esa-sceva/satcom-chunk-collection huggingface dataset:
# Server Operation and Virtualization to Save Energy and Cost in Future Sustainable Computing
## 2 Background
### EPC and vEPC (Virtual EPC)
NFV technology involves implementing/operating network functions (e.g., S/P-GW, Content Delivery Network (CDN), Deep Packet Inspection (DPI), etc.) on a dedicated hardware platform. The goal is to speed up and reduce the cost of bringing innovation to the network. In terms of standardization, multiple PoC (Proof-of-Concept) is carried out to virtualize the representative network function in the global mobile network service provider and network equipment vendor in ETSI (European Telecommunication Standards Institute), NFV ISG (Interest Study Group) [15]. In particular, the first network function that many mobile network service providers plan to commercialize is the IMS function and EPC function.
## 2 Results and Discussion
### Synthesis of PSt-\(l\)-PDMS and PSt-\(l\)-PDMS/DVB Polymer Conetworks
These results indicate that the crosslinker concentration provided solely by methacryloxyppropyl-telechelic PDMS (MA-PDMS-MA) is insufficient to achieve higher gel fractions, and soluble fractions with higher polystyrene contents are formed. Addition of DVB increases the crosslinker concentration, and this leads to higher gel fractions. It is noteworthy that the MA-PDMS-MA \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample ID** & \begin{tabular}{c} **\({}^{\textit{H}}\)PDMS,feed** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Gel Fraction** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **\({}^{\textit{H}}\)PDMS,eat** \\ **by Elemental** \\ **Analysis (\%)** \\ \end{tabular} &
\begin{tabular}{c} **\({}^{\textit{H}}\)PDMS,extr** \\ **by Composition** \\ **of Extractables (\%)** \\ \end{tabular} \\ \hline S-4.7-48 & 30 & 55.9 & n. a. & 48.4 \\ S-4.7-59 & 40 & 60.4 & 61.3 & 59.3 \\ S-4.7-67 & 50 & 60.2 & 67.1 & 67.4 \\ S-4.7-76 & 60 & 63.0 & 77.2 & 76.4 \\ S-4.7-84 & 70 & 53.2 & 84.1 & n. a. \\ \hline SD36-4.7-33 & 30 & 88.1 & n. a. & 33.4 \\ SD36-4.7-44 & 40 & 86.3 & n. a. & 44.3 \\ SD36-4.7-55 & 50 & 86.7 & n. a. & 55.4 \\ SD36-4.7-65 & 60 & 87.1 & n. a. & 65.2 \\ SD36-4.7-75 & 70 & 86.8 & n. a. & 75.4 \\ \hline SD5-4.7-32 & 30 & 92.1 & 33.8 & 32.4 \\ SD5-4.7-43 & 40 & 88.9 & 42.3 & 43.4 \\ SD5-4.7-54 & 50 & 87.8 & 52.6 & 53.9 \\ SD5-4.7-63 & 60 & 85.9 & 61.3 & 62.7 \\ SD5-4.7-75 & 70 & 84.1 & 73.6 & 75.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The PDMS contents of the feed, the gel fraction and the composition of the PSt-\(l\)-PDMS and the PSt-\(l\)-PDMS/DVB conetworks, determined by elemental analysis (\(m_{\text{PDMS,EA}}\)) and by the composition of the extractables residues (\(m_{\text{PDMS,extr}}\)) obtained by the \({}^{1}\)H NMR spectra of the extraction residues (see Section 4.2 for sample identification). Figure 1: The transparent PSt-\(l\)-PDMS/DVB conetworks with 36:1 St/DVB weight ratio. macrocrosslinker incorporates into the conetworks at high levels, close to the feed ratios, even when a high DVB/St ratio of 1:5 (\(m/m\)) was used. This means that hypercrosslinked conetworks with predetermined macrocrosslinker contents can be obtained in the presence of additional low molecular weight crosslinkers.
