{c}{**N@10 Ranking**} & \multicolumn{6}{c}{**N@20 Ranking**} \\ \hline
**Model** & **Sports** & **Baby** & **Clothing** & **Model** & **Sports** & **Baby** & **Clothing** \\ \hline GC4 MRec (Ours) & 0.021 & 0.0360 & 0.0349 & CG4 MRec (Ours) & 0.0523 & 0.0462 & 0.0429 \\ DCVAE & 0.0410 2.69 & 0.0340 58.03 & 0.0336 39.875 & DCVAE & 0.0650 33.005 & 0.0465 38.045 & 0.0412 43.435 \\ FREEDOM & 0.0385 39.395 & 0.0330 39.995 & 0.0341 23.875 & FEEDOM & 0.0481 87.775 & 0.0424 36.905 & 0.0420 21.14 \\ MMSSL & 0.0490 4.003 & 0.0333 31.175 & QMSSL & 0.0462 13.395 & QMSSL & 0.0427 23.395 & 0.0427 34.395 & 0.0345 23.395 \\ SIMRec & 0.0354 18.895 & 0.0299 20.495 & 0.0231 50.945 & SLMRec & 0.0445 75.505 & 0.0367 58.995 & 0.0299 43.645 \\ LATTICE & 0.0311 18.575 & 0.0251 28.117 & 0.0251 39.045 & LATTICE & 0.0399 31.048 & 0.0352 21.293 & 0.0311 37.944 \\ VIPR & 0.0310 58.845 & 0.0236 65.845 & 0.0155 215.154 & VBPR & 0.0401 10.465 & 0.0299 45.523 & 0.0189 126.006 \\ LightCCN & 0.0291 144.045 & 0.0250 40.045 & 0.0192 87.775 & LightCCN & 0.0354 47.755 & 0.0335 57.018 & 0.0248 278.955 \\ MMGCN & 0.0189 122.755 & 0.0207 73.955 & 0.0115 204.455 & MMGCN & 0.0244 114.345 & 0.0284 24.695 & 0.0153 180.995 \\ MF-BPR & 0.0244 72.554 & 0.0205 75.845 & 0.0100 240.005 & MF-BPR & 0.0301 173.755 & 0.0250 48.4605 & 0.0119 208.505 \\ \hline \hline \end{tabular} Note. Improvement is calculated as (GC4 MRec Value – Model Value)/ Model Value \(\times\) 100.  
\end{table} 
Table 2: Performance comparison: models ranked by metric (with improvement). and metrics, as observed in Table 2, provides compelling evidence for the robustness and generalizability of GC4 MRec.
## 3 Proposed System Model  
In this section, we discuss the proposed PD-NOMA system model, where a single BS and \(N\) UEs are considered. The pilot data \((x_{i})\) are generated for each UE independently and then adaptively coded by applying BCH codes. The BCH code rates are randomly initialized and updated after each stage of channel estimation. The coded data \((x_{i})\) are applied to the modulator input, and the resulting constellation is rotated. The constellation rotation is followed by introducing the cyclic-Q delay. The output data streams, \(xq_{i}\), for \(N\) users are initially allocated random power coefficients and are superimposed to determine the signal, \(s\), to be transmitted. The signal, \(s\), is transmitted by a single BS antenna through \(N\) Rayleigh channels. The receiver at each UE terminal applies LSTM channel estimation to determine the channel conditions and the corresponding SNR value for its particular channel. Accordingly, the power coefficients and the BCH code rates are re-assigned depending on the predicted SNR value. Each receiver performs SIC to determine its own signal from the superimposed signal. The complete system model is shown in Figure 4, and the subsequent sections discuss the operation of each individual system component in detail.
