esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Enhanced Name Resolution System
### Deployment  
Deployability is important for a real-world NRS. If the NRS system can be deployed from the edges, then the deployment can be simplified [22]. For example, parts of the NRS can be deployed and used without immediately requiring a full deployment.  
The deployment of ENRS can be flexible, since HMs in different levels are decoupled, and the registration and resolution approaches are independent of the cross-layer relationships between HMs. The lowest level of HM can be deployed on the AN level equipment, e.g., RAN, to meet the ultra-low latency requirements. The highest level of HM can be deployed on AS-level equipment, e.g., the cloud data center, to meet the general latency requirements. The intermedia level of HM between AN-level and AS-level can be deployed selectively based on differential low-latency demands.  
In our system, the name resolution service interfaces are exposed to UE by an agent via network attachment, so that the name resolution service is transparent to UE. The service interfaces that the ENRS agent provides are as follows:  
* the interface to the global naming system to get EID for data;
* the partitioning parameters of each level of HEAs the ENRS agent belongs to;
* the HM addresses of each level of HEAs the ENRS agent belongs to.  
The ENRS agent will intercept the requests that are sent to the ENRS and forward them to the proper HM according to different message types and service parameters. The ENRS agent can be deployed in any equipment or device that is connected to the 5G-ICN network.
# Optimal Control Method of Semi-Active Suspension System and Processor-in-the-Loop Verification
## 5 Conclusions  
This paper presents an improvement in ride comfort and road holding simultaneously on a real SASS with MR damper experimental setup. The MR damper model was obtained with ANN, and correlation coefficients of the network for training, test, and validation were calculated as 0.99987%. Before application, the control algorithm was validated by using PIL verification. PID controller coefficients were optimized with PSO. Although the classical controller was used, optimizing the controller coefficients contributed significantly to the results. The optimized controller performance was compared to the passive suspension system performance. The passive mode was obtained by not applying current to the MR damper. A current in the range of 0- 2A was supplied to the MR damper for the semi-active mode. The proposed controller reduced sprung mass displacement by 59.1%, sprung mass acceleration by 63.45%, and suspension deflection by 52.78%, respectively, for the highest bump disturbance. In the analysis of test results, it was observed on a real test setup that all parameters of ride comfort and road holding, which have a trade-off characteristic, were significantly improved simultaneously. This observation forms the inspiration point for this study. The test setup developed to control a real quarter car SASS contributes to the existing literature. Additionally, the utilization of PIL verification for the first time in the validation of a suspension system provides another contribution to the literature. As an important conclusion, the proposed controller can be used effectively on the SASS with the MR damper.  
Conceptualization, T.E.; methodology, M.O.Y.; software, M.O.Y.; validation, M.O.Y. and T.E.; investigation, T.E. and M.O.Y.; writing--review and editing, M.O.Y.and T.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We thank [PERSON], [PERSON], and [PERSON] for their valuable contributions to this study.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Cell Viability  
An MTS assay (CellTiter 96 Aqueous One Solution Cell Proliferation Assay, Promega, Madison, WI, USA) was performed to study the influence of the plasma treatment on the cell's viability and metabolism. Cells were seeded into 96-well plates for 24 h. After that time, the medium was aspirated and replaced by 100 \(\mu\)L of the plasma-treated medium. The cells were incubated for a further 24 h. Then, 20 \(\mu\)L MTS solution was added and after a 2-h incubation, the medium was transferred into a new 96-well plate and the spectrophotometric absorption was analysed by an ELISA reader (Anthos 2010, Anthos Labtec Instruments, Salzburg, Austria) at 490 nm (see Figure 1 for an overview of the experimental setup). Since the sole plasma treatment of the medium also leads to a slight colour change of the MTS solution, a separate blank was carried along for each treatment time and medium. This value was subtracted from the respective cell sample. The calculated absorbance is proportional to the metabolic activity of the cells. The viability of the cells was calculated in relation to that of the cells in the untreated control medium (100%). Each treatment condition was tested in triplicate in at least three individual experiments.  
Figure 1: Overview of the experimental setup. (**a**) gives a summary of the different cell culture conditions, while (**b**) shows a schematic workflow of the experiment (created with BioRender.com accessed on 26 January 2021).