## 1 Introduction
Such results may delay the diagnosis of osteoporosis and the initiation of appropriate treatment. Densitometry testing may also fail to assess bone quality, meaning that early changes in microarchitecture may remain undetected. Additionally, densitometry results can be inaccurate in patients with extreme body weights, both in obese and very thin individuals. Measurements in the spine, despite the high trabecular bone content, may be distorted in older patients with advanced degenerative changes. BMD norms are based on reference populations, which may not account for individual differences, especially in elderly individuals [13; 14]. New studies presenting alternative solutions for diagnosing osteoporosis in various areas of human bone tissue are still appearing in the literature. Analysis of available publications showed that the most common direction of research is the analysis of the image of bone microarchitecture [15]. The article by [16] shows clinical interest in bone texture analysis using a high-resolution X-ray device. These studies show that the combination of BMD values and texture parameters allow for a better assessment of fracture risk than that which can be obtained based on BMD measurement alone. Texture analysis has also been used in hip bone diagnostics, as described in the article [6]. It was shown that the most efficient features are covariance features; they provide a diagnostic error probability of 0.2. Similar conclusions were also presented in articles [17; 18; 19], in which the relationship between BMD and texture parameters was also demonstrated. The authors of article [20] proposed a multifractal method for characterizing the texture of trabecular bone. The effectiveness of this method was also confirmed in studies [21; 22; 23; 24; 25]. Another popular approach in osteoporosis diagnostics is the use of deep neural networks for tissue image classification. In the work by [26], four CNN models were used: AlexNet, VGGNet, ResNet, and DenseNet. Promising results of DNN application were obtained in the case of spine image analysis [27; 28; 29; 30]. The research activities were focused on developing a foundation for creating an additional diagnostic module to analyze the microarchitecture of spongy tissue and detect potential defects, ensuring compatibility with currently used devices and software. The previous publications present different approaches, including texture analysis and the classification of tissue images [31; 32]. This article presents the influence of using a different number of texture features of spongy tissue of the spine on the effectiveness of classification of computed tomography images. Due to the fact that it is in the lumbar spine that osteopororotic changes appear the earliest, which results from the large area of occurrence of trabecular tissue, this paper focuses on the spine area. Statistically, the L1 vertebra is most exposed to osteopororotic fractures [10].
## 1 Introduction  
Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent and aggressive lymphomas [1]. Patient risk stratification is generally assessed through clinical parameters, grouped into the International Prognostic Index (IPI) score [2]. However, disease relapse occurs in approximately 20% of patients within 5 years after first-line therapy [3]. The early identification of patients at higher risk of relapse might allow the optimization of the treatment strategy, possibly improving the outcome after first-line therapy. Molecular imaging and genetic data are emerging as potential predictive biomarkers [4; 5; 6] to improve risk stratification and treatment optimization.  
\({}^{18}\)F fluorodeoxyglucose positron emission tomography (\({}^{18}\)F [FDG] PET) imaging is a standard-of-care imaging procedure for staging DLBCL patients [7]. Quantitative analysis of \({}^{18}\)F[FDG] PET images is conventionally performed by measuring FDG uptake (standardized uptake value [SUV] derivates) and metabolic volumes (metabolic tumor volume [MTV], total lesion glycolysis [TLG]). The role of these conventional PET parameters as prognostic biomarkers for DLBCL patients has been suggested in different clinical settings [8; 9; 10]. In addition, radiomics is an emerging approach [11] that allows the exploration of PET images by exponentially increasing the number of quantitative parameters analyzed. Promising results have been obtained also in the case of different lymphomas, including DLBCL, with a different purpose in relation to the different prognostic role of PET/CT for each lymphoma sub-type [12; 13; 14]. These preliminary studies support the hypothesis that PET radiomic imaging biomarkers might be reliable predictors in the early assessment of DLBCL patients' treatment response, laying the basis for the present investigation [15].  
Radiomic analysis should be robust, reliable, and reproducible [16]; nevertheless, the use of different techniques for image acquisition and analysis affects the comparison and reproducibility of the results obtained among different centers and studies. Noise in the image signal might be observed in the case of a heterogeneous dataset, thus affecting the identification of potential associations with survival outcomes. For this reason, when performing a radiomic analysis on PET images, it is crucial to test the stability of the results by applying different methodologies as an indicator of robustness and reproducibility.  
This methodological study has been designed to investigate the impact of different PET segmentation methods on the extraction of radiomic features (RFs) in a population of DLBCL patients who performed baseline \({}^{18}\)F[FDG] PET/CT prior to first-line therapy. The selection of two different target lesions for radiomics analysis and the subsequent impact on the overall analysis have also been explored.
