esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Magnetic Nanofluids on the Performance of a Fin-Tube Heat Exchanger
## 1 Introduction
[11] studied the plate heat exchanger under a magnetic field, while working fluids, hot water, and cold ferrofluid were used. Positions of magnets were set into six cases, and the position showing the best heat transfer performance was selected. The case where resistance loss and an improvement in heat transfer performance can be achieved at the same time is when the magnet is set vertically. It was confirmed that the pressure drop decreased when the magnets were arranged without overlapping. Finally, the heat transfer performance and pressure drop were considered at the same time. It was concluded that the optimal effect can be obtained when the magnets are placed vertically in a non-overlapping state. [PERSON] et al. [12] compared the cooling performance, according to the magnetic flux density in a fin-tube heat exchanger. Five different densities were set as variables. The cooling performance of the tube farthest from the inlet was the lowest among the three tubes. It was also concluded that the change in the flow was greater when the magnetic flux density was strong. The purpose of this study was to improve cooling performance, by using ferrofluid in the fin-tube heat exchanger when there is a permanent magnet outside. The numerical analysis was carried out in the absence of magnetic fields, and this result was set as a reference model. Based on this, three conditions were set. The first is a case in which a magnetic field was applied to the reference model. Nanofluids responded to the magnetic field and began to create flows in the heat exchanger. The second involved a case where permanent magnets were added. The last case involved installation of VGs (vortex generators). The heat transfer coefficient was used as an output parameter to compare the cooling performance of these cases. It is believed that these research results can be used as a means to improve the heat dissipation performance of the system in a gravity-free environment, where external forces cannot be applied.
_Applied_  
_sciences_  
_Article_  
**Beyond 5G Network Architecture Study: Fractal Properties of Access Network**  
**[PERSON] 1,2\({}^{\copyright}\)**, [PERSON] 1,*\({}^{\copyright}\)**, [PERSON] 3, [PERSON] 4,5,*\({}^{\copyright}\)**, [PERSON] 3, [PERSON] 6 and [PERSON] 1  
1 Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications (SPBSUT), 193232 St. Petersburg, Russia; [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (A.K.)  
2 Applied Mathematics & Communications Technology Institute, Peoples' Friendship University of Russia (RUDN University) 6 Miklukho-Maklaya St, 117198 Moscow, Russia  
3 Collage of Computer Science and Engineering, University of Jeddah, Jeddah 21493, Saudi Arabia; [EMAIL_ADDRESS] (O.I.A.); [EMAIL_ADDRESS] (R.A.)  
4 School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China  
5 Department of Computer Science, Faculty of Computers and Information, Menoufia University,  
Shibin el Kom 32511, Egypt  
6 Radio Research and Development Institute (NIIR), Deputy Head of Department, 105064 Moscow, Russia; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (I.A.E.)  
Received: 19 September 2020; Accepted: 12 October 2020; Published: 15 October 2020  
**Abstract:** Wireless networks connect various devices through radio waves in which the network connection may have different structures. Moreover, the network structure is determined based on the placement areas of the network elements, which can be affected by the building and their locations. However, the numerical characteristic which describe the features of the real environment and allow them to be related to the properties of the model are still a challenge that has not been well addressed. To this end, in this paper, we analyze the modeling problems related to the structure of user placement in the access network. Our proposed solution is based on a description of the user environment structure in which cities in the form of buildings and constructions are considered as a typical environment. We propose a new model for addressing the wireless network structure in an efficient manner in which the features of the environment are considered, which are numerically expressed in the form of the Hurst parameter or fractal dimension. In addition, the fractal dimension, geometric fractals, and the characteristics of the user's distribution territory and urban development are efficiently utilized. Then, we analyze the influence of the fractal properties of the environment on the structure of promising communication networks; in particular, on the structure of the Internet of Things network. Finally, simulation results proved that the proposed model is considered as a beneficial solution for modeling mobile communication and wireless access networks, including fifth-generation networks.  