# Improved Autocollimator for Roll Angle Measurement with an Enlarged Measuring Range
## 3 Experimental Results
### Calibration and Resolution Test  
A standard autocollimator (Shanghai MicroCre Light-machine, Type YRMAT-2010B, Shanghai, China) and a polyhedral mirror were employed as the benchmarks to calibrate the improved autocollimator. The standard autocollimator possessed a 0.1 arcsec resolution and \(\pm\)0.5 arcsec accuracy. The polyhedral mirror had 24 faces in total, and the angle between the normals of two adjacent faces was 15\({}^{\circ}\). Figure 5a illustrates the calibration experiment. The WP and polyhedral mirror were installed on a rotary table for rolling. The improved autocollimator was aligned to the WP, while the standard autocollimator on the side was aligned perpendicularly to one face of the polyhedral mirror. The rotary table rolled at 30\({}^{\circ}\) intervals and was measured simultaneously by both autocollimators. Figure 5b maps the results of the calibration experiment. The measurement errors acquired by comparing the measured values given by the improved autocollimator with the reference values given by the standard autocollimator did not exceed \(\pm\)13.55 arcsec, with a standard deviation of 8.88 arcsec, over a range of 360\({}^{\circ}\). These measurement errors were mainly caused by random and systematic errors. The former arose from electrical noise in the CMOS, fluctuations in environmental parameters such as humidity and temperature, mechanical vibrations, and stray light. The latter originated from the WP's optical parallelism deviation owing to manufacturing flaws, as well as deformation and misorientation owing to improper installation. The measured values of the improved autocollimator were linearly fitted, and a linearity of 0.9993 was obtained.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Order** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Measured value (arcsec) & \(-2.55\) & \(0.34\) & \(-2.83\) & \(-4.73\) & \(-1.67\) & \(-5.5\) & \(-1.19\) & \(-2.16\) & \(-3.76\) & \(-3.44\) \\ \hline \end{tabular}
\end{table}
Table 1: Results of repeatability test.  
Figure 4: Results of stability test.  
Moreover, the roll angle resolution was tested utilizing the same experimental kit. The standard autocollimator monitored the roll angle increment of a certain face of the polyhedral mirror, while the improved autocollimator synchronously detected the roll angle of the WP, and its measurement results are drawn in Figure 6. As can be seen, the improved autocollimator could recognize the increment of 10 arcsec, and thus the roll angle resolution was better than 10 arcsec.
## 8 Implementation and Discussion  
The BFS system, as evidenced by its implementation, offers transformative potential in financial reporting and liquidity management. By adhering to the architectural framework of DDD, the BFS system prototype demonstrates its theoretical feasibility and practical effectiveness in developing a technically sound and domain-aligned modular solution capable of integrating blockchain technology within the financial reporting domain and aligning with business orchestration needs. The Java-based prototype of the BFS system demonstrates its functionality, encapsulating the dual-blockchain framework, smart contracts, and interaction dynamics among various system actors, such as business entities, counterparties, validators, and accountants. This setup demonstrates how the BFS system can facilitate secure, efficient, and transparent financial transactions within an integrated ecosystem. This approach ensures that the complexities within the wide business domain in the BFS system and the interactions of its components are accurately modelled and implemented. Overall, the BFS system prototype not only confirms the feasibility of integrating blockchain technology into financial and accounting systems but also highlights the practical benefits of this integration for enhancing the transparency, efficiency, and security of financial operations.  
The BFS framework serves as a conceptual basis for a blockchain-based accounting system. Our focus here is on establishing the fundamental architecture, components, and interactions within the proposed framework. Specifying detailed efficiency metrics, such as data throughput, volume, query capability, or energy consumption, and setting corresponding baselines would require a fully functional implementation and extensive testing. Similarly, a thorough security assessment, including identifying potential attack vectors, attacker resources, and motives, necessitates evaluating an implemented system. The BFS framework uses key features of blockchain technology, such as immutability, decentralisation, and cryptographic mechanisms, which can enhance the security and transparency of the accounting system. The proposed framework also aims to improve data accessibility, enable real-time reporting, and streamline processes, potentially increasing efficiency in accounting practices. However, further research and development are needed to fully realise and validate these potential benefits. Future studies should focus on implementing the BFS framework, defining the relevant metrics, and conducting rigorous evaluations to assess its efficiency and security against established baselines and potential threats. This approach will help address the specific requirements and objectives of the system and provide a better understanding of the practical implications of the BFS framework. Despite these limitations, our work provides a valuable starting point for exploring the application of blockchain technology in accounting systems and lays the groundwork for future advancements.