## 4 Experiments and Analysis
### Denoising Experiments and Analysis of Different Types of Co-Band Noise  
According to the frequency band range of seismic signals, co-frequency band noise was divided into medium-high-frequency noise, low-frequency noise, and mixed-frequency band noise, and the denoising performance of these three kinds of co-band noises was tested using the ARDU model to verify the effectiveness of the model for different noises. The test results are shown in Figures 8-11, where (a), (c), and (e), respectively, represent the time-domain seismic signals with labels, seismic signals with noise, and the denoised seismic signals, while (b), (d), and (f), respectively, represent the time-frequency domain signals of (a), (c), and (e). Figure 8 shows the medium-high-frequency noise. The frequency of the medium-high-frequency noise was relatively high, and the overlapping range with the frequency band of the seismic signals was small. After denoising, the SNR of the signals increased by 12.45 dB, \(r\) increased by 25.18%, and the RMSE reduced by 76.16%. Figure 9 shows the low-frequency noise. The low-frequency noise in the co-band seriously overlapped with the frequency band of the seismic signals, and the noise and seismic signals were poorly separated. Compared to medium-high-frequency noise, denoising is more difficult. After denoising, the SNR of the signal increased by 10.41 dB, \(r\) increased by 30.03%, and the RMSE reduced by 70.68%. Figures 10 and 11 shows the mixed-frequency band noise. The mixed-frequency band noise contained multiple frequency components, and it seriously overlapped with the frequency band of the seismic signals, so it was difficult to reduce the noise. After denoising, the SNR of the signal increased by 9.91 dB, \(r\) increased by 27.34%, and the RMSE reduced by 62.27%. To sum up, the ARDU model can efficiently remove different types of noise, improve the SNR of the signals, protect effective signals, and reduce waveform distortion.  
Figure 7: Improvement of the SNR and correlation coefficient in test set.  
Figure 8: Co-band denoising of medium-high-frequency noise and seismic signals.  
Figure 10: Co-band denoising of band-limited noise and seismic signals.  
Figure 9: Co-band denoising of low-frequency noise and seismic signals.
# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
## 2 System Model
### Signal Model
#### 2.2.2 Downlink Model  
The downlink spectrum \(f_{d}\) is divided into \(M\) sub-bands, each with a bandwidth of \(B_{d}/M\). In the \(m\)-th sub-band \(f_{dm}\) of the downlink, assume there are \(L_{m}\) users performing NOMA transmission. The total transmitted signal from the signal source on \(f_{dm}\) is:  
\[y_{m}^{(d)}(t)=\sum_{j=1}^{L_{m}}h_{j}^{(d)}\sqrt{a_{j}p_{i}^{(d)}}x_{j}^{(d)} (t)A_{j}^{(u)}\big{(}d_{j},f\big{)}+n_{m}^{(d)}(t) \tag{12}\]  
where \(h_{j}^{(d)}\) is the channel gain of user \(U_{j}\) on sub-band \(f_{dm}\), and \(n_{m}^{(d)}(t)\) is the noise on sub-band \(f_{dm}\). The power allocation factor \(a_{j}\) satisfies \(\sum_{j=1}^{L_{d}}a_{j}=1\). The receiver also uses SIC technology to decode user signals sequentially in descending order of power. Assume the signal power of user \(U_{j}\) is \(p_{j}^{(d)}\). The signal of the user with the highest power, \(x_{j1}^{(d)}(t)\), is decoded first:  
\[x_{j_{1}}^{(d)}(t)=y_{m}^{(d)}(t)-\sum_{k=2}^{L_{m}}h_{k}^{(d)}\sqrt{p_{i}^{( d)}}x_{j_{k}}^{(d)}(t)A_{jk}^{(d)}\big{(}d_{jk},f\big{)} \tag{13}\]  
The remaining signals are decoded sequentially. This establishes the full-duplex communication model for both uplink and downlink.  
These models describe the time-varying characteristics and multi-user interference of power line channels in detail and provide a theoretical basis for further optimization. However, the complexity of dynamic channel state and multi-hop relay requires an efficient joint optimization strategy. To this end, this paper proposes a hybrid framework that integrates deep learning and heuristic algorithms, and the specific process is as follows.
