esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality of Service Based NOMA Group D2D Communications
## 2 System Model  
Consider inband D2D communications with an overlay cellular network. We consider a frequency reuse factor of one among D2D users to achieve better spectrum efficiency. With this setting, every D2D transmission by a DT is subjected to interference from other active DTs. We consider a composite fading and path loss channel model between every DT and DR. In this work, we assume that the power fading coefficients are independent and identically distributed (i.i.d) with exponential distribution of unit mean, and adopt a path loss model of \(d^{-\alpha}\), where \(d\) is the distance between the probe DR and test DT, and \(\alpha\) is the path loss exponent.
# Transmissive Polarizer Metasurfaces: From Microwave to Optical Regimes
## 2 Why Polarization Conversion Is Required  
Amplitude and polarization direction can characterize any electromagnetic wave. This polarization direction represents the direction of oscillations an electromagnetic wave carries while it passes through the medium. The polarization state of an electromagnetic can control electromagnetic waves in many ways, e.g., polarization rotation and polarization conversion, often termed polarization transformation. The term 'polarization conversion' means the conversion of one form of polarization into another (linear polarization (LP) to circular polarization (CP)). Polarization converters have enormous applications, depending upon the operating region they are being used. They are used from microwave to optical regions, depending upon the applications that need CP.  
In microwave and millimeter-wave communications, the polarization selection of the antenna highly depends on the application and medium. In such a case, polarization mismatch, Faraday's rotation by the ionosphere, and multipath fading significantly disturb and degrade the performance of the channel. Therefore, CP waves are mostly used for command guidance, global navigation systems (GNS), and satellite communication systems because of their better performance than LP waves for their lower sensitivity toward the multipath fading and Faraday's rotation. CP waves can be generated or transformed using a polarization conversion phenomenon. The former has many disadvantages, e.g., the CP horn antenna is cascaded with a polarizer, such as a screw circular polarizer [15; 16], a built-in dielectric-based polarizer which makes these antennas costly and complicated. Moreover, CP antennas, once designed and fabricated, can only perform as CP antennas. On the contrary, CP waves obtained from the conversion phenomenon allow antennas to perform as LP antennas as well. Moreover, the LP antennas are simpler to design.  
The terahertz (THz) band, lying between 300 GHz to 3 THz of the electromagnetic spectrum, has been investigated in numerous applications, such as bio-imaging, spectroscopy, environmental surveillance, biochemical, and pharmaceutical sciences [17; 18; 19]. Circular polarized THz waves are used in biomedical imagining due to the different chirality of biomolecules for CP THz beams. Similarly, they are used in biosensing and drug delivery [17; 20]. Therefore, LP-to-CP converters in THz technology, which are termed quarter-wave plates (QWPs), are very important for THz systems and advancements in their performance can have a significant impact on existing THz systems' performance.
## 2 Methodology
### Influence Factors of the Sediment Flushing  
Sediment flushing efficiency of reservoir \(\lambda\) shown in Equation (1) is chosen as the indicator of the flushed sediment:  
\[\lambda=\frac{S_{out}}{S_{in}}, \tag{1}\]  
where \(\lambda\) is the reservoir sediment flushing efficiency; \(S_{out}\) is the flushed sediment amount out of the reservoir; and \(S_{in}\) is the sediment inflow into the reservoir.  