# Contribution Analysis of Assembled Brake System to Reduce Squealing
## 5 Conclusions  
A contribution analysis was conducted to select the target subpart of an assembled brake system to control squealing. FE models of the disk brake assembly were constructed, and two critical frequencies, 3.4 and 6.4 kHz, were derived by complex eigenvalue analysis. The model was validated through a brake dynamo test, and two major squealing frequencies, 3.2 and 6.8 kHz, were identified. The FE model was reliable, with small spectral frequency errors of 6.25% and 5.88%, respectively. The contribution analysis was conducted using a proposed contribution index, and all spectral response vectors of the subparts were obtained through a forced response analysis of the assembled brake system model. Based on the contribution analysis, the brake pad was selected as the target subpart of the redesign process. The design modification plan of the disk pad was selected for two different shape angles, 60\({}^{\circ}\) and 55\({}^{\circ}\). Finally, the redesigned solution was verified using a chassis dynamometer test, which detected no squealing at the modified shape angle of 55\({}^{\circ}\).  
**Author Contributions:**: Mechodology, investigation, formal analysis, writing--original draft preparation, C.-J.K.; resources, data curation, writing--review and editing, S.-J.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:**: This research was supported by Research Grant of Pukyong National University (2021).  
**Institutional Review Board Statement:**: Not applicable.  
**Informed Consent Statement:**: Not applicable.  
**Data Availability Statement:**: The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:**: The author declares no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pad Shape** & **Squeealing** & **Sound Pressure Level** \\ \hline Base brake pad & Yes & 74–78 dB(A) \\ (\(\theta=60^{\circ}\)) & No & - \\ \hline Modified brake pad (\(\theta=55^{\circ}\)) & No & - \\ \end{tabular}
\end{table}
Table 2: Chassis noise dynamometer test results.  
Figure 12: Measured spectral response of the microphone data: (**a**) base brake pad and (**b**) modified brake pad.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 5 Proposed WLAN Aware Cognitive MAC (WAC- MAC) Protocol
### Adaptive Wake-Up Scheduling for Sensor Nodes  
IoT sensor nodes go into sleep mode for the whole superframe duration if the medium is sensed as busy and wakes up at the start of the next beacon interval. This is done to conserve energy in sensor nodes to extend their lifespan; however, it also results in large end-to-end delay. To overcome this problem, in WAC-MAC, an adaptive wake-up scheduling scheme is proposed based on results of energy detection in which sensor nodes adjust their sleep time if the medium is found busy. This is done through pruning in superframe duration \(SD\) by half, thus resulting in an early wake-up. The coordinator node, after beacon transmission, immediately starts sensing the spectrum for primary user activity. In the case of the user being present, the coordinator node also adjusts its beacon transmission time accordingly. IoT sensor nodes dynamically adjust their wake-up scheduling based on the result of energy detection to reduce end-to-end-delay, as illustrated in Algorithm 1.  
```
1 Initialization;
2 Sensor node wake-up and starts listening the channel;
3 Data: Input
4\(E_{WLAN}\leftarrow\) WLAN Energy;
5\(T_{sleep}\leftarrow\) Sensor Sleep Time;
6\(T_{tx}\leftarrow\) Transmission Time;
7\(T_{beacon}\leftarrow\) Beacon Frame;
8\(T_{sensing}\leftarrow\) Sensing Time;
9\(SD=960\times 2^{SO}\);
10\(SD=BI\leftarrow\) Superframe Duration = Beacon Interval;
11 Scan the channel until receive the beacon;
12 Scan the channel for WLAN activity;
13 while Scanning for WLAN activitydo
14 if\(E_{WLAN}=1\)then
15\(BI=\frac{BI}{2}\);
16\(T_{sleep}=\frac{SD}{2}\times(T_{beacon}+T_{sensing})\);
17 WLAN user is present;
18 Superframe duration become half of the current duration;
19 else
20\(BI=BI\);
21\(T_{sleep}=SD\times(T_{beacon}+T_{sensing}+T_{tx})\);
22 WLAN user is absent;
23 Superframe duration remain same for the current duration;
24
25 end while
26
27 end while
28 Output:\(BI\),\(T_{sleep}\)
```  
**Algorithm 1**Adaptive Wake-Up Scheduling Algorithm
# A Review of Transformer-Based Approaches for Image Captioning
## 7 Challenges and Future Directions
### Visual Feature Representation  
The bottom-up model of [PERSON] et al. [35] relied significantly on the well-annotated and attribute-rich Visual Genome dataset [58]. Their object detector was based on Faster RCNN [15]. Their model showed the usefulness of enriching the visual features by employing a variety of other object attributes of image regions instead of only using the object names. Since its invention, the bottom-up model has became the de facto image extractor for many of the subsequent models.  