**Keywords:** wireless network; user environment; access network; fractal; modeling; self-similar; Internet of Things
## 2 Background and Related Work
### Maritime Service Portfolio  
The development of e-Navigation Test Beds is not only to solve the problem of data standards and management but also to provide users with navigation safety services. Therefore, IMO has identified 16 MSP [23], as shown in Table 1. MSP is the realization method of providing services to ships through e-Navigation. An MSP is a set of standardized, operational and technical maritime services provided to seafarers onshore in a channel, port or similar area [24]. From this definition, MSP is established by area. At present, the six major regions for the implementation of MSP are ports and adjacent port areas, coastal or restricted areas, high seas and open areas, coastal or infrastructure construction areas, polar areas and other remote areas. The content, accuracy and level of the e-Navigation service provided by different MSP areas will be different.  
The MSP Service Portfolio can guide scholars to study maritime service. [PERSON] et al. proposed a Bayesian ship prediction algorithm based on particle filtering (PF), which was evaluated using real AIS data [25]. [PERSON] et al. proposed a complete ship route visualization method from the original AIS data [26]. [PERSON], [PERSON] et al. proposed a GML-based S-101 ENC data recording encoding method [27]. [PERSON], W.X realized the conversion of the S-57 data structure to the S-101 data structure by analyzing the differences between S-57 and S-101 in the structure and geometry of electronic navigation chart coding [28]. [PERSON] focused on S-102 data organization and spatial schemes, and realized the reading and display of S-102 Bathymetric Attributes Grid (BAG) data based on code programming [29]. [PERSON] et al. proposed a comprehensive framework to assess the capability of maritime Synthetic Aperture Radar (SAR) in the South China Sea [30].
# Phytomicrobiones: A Potential Approach for Sustainable Pesticide Biodegradation
## 3 Phytomicrobiome and Pesticide Biodegradation
### Flower Microbiome  
Among the myriad microbiomes that grace the botanical world, the flower microbiome has remained shrouded in relative obscurity [116; 117]. Despite the lack of scientific limelight, these floral microbial communities perform a pivotal role by beckoning insects for pollination, thus yielding a substantial influence over the plant's reproductive success. A closer look reveals Proteobacteria, Firmicutes, and Bacteroidetes as the dominant phyla that populate the enigmatic world of the flower microbiome. These microbial denizens each contribute uniquely to the intricate tapestry of this ecosystem [118]. Surprisingly, the realm of the flower microbiome remains largely uncharted territory, with a conspicuous absence of research on its structural composition, its potential contributions to pesticide degradation, or its responses to pesticide applications. This gap represents a tantalizing opportunity for advancing environmental sustainability. Noteworthy is the dynamic evolution of the flower microbiome over its lifetime, a phenomenon illuminated by this [119]. In their comprehensive study, they meticulously assessed bacterial diversity in apple flowers from their nascent bud stages to the twilight of senescence. Dissecting the flower into its constituent parts, including stigmas, stamens, receptacles, and petals, they revealed intriguing revelations. Petals emerged as havens of microbial richness and diversity, while stigmas exhibited a relatively fewer number of operational taxonomic units (OTUs). Throughout the flower's lifecycle, Proteobacteria, Deinococcus-Thermus, Saccharomyces, Bacteroidetes, and Firmicutes remained steadfast as dominant phyla. Notably, certain species, including _Pseudomonas_ and Enterobacteriaceae within these phyla, showcased intriguing operational patterns that bolstered the flower's development [119]. An uncharted realm of interplay exists between seed and flower microbiomes. Studies suggest that an augmentation of diversity within the flower microbiome cascades into heightened microbial diversity in the seed microbiome [120]. Yet, an unsettling shroud of ignorance cloaks the subject of pesticide degradation by these floral and seed-associated microbes. The flower microbiome, largely overlooked until now, awaits its moment in the spotlight. Delving into this domain holds the promise of unearthing invaluable insights into the degradation of pesticides and their interplay with these microbial enclaves. In an era marked by growing environmental concerns, this represents a compelling frontier for research, one that may hold the keys to a more sustainable coexistence between agriculture and nature.
## 2 Demand Response Modeling for ACL
### Cost Calculation for the DR of the ACL  
Controlling peak load by directing air conditioner usage behaviors can be on the basis of price or incentives. Most of the current research adopts the DLC method to manage the ACL. The DLC is a type of load control method based on the incentive DR. It refers to using smart terminals to directly manage and control a part of the load usage by the dispatching center under the permission of consumers. In return, the customer can receive some compensation. In this paper, the DLC measure has been introduced to control electricity usage of ACL consumers. The detailed control method has been described in [16]. This section primarily provides the cost calculation of using ACLs to cut down peak load on the basis of the above-mentioned method.  