Referring to the 1D unsteady flow and sediment mathematical model, the major factors affecting sediment flushing efficiency are selected. The model includes: flow continuity equation as shown in Equation (2), flow motion equation as shown in Equation (3), sediment continuity equation as shown in Equation (4), and the riverbed deformation equation as shown in Equation (5):  
\[\frac{\partial Q}{\partial x}+\frac{\partial A}{\partial t}=0, \tag{2}\]  
\[\frac{\partial Q}{\partial t}+\frac{\partial}{\partial t}\left(\frac{Q^{2}}{A }\right)+gA\frac{\partial H}{\partial x}+g\frac{n^{2}Q\left|Q\right|}{AR^{ \frac{3}{3}}}=0, \tag{3}\]  
\[\frac{\partial AS}{\partial t}+\frac{\partial QS}{\partial x}=-\alpha Bq \left(S-S_{*}\right), \tag{4}\]  
\[\gamma_{s}\frac{\partial A_{s}}{\partial t}=\alpha\omega B\left(S-S_{*}\right), \tag{5}\]  
where  
\[H=L-T, \tag{6}\]  
\[\omega=\sqrt{\left(13.95-\frac{\
u}{d}\right)^{2}+1.09\frac{\gamma_{s}}{ \gamma}gd}-1.95\frac{\
u}{d}, \tag{7}\]  
\[S_{*}=\kappa\left(\frac{U^{3}}{gR\omega}\right)^{l}, \tag{8}\]  
and \(\gamma\), \(\gamma_{s}\) are the volume-weight of water and sediment, respectively; \(d\) is the sediment grain size; \(A\) is the cross-sectional area; \(R\) is the hydraulic radius; \(Q\) is the flow; \(B\) is the cross-sectional width; \(H\) is the water head between upstream and downstream; \(L\) is the reservoir water level; \(T\) is the tailwater elevation; \(\alpha\) is the coefficient of saturation recovery; \(\omega\) is the sediment settling velocity; \(S\) is the sediment concentration; \(S_{*}\) is the sediment-carrying capacity of flow; \(\kappa\), \(l\) are coefficient and exponent; and \(\
u\) is the kinematic coefficient of viscosity.  
The sediment inflow into a reservoir differs from month to month. A majority of the sediment comes in flood season with flood, and, in non-flood season, the incoming sediment is far less. To improve the simulative accuracy, the sediment delivery characteristics are studied separately for different time periods in a year. Thus, the parameters regarding the water and sediment properties, such as volume-weight, sediment grain size, and kinematic coefficient of viscosity, are considered to be changeless. For each period, the reservoir operation water level is relatively fixed by the operation rules. Thus, the geometric parameters in Equations (2)-(7), including cross-sectional area, width, and hydraulic radius can also be regarded as constants.  
On the basis of the analysis above, it can be seen that the major factors that impact the sediment flushing efficiency of reservoirs include: inflow, sediment inflow, release, and water head.
## 3 Statistical Methods
### Goodness-of-Fit Test  
In this study, the Anderson-Darling method, which was introduced in Section 3.1.2, was used to study the effectives of different statistical distribution models for modeling the AC breakdown performance. The selection of the statistical distribution for analyzing the AC-BDS of the epoxy/Al\({}_{2}\)O\({}_{3}\) micro-composites was based on References [23; 24]. According to [23], distributions such as Weibull, Gumbel, and log-normal are used in the statistical analysis of failure data for insulating materials. Furthermore, [24] stated that log-normal, exponential, and gamma distributions are used in high-voltage engineering. Accordingly, this paper analyzes the AC-BDS of the epoxy/Al\({}_{2}\)O\({}_{3}\) micro-composites using normal, Weibull, log-normal, exponential, and gamma distributions. At this stage, the best suitable distribution was selected by comparing the Anderson-Darling test statistic (A-D value) with the \(p\)-value. Meanwhile, the characteristics of the normal distribution and log-normal distribution are similar. Hence, please note that the normal distribution was excluded from the goodness-of-fit test. The statistical distributions used for the goodness-of-fit test are briefly introduced as follows.