[PERSON] et al. [39] further showed that the visual features really do matter in image captioning systems. Many previous models had focused on the encoder-decoder aspect of captioning while leaving the feature extractor virtually untouched since the invention of the bottom-up attention model. The VinVL model [39] focused on improving the feature extractor by improving on the underlying object detection through building a bigger and better model as well as employing much larger datasets for training.  
We expect that there will be renewed interest and further research in improving the feature extractor used in captioning models. Subsequent to Faster RCNN [15] and the bottom-up approach [35], which have come to constitute a standard approach in object detection, there have been a number of recent developments in object detectors which have yielded improved results.  
The Faster RCNN and its family of object detectors are two-stage object detectors, which enjoy relatively high accuracy. However, they tend to be slower than the one-stage object detectors. Single Shot Detector (SSD) [130] and You Only Look Once (YOLO) [131] are classic one-stage object detectors and when they were introduced, they represented a significant step towards real-time object detection. There have been subsequent versions of YOLO released [132; 133; 134; 135], which have consistently improved on speed and accuracy. YOLOv3 was already as accurate as SSD but three times faster. The most recent version in the YOLO family is YOLOv7 [136] and there is already ongoing work to release YOLOv8 [137].  
Other recent object detectors include [138; 139; 140; 102]. The recent transformer-based detectors, such as DETR [121], have demonstrated improved performance and facilitate the design of end-to-end transformer-based vision models. More research and advancements in these areas will yield greater benefits for the vision part of captioning systems and other computer vision tasks.
## 2 Experimental Protocols
### Thermometric Photography and Raman Spectroscopy  
In the cold room at \(-\)10 \({}^{\circ}\)C, we then ran thermometric photography and Raman spectroscopy to observe the ice and RDP in real time (Figure 3). Building upon the initial ice melting test, we adapted these additional tests to be more detailed. For these tests, we used 0.05 g of crushed RDP (equivalent to one grain of RDP). The RDP was deposited onto an ice surface using a 0.05 mL semi-spherical spoon. The RDP was thus in direct contact with the ice--a contact surface of 28 mm2, approximately the size of one salt grain and ensuring a consistent shape and surface area for analysis.  
To measure the temperature distribution of the grain at the ice-RDP interface, we then used an Optris Pi640 thermal camera (Optris, Berlin, Germany) equipped with a microscopic lens and running Optris PIX Connect software (Rel.1.2.1030.0). This setup included a 640 \(\times\) 480-pixel microbolometer frame plane array detector and a Ge lens, providing a 15\({}^{\circ}\)\(\times\) 11\({}^{\circ}\) FOV for thermal imaging of the RDP on the ice. The camera had a NETD of 40 mK. The camera was positioned approximately 40 cm away from the target and was turned on at least 30 min before any measurements to ensure the system attained a thermal equilibrium. Additionally, a reflective crumpled aluminum foil mirror was placed on the ice surface in the camera's FOV to correct for the radiative environment. Thermal images were captured in video mode at 30 Hz.  
The second instrument was an i-Raman Plus portable spectrometer from BWTek (Plainsboro, NJ, USA), operating with a 785 nm laser at an output power of 340 mW. The Raman signal was collected using a confocal lens Raman probe, allowing for a contactless measurement at a distance of approximately 5 mm. The spectrometer was controlled using BWSpec 4.11 software, which managed the laser power (ranging from 1 to 100% of available power), integration time, and acquisition frequency. A portable spectrometer also permits in situ measurements of the Raman depth profile directly on the runway; this eliminates the need for sample collection and preparation as required for FTIR or DSC methods, thereby speeding up RDP analyses. The instrument provided Raman spectra within the 300-3400 cm\({}^{-1}\) range at a resolution of 4 cm\({}^{-1}\). During the ice melting experiment, the  
Figure 3: Experimental setup for thermometric imaging and Raman spectroscopy.  
integration time was set at 20 s, and each produced spectrum was an average of four spectra. Each spectral measurement lasted 80 s, and Raman spectra were collected every 0.01 s. Raman spectra of the RDP or brine contained characteristic peaks that correspond to specific chemical bonds; peak intensity correlates with the concentration of the analyzed chemical compound. In this study, we focused specifically on the peak at 930 cm\({}^{-1}\).