The advanced metering technology [29] in the smart grid enables bidirectional communication between the utility operator and the end-user. Therefore, power companies can rely on the infrastructures of the smart grid to dynamically direct consumers' electricity usage behaviors. It is assumed that every air conditioner is installed with a smart controller. The smart controller can send information to the power company, and when the smart controllers receive a load reduction signal sent from the power company, they will adjust the ACL consumption to reduce the load. At the same time, the income of the power company will be decreased because of the decreased use of power. In addition, the power company has to provide some compensation to the users. Thus, the cost of using ACLs to cut down peak load is equal to the sum of the DR compensation cost and the lost revenue due to the decrease in the load.  
The revenue loss of the power company, \(\Delta C_{n,t}\), after the load reduction is  
\[\Delta C_{n,t}=\lambda\Big{(}P_{AC,n,t}^{pre}-P_{AC,n,t}^{post}\Big{)}=\lambda P _{AC,n,t} \tag{3}\]  
where \(\lambda\) refers to the electricity price, \(P_{AC,n,t}^{pre}\) to the power of the \(n\)th-type ACL in the DR program before the load reduction at time \(t\), \(P_{AC,n,t}^{post}\) to the the corresponding power after the load reduction at time \(t\), and \(P_{AC,n,t}\) to the load reduction.  
The compensation cost is the customer outage cost because of the load reduction. The compensation cost \(C_{AC,n,t}^{comp}\) can be calculated as a quadratic function of the load reduction [30]:  
\[C_{AC,n,t}^{comp}=K_{1}\cdot P_{AC,n,t}^{2}+K_{2}\cdot P_{AC,n,t} \tag{4}\]  
where \(K_{1}\) and \(K_{2}\) are the cost coefficients; their values are influenced by the load type.  
Thus, the cost \(C_{AC,n,t}\) for the DR of the ACL is  
\[C_{AC,n,t}=C_{AC,n,t}^{comp}+\Delta C_{P,t} \tag{5}\]  
Substituting Equations (3) and (4) into Equation (5), the final cost \(C_{AC,n,t}\) for the DR of the ACL becomes  
\[C_{AC,n,t}=K_{1}\cdot P_{AC,n,t}^{2}+(K_{2}+\lambda)\cdot P_{AC,n,t} \tag{6}\]
# LAMDA Controller Applied to the Trajectory Tracking of an Aerial Manipulator
## 4 Experimental Results
### Experiment 2: Inverse Dynamics Controller  
In this test, the performance of the controller for trajectory tracking is observed. The trajectory described by the aerial manipulator robot and the desired trajectory are presented in Figure 10. We observed that the end effector of the aerial manipulator robot follows the desired trajectory with a margin of error of approximately 0.4 m as verified in Figure 11. Therefore, a better performance of this controller is observed in comparison with the Kinematic controller, despite the presence of a wind of approximately 8.3 km/h.  
Figure 8: Trajectory tracking: Kinematic controller.  
Figure 9: Control errors: Kinematic controller.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 3 Biomarkers in Diabetics with Known HF
### Biomechanical Stress Biomarkers
#### 3.1.2 Cardiac Troponins  
Progression of both T2 DM and HF was independently associated with the development of subclinical myocardial damage, characterized by elevated levels of hs-cTn [73]. Metabolic and oxidative stress can damage cardiac myocyte membranes and may induce a leakage of cytoplasmic troponin through it [74]. Additionally, there is evidence of the occurrence of irreversible cardiac troponin modifications by shaping the complex with AGE [73]. These irreversible troponin and AGE complexes occur during myofilament relaxation and may play a key role in the development of contractility dysfunction [74].  
Although circulating levels of hs-cTn are largely increased in HFpEF/HFrEF patients with T2 DM, when compared to those without T2 DM, hs-cTn yields additional prognostic information for HFrEF and HFpEF patients regardless of a presence of T2 DM [75]. Moreover, hs-cTn predicts adverse outcomes independently from NT-proBNP and thereby can identify T2 DM patients at an extremely high absolute CVD risk [76].  