# Metamaterial Design with Nested-CNN and Prediction Improvement with Imputation
###### Abstract  
Metamaterials, which are not found in nature, are used to increase the performance of antennas with their extraordinary electromagnetic properties. Since metamaterials provide unique advantages, performance improvements have been made with many optimization algorithms. Objective: The article aimed to develop a deep learning model that, unlike traditional optimization algorithms, takes the desired reflection coefficients' parameter as an input and gives the image of the corresponding metamaterial. Method: An amount of 29,722 metamaterial images and reflection coefficients corresponding to the metamaterials were collected. Nested-CNN, designed for this task, consisted of Model-1 and Model-2. Model-1 was designed to generate the shape of metamaterial with a reflection coefficient as the input. Model-2 was designed to detect the reflection coefficient of a given image of metamaterial input. Created by using Model-2 in Model-1's loss function, the nested-CNN was updated by comparing the reflection coefficient of the produced image with the desired reflection coefficient. Secondly, imputation, which is usually the complete missing data before the process of training in machine learning algorithms, was proposed to use in the prediction side to improve the performance of the nested-CNN. The imputation for prediction was used for the non-interested part of the reflection coefficient to decrease the error of the interested region of the reflection coefficient. In the experiment, 27,222 data were used for the KNN-imputer, half of the reflection coefficient was considered as the non-interested region. Additionally, 40 neighbors and 50 neighbors were given the best mean absolute errors (MAE) for specified conditions. Result: The given results are based on test data. For Model-2, the MAE was 0.27, the R2 score was 0.96, and the mean correlation coefficient was 0.93. The R2 score for the nested-CNN was 0.9, the MAE of nested-CNN was 0.42, and the MAE of nested-CNN with 50 neighbors was 0.17.  
metamaterial; metasurface; frequency selective surfaces; convolutional neural networks; deep learning Article
# Recent Advances in Ultrafast Structural Techniques
## 3 Structural Techniques Involving Ultrashort Electron Pulses
### Transverse Electron Beam Properties: Transverse Emittance and Coherence Length
Equivalently, one could introduce an effective longitudinal coherence length \(L_{\mathrm{I,eff}}\ \approx\ \frac{L_{\mathrm{I}}}{\theta}\ \gg L_{\mathrm{x}}\) by noting that their effects on the diffraction peak widths are weighted by their relative projections on the screen. Hence, the spatial resolution in FED experiments is limited by \(L_{\mathrm{x}}\), which is about 3 nm for conventional electron sources. In the case of synchrotron and XFEL sources, the transverse coherence, given by Equation (8), is in the order of several \(\upmu\)m and thus, \(L_{\mathrm{I}}\) governs the spatial resolution. Considering typical XFEL beam parameters \(\lambda\ \approx\ 0.1\) nm and bandwidth \(\approx\)10\({}^{-3}\), we obtain \(L_{\mathrm{I}}\ \approx\ 50\) nm. This suffices to spatially resolve systems with relatively large unit cells or large molecules such as proteins. Moreover, if necessary, the bandwidth can be reduced through the use of a monochromator in order to increase \(L_{\mathrm{I}}\). This is perhaps the main advantage of XFELs over FED instruments, which have not yet succeeded in monitoring protein dynamics. On the other hand, the scattering cross section for electrons is approximately 10\({}^{5}\)-10\({}^{6}\) higher than X-rays for the same energy and are about 1000-fold less damaging [209; 210; 211]. These facts make electrons a better probe of matter at the nanoscale.
## 3 Results and Discussion
### Heavy Metals Concentrations
For As, no limits have been set, but the US Environmental Protection Agency has considered an upper limit of 1.3 mg/kg of wet weight [38]. There is suggestive evidence from in vivo studies of its estrogenic activity, but we have also to consider that the proportion of inorganic As in fish is very low, below 1%, and the presence of organic As in fish poses less health problems than inorganic arsenic. Cd is well recognized as an ED, and its interactions with estrogen receptors are the best characterized of all of the heavy metals, and it is implicated in the development of several types of cancer. The Cd concentrations found in the trout in this study were very low and would not cause concern. The same situation occurred for Pb levels. In the United States, as well in European countries, due to legislative restrictions, the large reduction in Pb emissions from transportation sources have resulted in a reduction of Pb levels in terrestrial and aquatic ecosystems. The data reported in the present study support the trend of decreasing Pb levels in aquatic organisms. Among the essential metals, Co and Ni were present in all of the trout samples, but at very low concentrations. Both metals are considered essential trace elements for animals, but exposure to excess levels of Ni and Co may result in adverse effects, causing neurotoxicity, hepatotoxicity, nephrotoxicity, gene toxicity, reproductive toxicity, and increased risk of cancer [39; 40]. Cr, which is considered an essential trace element, was also detected in all of the samples at low concentrations, but can also show aquatic toxicity depending on the oxidation state. Two forms of Cr may be present in the water environment; the trivalent which is a more stable state in oxidation, and hexavalent, which is more toxic and can induce alterations at biochemical, histologic, genetic, and immunologic levels [41]. Hg does not play any metabolic function but can be harmful for humans, even at low concentrations, when ingested over a long time period. The analyzed samples for trout in this study showed lower concentrations compared with the minimum values reported in the same species [42] in Norway, and ten times lower than the maximum levels established by the European Commission, indicating that Hg can be considered to be not harmful in the rivers considered in this study.