# Comparative Real-Time Study of Three Enhanced Control Strategies Applied to Dynamic Process Systems
###### Abstract  
In this study, a comparative analysis of three different control methods for precise, real-time control of a complex dynamic double-tank liquid level process system was performed. Since the system in question has a time-delayed structure, feedforward proportional integral (FF-PI) control and cascaded nonlinear feedforward proportional integral delayed (CNPIR) controllers were tested on the process system. While the FF-PI controller improved the response time of the system, it showed limitations in handling external disturbances and nonlinearities. On the other hand, the CNPIR controller showed better improvements in control accuracy and lower overshoot compared to the FF-PI controller. Since the process system has a nonlinear model and is affected by external disturbances, these two controllers were inadequate in this study when compared to the fractional order adaptive proportional integral derivative sliding mode controller (FO-APIDSMC). The FO-APIDSMC controller provided fairly good performance in both tracking accuracy and disturbance rejection control for non-chattering, fast finite-time convergence, increased robustness, and uncertain dynamic processes. Experimental results reveal that the FO-APIDSMC controller achieves superior minimized tracking error and outperforms the FF-PI and CNPIR controllers by effectively handling uncertainties and external disturbances.  
c 2024 14 9955 10.3390/app14219955
## 2 Materials and Methods
### Veracity Analysis  
Veracity analysis was performed based on the use of measures related to accuracy, uncertainty, reliability, and quality of data [18; 21; 22; 23]. All these measures were only obtained when explicitly reported by the authors'own data. For the used instruments, accuracy was checked in the manufacturer's official documentation or website. Accuracy is understood as the closeness of the agreement between the result of a measurement and the true value of the measurand [24]. Uncertainty and reliability were obtained by absolute and relative consistencies using the standard error of measurement (SEM) [25] (also known as the typical error of measurement (TEM) or typical error (TE) [26], beyond the measures generated from the SEM itself as the minimum difference (MD) or minimum detectable change (MDC)) and the intraclass correlation coefficient (ICC) [25; 26; 27]. The SEM is the measure of the amount of error variance in a set of obtained scores, different from the standard error of estimate (SEE), which is the standard deviation of true scores if the observed score is held constant [28]. Standard uncertainty is the uncertainty of the result of a measurement expressed as a standard deviation [29]. Precision is understood as the closeness of agreement between the results of successive measurements of the same measurand, carried out under the same conditions of measurement. Precision is also called repeatability [24]. Limits of agreement (LOAs) refer to the reference interval for the test-retest differences expected for 95% of the population [30]. The coefficient of variation (CV) refers to the variation around the average, expressed as a percentage; data quality by means of the CV is interpreted by the level of instability [31].  
Some reference values are assumed to better understand the results. SEM results lower than 5% have been suggested [32; 33] and classified as good (SEM = \(<\) 5.0%), moderate (SEM = 5.0-9.9%), or poor (SEM \(\geq\) 10.0%) [34; 35; 36]. However, it is recommended to present the corresponding value with confidence intervals (CIs) [37; 38] because this is a measure of how much the measured test scores are spread around a \"true\" score [37; 38]. Although the SEM may be better than the ICC [25] to evaluate reliability, both are reported in the present manuscript. According to [PERSON] and [PERSON] [39], ICC values \(<\) 0.50 = poor, between 0.50 and 0.75 = moderate, between 0.75 and 0.90 = good, and \(>\)0.90 =indicative of excellent reliability. For the CV, it has been suggested that CV \(>\) 30.0% = large, CV \(<\) 30.0% and \(>\)10.0% = medium, and CV \(<\) 10.0% = small [40]. These reference values were used in the figure to determine the traffic-light systems(i.e., red, amber or green). For accuracy, MDC, SEE, precision, uncertainty and LOAs, no reference values were found in the scientific literature.
# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 5 Discussion and Open Issues
Resource allocation: Due to UAV battery concerns and trajectory constraints, resource allocation is critical in UAV-enabled MEC networks. Resources allocation is involved in three processes: computing task offloading, local computing, and UAV hovering. Designing an appropriate trajectory can ultimately compromise the calculated performance and operating costs. According to the authors of [146], resource allocation may be structured to meet various goals, including computing bit maximization, energy minimization, computational efficiency maximization, cost minimization, completion time minimization, and the consideration of fairness. UAV-enabled IoT for B5G and 6G issues: Data-gathering faces several problems in IoT that extend beyond the fifth-generation (B5G) and upcoming sixth-generation (6G) wireless mobile networks. A large amount of data will be generated by heterogeneous devices, particularly IoT devices and smartphones at the mobile edge; these data are fragmented and dispersed among several machines. Therefore, distributed solutions such as UAV-enabled MEC should be leveraged near mobile users to offload and process the data. Meanwhile, growing privacy concerns make data collecting increasingly challenging. This consumes more energy and decreases the system's offloading time [147].
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 4 NDR-Net Assisted Channel Estimation  
In this section, we first introduce the architecture and workflow of the proposed channel estimation model NDR-Net in Section 4.1. Then introduce the proposed noise level estimation subnet NLE, and explain the selection of network parameters in Section 4.2. Then introduce the selection of experimental data sets in Section 4.3. Finally, in Section 4.4, we analyze the complexity of the proposed algorithm and compared it with other algorithms.