Importantly, TnI is commonly elevated among T2 DM patients at higher risk of CVD events, incident HF, and stroke [77]. Circulating levels of hs-cTn in T2 DM patients with atherosclerotic CVD were associated with higher hemoglobin A1c levels, severity of hypoglycemia, and markedly predicted subclinical changes in structure and function of the heart, brain, vasculature, and kidney [77; 78]. In a meta-analysis of 28 relevant studies by [PERSON] et al. (2017) [79], it was unveiled that those in the general population with high hs-cTn concentrations within the normal range were associated with an increased risk of CVD. The ARIC (Atherosclerosis Risk in Communities) study has shown that elevated hs-TnI (\(\geq\)3.8 ng/L) in the general population is associated with greater incident CHD (hazard ratio (HR) = 2.20; 95% CI, 1.64-2.95), ischemic stroke (HR = 2.99; 95% CI, 2.01-4.46), atherosclerotic CVD (HR = 2.36; 95% CI, 1.86-3.00), HF hospitalization (HR, 4.20; 95% CI, 3.28-5.37), and all-cause mortality (HR, 1.83; 95% CI, 1.56-2.14) [80]. Overall, T2 DM patients having HF with evidence of subclinical myocardial damage are at the highest risk for premature death, CVD, and HF-related events. However, mild elevated hs-cTn did not improve the predictive ability of NPs, and helps to rule out atheorthrombotic events rather than HF progression in diabetics. However, hs-cTn did not fit to point-of-care therapy of HF and there is no need in continuous monitoring of the concentration in HF outpatients and ambulatory asymptomatic individuals at high risk of HF.
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
## 5 Hardware Implementation
### Hardware Dataflow Actors  
To implement the SDF-AP model in hardware, each SDF-AP actor becomes a block of logic which encapsulates its own state that cannot be shared among other blocks in the network. The block of logic is also known as an IP core (or hardware (HW) block) and it has handshaking communication ports both on the input and output interface. For each HW block to execute, it must obey all the firing rules of an actor as specified by the SDF-AP model. All the HW blocks are expected to be synchronous to a fundamental clock (_clk_) input port and can be reset asynchronously via a _reset_ input port. The input data is received on data-in (_din_) input port when the value of valid-in (_en_) input port is set high. Furthermore, an output data is sent through data-out (_dout_) output port when the value of the valid-out (_vld_) output port is set high to denote a valid output data.
# Stress-Dilatancy Behavior of Alluvial Sands
## 2 Stress Ratio-Plastic Dilatancy Relationship  
The general stress ratio-plastic dilatancy relationship obtained using the FSC [11] is  
\[\eta=Q-AD^{p}, \tag{1}\]  
where  
\[Q=M^{o}-\alpha A^{o}, \tag{2}\]  
\[A=\beta A^{o}, \tag{3}\]  
\[A^{o}=1-M^{o}\big{(}\delta q/\delta p^{\prime}\big{)}, \tag{4}\]  
\[D^{p}=\delta\varepsilon_{v}^{p}/\delta\varepsilon_{q}^{p}. \tag{5}\]  
\(\alpha\) and \(\beta\) are FSC parameters, and \(D^{p}\) is the plastic dilatancy. For drained triaxial compression with \(\sigma_{3}^{\prime}=\) constant, \(\delta q/\delta p^{\prime}=3\)[11] and  
\[M^{o}=M_{c}^{o}=6\ sin\phi^{o}/(3-sin\phi^{o}), \tag{6}\]  
\[A^{o}=A_{c}^{o}=1-\frac{1}{3}M_{c}^{o}, \tag{7}\]  
\[q=\sigma_{1}^{\prime}-\sigma_{3}^{\prime}=\sigma_{a}^{\prime}-\sigma_{r}^{ \prime}, \tag{8}\]  
\[p^{\prime}=\frac{1}{3}\big{(}\sigma_{1}^{\prime}+2\sigma_{3}^{\prime}\big{)}= \frac{1}{3}\big{(}\sigma_{a}^{\prime}+2\sigma_{r}^{\prime}\big{)}, \tag{9}\]  
\[\delta\varepsilon_{v}=\delta\varepsilon_{1}+2\delta\varepsilon_{3}=\delta \varepsilon_{a}+2\delta\varepsilon_{r}, \tag{10}\]  
\[\delta\varepsilon_{q}=\frac{2}{3}(\delta\varepsilon_{1}+\delta\varepsilon_{3} )=\frac{2}{3}(\delta\varepsilon_{a}+2\delta\varepsilon_{r}), \tag{11}\]  
\[\delta\varepsilon_{v}^{p}=\delta\varepsilon_{v}-\delta\varepsilon_{v}^{e}, \tag{12}\]  
\[\delta\varepsilon_{q}^{p}=\delta\varepsilon_{q}-\delta\varepsilon_{q}^{e}, \tag{13}\]  
where  
\[\delta\varepsilon_{v}^{e}=\delta p^{\prime}/K, \tag{14}\]  
\[\delta\varepsilon_{q}^{e}=\delta q/3G. \tag{15}\]  