###### Abstract  
In this paper, for the sake of enhancing the security of wireless transmission, we proposed a novel system based on spatial and direction modulation (SDM) combined with generalized spatial modulation (GSM) which is aided by covert information mapping (CIM), termed as the CIM-GSDM system. In such a system, the legitimated user is equipped with distributed receivers so as to demodulate the conveyed signal by exploiting its indices while disturbing eavesdroppers for information security. More specifically, part of the information is modulated into the indices of the legitimated distributed receiver subsets with the aid of the mapped covert information and the interference matrix, while another part of the message is arranged by conventional amplitude-phase modulation. The proposed system can reap the benefits from both GSM and CIM to make eavesdropper suffer great mixture. Furthermore, the detection scheme and theoretical analysis of error performance are discussed as well. The simulation results exhibit that the bit error rate (BER) performance of legitimate user is much better than that of the eavesdropper while the proposed scheme improves the security compared to the original CIM-SDM system at the same spectral efficiency.  
spatial and directional modulation (SDM); generalized spatial modulation (GSM); covert information mapping (CIM); receiver subset selection (RRS); eavesdropping +
Footnote †: journal: _Sensors_**2024**, 24, 6333. [[https://doi.org/10.3390/s24196333](https://doi.org/10.3390/s24196333)]([https://doi.org/10.3390/s24196333](https://doi.org/10.3390/s24196333))
## 2 Materials and Methods
### TCP/IP Socket  
Socket programming, based on the TCP/IP protocol, serves as a foundational element in network communication. It encompasses two prevalent architectural patterns: the client-server architecture and the peer-to-peer architecture. This study focuses specifically on the client-server architecture, characterized by a hierarchical model where multiple clients establish connections with a central server to request services or retrieve information. At the core of socket programming lies the concept of a socket, which serves as an abstract representation of a network endpoint [56].  
These functions encompass various essential aspects of socket communication, including (1) socket creation, (2) binding to specific addresses, (3) listening for incoming connections, (4) connecting to specific addresses, (5) accepting connections, (6) transmitting data, (7) receiving data, (8) closing connections, and (9) handling encountered errors. This robust set of functions provides the necessary capabilities for efficient and reliable socket-based communication.  
Our objective of synchronizing the initiation of correct acceleration among all end nodes necessitates addressing the limitations of plain socket-based communication. While sockets lack native support for simultaneously broadcasting messages to multiple clients, it is feasible to implement a broadcasting mechanism by introducing additional functionality using plain sockets. This can be achieved by maintaining a list of connected clients on the server-side and iterative sending'start' message to each client individually. By adopting this approach, the server can effectively disseminate messages to multiple clients, simulating a mimic broadcasting behavior within the socket-based communication system.
# A Petri Net Model for Cognitive Radio Internet of Things Networks Exploiting GSM Bands +
## 3 Characterization of the Cognitive Environment
### Characterization of the GSM Primary User
The time slots can be dynamically assigned to each service according to the needs of the cell. Thanks to the introduction of the 8-PSK modulation scheme, EDGE transceivers permit the transmission at a rate of 69.2 kb/s in a single time slot. The time slot format for EDGE (Figure 2) is very similar to that of GSM; specifically, the time length is the same, and it carries 8-PSK symbols instead of GMSK ones. Figure 3 shows some examples of time slots allocation in GSM and EDGE transceivers. Figure 1: GSM frame structure. Figure 2: Time slot format for EDGE.