## 5 CNN-SSAE Neural Network  
As shown in Figure 1, the CNN-SSAE neural network proposed in this paper consists of a CNN and an SSAE. First, the spectrogram is taken as input, and the deep features are obtained by the CNN, Second, the deep optimized features are obtained by SSAE dimension reduction. Finally, the recognition results are classified through the softmax layer.
## 3 Review Results
### Technology Acceptance Model and Its Extensions
#### 3.1.2 Extended TAM with System Characteristics and Individual Factors  
Some studies have incorporated system characteristics such as perceived system quality and online course design into the Technology Acceptance Model (TAM). These factors positively impact perceived usefulness and ease of use, enhancing users' attitudes and behavioral intentions towards ChatGPT. High system quality, characterized by reliability, efficiency, and user-friendliness, ensures that users have a seamless and productive experience with ChatGPT. Similarly, well-designed online courses that effectively integrate ChatGPT can significantly improve the perceived value and usability of the technology. By addressing these system characteristics, educators and developers can foster a more positive attitude towards ChatGPT, encouraging greater adoption and sustained use in educational settings [25]. Including system quality and course design aspects highlights the importance of a robust and user-centric technological infrastructure in driving the acceptance and effectiveness of educational technologies.
## 3 Stationary Localization Algorithms
### Centralized Localization Algorithms
#### 3.1.2 Hyperbola-Based Localization Scheme (HLS)  
In [14], the authors proposed a Hyperbola-based Localization Scheme (HLS). Instead of using the commonly adopted circle-based detection and least squares algorithm based location estimation, the proposed scheme utilizes the hyperbola-based approach for localization and a normal distribution for estimation error modelling and calibration. As shown in Figure 2, when any unknown node detect one event, it will report the event to anchor nodes immediately. Anchor nodes \(A_{1}\) and \(A_{2}\) receive the event at time \(t_{1}\) and \(t_{2}\). The difference between \(t_{1}\) and \(t_{2}\) is a constant, in other words, the difference between the corresponding distance \(r_{1}\) and \(r_{2}\) is a constant. Based on the property of hyperbola, the unknown node is localized on a hyperbola \(N_{12}\). Similarly, another hyperbola \(N_{23}\) is plotted in the same figure. More curves can be added when more anchor nodes are involved in localization. The intersection of any two curves gives an estimation position of unknown node. In practical applications, the obtained set of intersection points may not coincide or converge well. In this case, algorithms like least squares can be adopted to further improve localization accuracy.  
Compared with the circle-based approach, HLS is more robust against distance measurement error and can localize more unknown nodes. Since two hyperbolas always intersect with each other with one cross point, while two circles will likely intersect with either two or zero cross point(s); there is very little chance to have only one cross point. Simulation results show that localization accuracy of HLS is better than that of the least squares algorithm. In general, HLS is more suitable for accurate localization in UWSNs. However, in HLS, sensor nodes are required to send long-range signals (around 1,000 meters). Thus, excessive energy is consumed. Furthermore, anchor nodes need to be stationary and hence HLS is not extendable to mobile UWSNs.
# An Intelligent Solution for Automatic Garment Measurement Using Image Recognition Technologies
## 3 Results
### UNet-Based Segmentation  
Figure 4 represents a few garment image segmentation results based on deep learning models described above. From the predicted masks we can see that UNet models pretrained with DeepFashion2 and Carvana dataset have the lowest accuracy compared to other models. In the segmentation results, we can see that the clothing lines in the photos are not preserved and the entire shape of the garment is lost. However, as with all models, the segmentation of the skirt shows very good results due to the bright red colour. This indicates that the high contrast with the environment in the photos is an important factor. The best results in maintaining bright and smooth boundaries of clothing are obtained with UNet models including additional encoding and decoding layers.  
Figure 4: Different Unet models’ segmentation results for selected clothes: skirt, jacket and dress.  
Dice values show that used deep learning models were highly volatile during the training phase and Dice coefficients ranged from 0.02 to 0.979. Average and maximum values of Dice coefficient were calculated estimating the results of five experimental runs (see Table 2). Models that were trained with the Carvana dataset or DeepFashion2 showed no significant improvement in accuracy. The UNet 128 \(\times\) 128 model with a maximum Dice accuracy of 0.979 demonstrated the highest accuracy results obtained through all five runs including augmentation. The average value of the Dice coefficient reaches 0.917 with augmentation and 0.899 without augmentation. However, it can be concluded that in choosing a UNet model, the variation in model depth should be rationally evaluated, as the classical UNet uses much less computational resources than models with additional layers, but compared to UNet 128 \(\times\) 128 its average accuracy according to the Dice value is 0.113 lower including augmentation and 0.047 without augmentation.  