\(K\) and \(G\) are the elastic bulk and shear moduli. The line representing the purely frictional state is called the frictional state line (FSL) and is defined by Equation (1) with \(\alpha=0\) and \(\beta=1.0\)[11]. The dilatant failure state (DFS) is defined as the state with the minimum value of the plastic dilatancy (\(D_{min}^{p}\)) state [11]. The line representing the DFS is called the dilatant failure state line (DFSL) equivalent to the FSL and is defined by Equation (1) with \(\alpha=\alpha_{f}=0\) and \(\beta=\beta_{f}=1\) for sands without breakage effects [11]. The stress ratio-plastic dilatancy relationship (1) is a form of the energy balance equation [11]. The FSL for sand sheared under conventional drained triaxial compression with low and medium stress levels, without breakage effects, represents the fully frictional states (\(\alpha=0\), \(\beta=1\)). The stress ratio (\(\eta\)) is dependent, but the energy dissipated during shear is not dependent on plastic dilatancy for these states [11]. The FSL is defined only by the critical friction state angle (\(\phi^{o}\)) for conventional drained triaxial compression. This is a very important conclusion from the FSC.
###### Abstract  
Acquisition times and storage requirements have become increasingly important in signal-processing applications, as the sizes of datasets have increased. Hence, compressed sensing (CS) has emerged as an alternative processing technique, as original signals can be reconstructed using fewer data samples collected at frequencies below the Nyquist sampling rate. However, further analysis of CS data in both time and frequency domains requires the reconstruction of the original form of the time-domain data, as traditional signal-processing techniques are designed for uncompressed data. In this paper, we propose a signal-processing framework that extracts spectral properties for frequency-domain analysis directly from under-sampled ultrasound CS data, using an appropriate basis matrix, and efficiently converts this into the envelope of a time-domain signal, avoiding full reconstruction. The technique generates more accurate results than the traditional framework in both time- and frequency-domain analyses, and is simpler and faster in execution than full reconstruction, without any loss of information. Hence, the proposed framework offers a new standard for signal processing using ultrasound CS data, especially for small and portable systems handling large datasets.  
Keywords:compressed sensing (CS); CS reconstruction; Fourier transform; envelope detection; Hilbert transform; analytic signal; ultrasound B-mode image; ultrasound attenuation map +
Footnote †: journal: Applied Sciences
## 6 Application of Cybersecurity Optimizer in Training Multi-Layer Perceptions for Attack Detection
### Real-World Application and Validation  
The CYO was applied in a real-world scenario involving the detection of network anomalies within a corporate network environment. The network was configured with a range of devices, generating diverse traffic patterns. Over the course of several weeks, the CYO was integrated with a Multi-Layer Perceptron (MLP) trained on network traffic data to detect anomalies, including Distributed Denial of Service (DDoS) attacks, malware propagation, and unauthorized access attempts.  
To validate the optimizer, real-world network traffic data was used in conjunction with publicly available datasets, such as NSL-KDD and CICIDS 2017. These datasets contain labeled instances of both normal and malicious traffic, which were employed to train the MLP. During the validation process, the results demonstrated the optimizer's capability to significantly enhance both the training speed and detection accuracy of the MLP, particularly in identifying previously unseen attacks. This performance validation was essential in demonstrating the real-world applicability of the CYO.  
The CYO demonstrated its adaptive capability by dynamically adjusting key parameters during training, leading to a high detection rate with a low false positive rate. The optimizer's performance was compared to traditional optimization methods, such as Differential Evolution (DE) and Particle Swarm Optimization (PSO), and it outperformed these methods in both convergence speed and accuracy during the MLP's training. This validation process highlights the optimizer's potential for practical applications in real-world cybersecurity environments.