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 7 Experimental Results
### Inspection Task
#### 7.1.2 Task Results  
Results are shown in Figure 7. The top graph compares the speeds \(v_{RB_{1}}\) (dark green line, operator's right hand), \(v_{RB_{2}}\) (light green line, operator's left hand), and \(v_{R}\) (yellow line, robot end effector), while the bottom graph represents the computed distances, which are processed to compute the robot speed scaling factor, \(k\) (black line). The minimum protective separation distances \(S_{RB_{1}}\) (dark blue line) and \(S_{RB_{2}}\) (light blue line) are compared with the respective distances \(d_{RB_{1}}\) (purple line) and \(d_{RB_{2}}\) (violet line) and the minimum allowed distance \(d_{min}\) (red line).  
Figure 6: Snapshots of the inspection task experiment.  
More in detail, during STEP 1, the operator is stationary (\(v_{RB_{1}}\approx v_{RB_{2}}\approx 0\) m/s), so \(S_{RB_{i}}\) only depends on \(v_{R}\): the separation distances \(d_{RB_{1}}\) and \(d_{RB_{2}}\) are much greater than the minimum permitted, so the robot moves at its maximum speed (\(k=1\)).  
STEP 2 starts at about 8 s: the operator walks towards the robot station; thus, the speeds \(v_{RB_{i}}\) increase up to a peak of 1.8 m/s. During this motion, since the direction of motion of the operator is opposite to the direction of motion of the robot, \(S\) increases according to the contribution of \(v_{RB_{i}}\). Also, the human-robot distances \(d_{RB_{i}}\) decrease, until they become very similar: the system supposes a possible risk for the operator, so it slows down the speed of the robot from approximately 9 s to 11.5 s, until the robot stops (\(k=0\)) because \(S_{RB_{2}}>d_{RB_{2}}\). When the operator stops in front of the robot, \(v_{RB_{i}}\approx 0\) m/s and the robot resumes its speed gradually (\(k\) increases).  
STEP 3 highlights that, even if the operator is almost stationary, the robot still slows down slightly when it passes in front of him to prevent possible risky situations (\(t\approx 17\) s).  
During STEP 4, the operator approaches the robot at a distance lower than the minimum allowed (\(t\approx 21\) s, \(d_{RB_{2}}<d_{min}\)), so the robot immediately stops (\(k=0\)). After that, the operator backs away for a few seconds (STEP 5) and the behavior is similar to STEP 3.  
Finally, in STEP 6, at \(t\approx 35\) s, the operator moves away from the robot station. Differently from STEP 2 in which they move at opposite directions that causes a decrease in \(k\), in this case, \(S\) decreases even if \(v_{RB_{i}}\) increases because the human operator directions of motions are the same, so the \(v_{RB_{i}}\) contribution is negative in the \(S_{RB_{i}}\) computation: the collaborative scenario is strongly safe and \(k=1\).
## 3 The Critical Importance of Product Identity in Agricultural Supply Chains
### Conceptual Development  
Blockchain technology serves as a decentralised ledger that can assign and maintain a unique digital identity for products in agricultural supply chains. Each physical item or batch can be linked to a tamper-proof record on the blockchain, often via tags such as QR codes or RFID, creating a \"digital twin\" of the product [37]. This secure product identity enables recording every transaction or transformation the product undergoes (from farm to processing to retail) in an immutable, time-stamped chain of data [31]. The result is an auditable trail that stakeholders can trust for its integrity and transparency: once data are recorded, they cannot be altered without detection, preserving the authenticity of product information [32].  
The importance of secure and verifiable product identities in agriculture cannot be overstated. Food supply chains are highly sensitive to issues of origin, safety, and quality. A reliable digital identity allows producers, regulators, and consumers to verify a product's provenance and attributes (e.g., organic certification, harvest date) with confidence [38]. For instance, a blockchain-based identity can assure a consumer that a package of organic tomatoes indeed came from a certified farm and was handled according to standards because every step is recorded and verified by the network. In essence, blockchain's role in product identity is to serve as the trust infrastructure, often dubbed \"the trust machine\" that underpins next-generation traceability systems, ensuring that each product's history is securely shared and verifiable by all authorised parties [32]. This conceptual foundation is critical for building transparency and accountability in farm-to-fork supply chains.