One of the five training processes of all included models during 50 epochs is shown in Figure 5 providing the variation of Dice coefficient value throughout the process. It can be noticed that more stable results are gained using deeper UNet models, observing more significant Dice value variations only until the 15 th epoch, while others had larger fluctuations around 0.2. The classical UNet model achieved an average DICE value of 0.860 without augmentation and 0.800 with augmentation. However, it has only stabilized in the last four epochs of the training process (see Figure 5). DICE values were calculated by estimating epochs from 10 to 50 and excluding the \"warming period\" of the first 10 epochs.
###### Abstract  
Non-orthogonal multiple access (NOMA) simultaneously provides multi-user access over the same frequency band or time period, which can significantly improve system throughput in visible light communication (VLC) networks. However, the different interference components of NOMA and the diversified user requirements in VLC are difficult to coordinate. To guarantee both throughput enhancement and quality of service (QoS) satisfaction, this paper presents a power allocation scheme based on dynamic user priority in indoor NOMA-VLC networks. We introduce fuzzy logic (FL) to flexibly analyse user priority and assign signal power using multi-dimensional user features. The experimental results show that our method achieves the best performance in terms of user fairness and satisfaction. In indoor VLC, which has small cells with a high user density, our method outperforms contrastive schemes in terms of the average user data rate (AUDR). Therefore, in comparisons between static and dynamic user priority, our study indicates the fairness advantages of FL for the dynamic evaluation of user priority. In comparisons between NOMA and orthogonal multiple access, our study reflects the AUDR advantages of non-orthogonal methods. Furthermore, in comparisons of user satisfaction, our method outperforms existing methods, indicating the achievement of the QoS guarantee.  
optical wireless communication; visible light communication network; non-orthogonal multiple access; power allocation; quality of service guarantee; dynamic user priority; fuzzy logic +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Environment Characteristics and Their Impact on Divergence  
The consistent patterns of divergence and stability observed across the eight Atari games can be primarily attributed to their intrinsic mechanics and objectives. By categorizing the environments, we can provide more targeted guidance for future DRL applications.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & Alien \#1 & Alien \#2 & Alien \#3 & Alien \#4 & Alien \#5 \\ \hline Alien \#1 & 0.000 (0.000) & 2.588 (2.449) & 3.184 (2.705) & 2.074 (2.055) & 3.578 (3.203) \\ Alien \#2 & 3.646 (3.635) & 0.000 (0.000) & 3.365 (3.147) & 2.009 (1.851) & 2.290 (2.735) \\ Alien \#3 & 3.841 (4.202) & 3.185 (2.868) & 0.000 (0.000) & 3.947 (3.600) & 3.666 (3.621) \\ Alien \#4 & 2.905 (3.664) & 1.577 (1.444) & 3.203 (2.997) & 0.000 (0.000) & 2.232 (2.240) \\ Alien \#5 & 3.798 (4.947) & 1.728 (1.823) & 3.778 (3.648) & 3.017 (2.609) & 0.000 (0.000) \\ \hline  & Boxing \#1 & Boxing \#2 & Boxing \#3 & Boxing \#4 & Boxing \#5 \\ \hline Boxing \#1 & 0.000 (0.000) & 7.752 (5.730) & 10.043 (6.436) & 8.779 (5.299) & 8.894 (5.835) \\ Boxing \#2 & 6.039 (4.752) & 0.000 (0.000) & 7.312 (6.168) & 5.546 (3.896) & 6.794 (5.672) \\ Boxing \#3 & 3.554 (4.251) & 5.966 (5.001) & 0.000 (0.000) & 5.373 (3.457) & 3.883 (5.556) \\ Boxing \#4 & 5.014 (4.029) & 3.505 (2.186) & 7.819 (6.909) & 0.000 (0.000) & 10.088 (8.279) \\ Boxing \#5 & 4.304 (4.144) & 6.278 (5.051) & 4.308 (5.952) & 4.978 (2.780) & 0.000 (0.000) \\ \hline  & Freeway \#1 & Freeway \#2 & Freeway \#3 & Freeway \#4 & Freeway \#5 \\ \hline Freeway \#1 & 0.000 (0.000) & 0.010 (0.000) & 0.001 (0.000) & 0.000 (0.000) & 0.148 (0.006) \\ Freeway \#2 & 0.005 (0.000) & 0.000 (0.000) & 0.002 (0.000) & 0.004 (0.000) & 0.014 (0.001) \\ Freeway \#3 & 0.001 (0.001) & 0.004 (0.000) & 0.000 (0.000) & 0.001 (0.000) & 0.086 (0.003) \\ Freeway \#4 & 0.000 (0.000) & 0.009 (0.000) & 0.001 (0.000) & 0.000 (0.000) & 0.139 (0.005) \\ Freeway \#5 & 0.007 (0.000) & 0.001 (0.000) & 0.004 (0.000) & 0.007 (0.000) & 0.000 (0.000) \\ \hline  & Qbert \#1 & Qbert \#2 & Qbert \#3 & Qbert \#4 & Qbert \#5 \\ \hline Qbert \#1 & 0.000 (0.000) & 0.643 (1.246) & 1.834 (3.214) & 0.868 (1.672) & 0.542 (1.135) \\ Qbert \#2 & 1.124 (2.864) & 0.000 (0.000) & 1.687 (2.995) & 0.492 (0.626) & 0.472 (0.528) \\ Qbert \#3 & 1.913 (4.283) & 1.331 (2.403) & 0.000 (0.000) & 1.230 (2.116) & 1.176 (2.075) \\ Qbert \#4 & 1.016 (1.841) & 0.441 (0.436) & 1.276 (2.430) & 0.000 (0.000) & 0.376 (0.582) \\ Qbert \#5 & 1.452 (2.305) & 0.608 (0.704) & 1.498 (2.852) & 0.359 (0.546) & 0.000 (0.000) \\ \hline \hline \end{tabular}
\end{table}
Table 17: Average and standard deviation of KLD for agents with identically initialized networks (actor network cross-evaluation).
# Preparation and Tribological Behaviors
## 2 Materials and Methods
### Tribological Behaviors  
Tribological behaviors of composites were investigated on the commercial tribological tester (HSR-2M, Zhongke Kaihua Technology Development Co., Ltd., China) in a pin-on-flat configuration under reciprocation motion. The friction face of the composite was held in continuous sliding contact with the steel counterpart. The schematic diagram is shown in Figure 1. The upper specimen, i.e., the pin, was a 45 steel cylinder (a diameter of 3 mm, a length of 12 mm, and a roughness of \(R_{\text{a}}=0.4\) um). The lower specimen, i.e., the flat, was a composites cuboid (a length of 20 mm, a width of 15 mm, a thickness of 10 mm, and a roughness of \(R_{\text{a}}=0.6\) um). All tribological experiments had a duration of 20 min under constant normal load of 150 N, reciprocation frequency of 10 Hz, and stroke of 3 mm. They were conducted under conditions of dry sliding and room temperature. The normal load and friction force were measured by the sensors simultaneously during the tribological experiment. Then, the friction coefficients were calculated by expression \(\mu=F/P\), where \(\mu\) is the friction coefficients, \(F\) is the friction force (N), and \(P\) is the normal load (N). The wear volume of composites was obtained by the measuring system of HSR-2M tribometer. Before each experiment, all steel pins and composites were ultrasonically cleaned with deionized water for 20 min at room temperature, to ensure the surface's cleanliness. The wear rate \(K\) was calculated by expression \(K=\Delta V/PS\), where \(\Delta V\) is the volume loss (mm\({}^{3}\)), \(P\) is the normal load (N), and \(S\) is the sliding distance (m). The experiments of each composite were carried out at in least three parallel experiments, to ensure a relevant statistical evaluation. The average value of friction coefficient and wear rate is presented in this work, with corresponding standard deviations. The worn surfaces of composites were inspected with SEM. Their three-dimensional morphologies were observed by a three-dimensional ultrafine optical microscope (Leica DVM6, Germany). Before examination, the samples were ultrasonically cleaned in the deionized water bath for 20 min, and then plated with a thin gold layer.
## 2 Materials and Methods
### Participants
#### 2.1.2 Exclusion Criteria  
* Noncardiac causes of heart failure symptoms:
* Significant valvular or coronary artery disease.
* Uncontrolled hypertension or arrhythmias.
* Primary cardiomyopathies.
* Significant pulmonary disease (forced expiratory volume in the first second [FEV1] <50% predicted, Global Initiative for Chronic Obstructive Lung Disease [GOLD] III-IV).
* Exercise disability or conditions that may interfere with exercise intervention.
* Myocardial infarction within the last 3 months.
Signs of ischemia during the maximal cardiopulmonary stress test. - Comorbidities (renal failure, cancer, and cognitive impairment) may influence patient prognosis for 1 year. - Participation in another clinical trial. To determine eligibility for inclusion or exclusion, a comprehensive echocardiographic evaluation was conducted, integrating all two-dimensional and Doppler measurements. Additionally, clinical evaluations, exercise capacity assessments, and patients' medical histories were taken into account, and spirometry tests were administered to exclude the presence of significant pulmonary conditions.
# AHC: Advanced Network-Hiding Access Control Framework
## 4 Methods
### Comparison of Advantages and Disadvantages of Different Methods  
Port Knocking, SPA, and AHAC represent three distinct approaches to network authentication and security. Port Knocking, typically implemented using Python and Bash, relies on pre-shared port sequences for authentication. It is simple and lightweight, making it suitable for basic access control and low-to-medium load scenarios. However, its security and extensibility are limited due to the lack of encryption and reliance on the secrecy of knocking sequences.  
SPA, on the other hand, uses strong encryption algorithms such as RSA and AES to enhance security. Implemented in C and C++, SPA involves more complexity than Port Knocking, integrating encrypted packets and firewall configurations. It offers better performance due to single-packet interactions but still faces potential issues such as IP amplification. SPA is generally compatible with various systems and is suitable for high-security scenarios like remote server access and secure gateway authentication.  
AHAC provides the most advanced and robust solution, combining the Noise Protocol with key pairs and the ECDH algorithm for mutual authentication. It supports multiple programming languages, including C/C++, Python, Java, and Go, and offers an advanced, scalable architecture. AHAC enhances device verification, defends against replay attacks, and addresses IP amplification issues. Its optimized performance, high availability, and high extensibility make it ideal for environments requiring strong authentication and encryption, such as enterprise IAM systems and secure resource access. AHAC's compatibility with multiple platforms and future cryptographic developments further underscores its superiority over traditional methods.  
To better understand the differences and advantages of various access control technologies like Port Knocking, SPA, and AHAC, we compare the dimensions of development language, communication, architecture, authentication, encryption, performance, network stealth, availability, extensibility, compatibility, security, and use cases, as shown in Table 1.  
This shows that the AHAC framework performs much better than the other two technologies.
# Architectural Patterns for Blockchain Systems and Application Design
## 4 Pattern Description
### Transactional View Patterns
#### 4.3.3 Chain-of-Blocks Pattern  
**Summary:** This pattern addresses concerns regarding structuring on-chain data.  
**Context:** A software application utilizes blockchain services as a distributed ledger.  
**Solution:** Confirmed transactions in the blockchain are grouped into blocks. Each block has a unique number, known as the block height. A block typically consists of two parts common to all blockchains: a block header and block data. The block header contains, among other information, a link to its preceding block, which allows the blocks to be chained in a strict order. Such a link is typically a hash of the preceding block. A block hash is cryptographically derived from the block's data. The block data contain a list of confirmed transactions. Thus, a block can be searched for by its block number or hash. Additional parts are customized per blockchain platform for the block structure. Usually, these data are not included in the block hash calculation. The time interval, or block interval, is the time allowed to add a new block to the chain. This time differs between blockchain systems based on the blockchain protocol specifications. For example, Steem produces blocks every three seconds. Figure 9 illustrates this pattern.  
**Known uses:** Ethereum is chain-based, and its block structure consists of a block header, block data, and a list of ommers. The blocks are connected to a single chain. Hyperledger Fabric is chain-based, and its block structure consists of a block header, data, and metadata. The blocks are connected to a restricted shared chain. Klayt is chain-based, and its block  
Figure 8: The replicated repository pattern for blockchain applications.  
Figure 9: The chain-of-blocks pattern for blockchain applications.  
structure consists of a block header, block data, and a list of uncles. The blocks are connected to a single chain.  
**Related patterns:** The chain of blocks in the blockchain is synchronized between the network participants as a replicated repository pattern. When more than one block has the same block height, i.e., they are mined at nearly the same time, there could be a chain fork pattern.