# Measurements of Operating Parameters of a Metal Hydride Compressor with a Heat Pump
###### Abstract  
The hydrogen compression that occurs in a metal hydride compressor is based on the chemical and thermal processes of hydrogen absorption and desorption into an intermetallic structure of metals. The concept presented in this article is centred on the use of an optimal metal alloy that is capable of absorbing hydrogen into its structure at a low temperature and pressure. After such an alloy is heated up, its pressure will significantly increase, and this facilitates the compression of hydrogen without it being in direct contact with the movable parts of the compressor. As a result, the safety of the compression process is increased. The hydrogen compressor contains a pair of tandem metal hydride (MH) tanks with an integrated heat exchanger. The tanks are alternately heated and cooled, so that while hydrogen is absorbed in one of the tanks it is concurrently desorbed in the other tank. The unique nature of the prototype single-stage metal hydride compressor consists in using a heat pump that facilitated a significant reduction of the consumed electric energy in the heat transport between the tandem-arranged tanks. The purpose of this investigation was to examine the possibility of integrating a heat pump as a source of heat and cold for the process of absorption- and desorption-based hydrogen compression. With the prototype MH compressor presented in this article, a compression ratio of \(\epsilon=2.0\) was achieved while the tanks were operated at temperatures ranging from 12 to 55 \({}^{\circ}\)C. In a single cycle, the compressor worked with 166 litres of hydrogen, which was absorbed into 3.125 kg of the \(\text{La}_{0.85}\text{Ce}_{0.15}\text{Ni}_{5}\) alloy. The average heating power that was achieved on the side of the condenser that heated the MH1 tank was 362 W, and the cooling power that was achieved on the evaporator side was 300 W. The achieved value of the heat pump COP was 2.92.  
hydrogen; absorption; metal hydride compressor; measurement; heat exchange +
Footnote †: journal: 16 February 2022  
Accepted: 23 March 2022  
Published: 24 March 2022
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 1 Introduction
### Organization  
The rest of this paper is organized as follows. In Section 2, the system model is detailed. In Section 3, the clustering of nodes in nOSS is discussed and the performance metrics in terms of capacity, spectral efficiency, and energy efficiency are derived. In Section 4, the algorithm of the proposed spectrum sharing technique is presented and an optimal number of ABSs is deduced. Default parameters and assumptions are discussed in Section 5 and the performance Result and comparison of the proposed techniques are carried out in Section 6. The paper is concluded in Section 7.
# Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning
## 1 Introduction
Construction of a multi-scale bearing fault diagnosis model. This study constructs a multi-scale bearing fault diagnosis model based on transfer learning. Through transfer learning strategies, this model effectively utilizes knowledge gained from related but  
Figure 1: Fault type matching relationship between source and target domains. (**a**) represents identical fault categories in both source and target domains; (**b**) shows the target domain’s fault types as a subset of the source domain’s; (**c**) illustrates the source domain’s fault types as a subset of the target domain’s; (**d**) depicts intersecting fault categories with unique types in both source and target domains. different tasks. This approach grants the model stronger generalization capabilities when dealing with new fault types. In particular, the model is capable of addressing potential differences in fault types between the source and target domains, thereby improving the diagnostic accuracy in target operating conditions. 2. Design of a network structure based on dynamic convolution. To capture dynamic changing features in bearing fault signals more effectively, this paper proposes a novel convolutional neural network structure based on dynamic convolution. Compared to traditional convolutional networks, dynamic convolution can automatically adjust its parameters based on the characteristics of the input data, thus adapting more flexibly to temporal and spatial changes. This effectively captures the dynamic patterns of specific features, offering more flexible receptive fields and adaptability. 3. Design of an improved cross-entropy loss function. This loss function better guides the model to learn effective feature representations, thereby enhancing the model's performance and accuracy in bearing fault diagnosis tasks. The rest of this paper is structured as follows. Section 2 offers a summary of fundamental concepts and theoretical knowledge related to transfer learning and dynamic convolution. Section 3 provides a detailed introduction to the bearing fault diagnosis method proposed in this paper. Section 4 evaluates the efficacy of the proposed model through comparative analysis utilizing the CWRU and JNU datasets against various models. Section 5 concludes the paper and discusses future work.
# Effect of Unit-Cell Size on the Barely Visible Impact Damage in Woven Composites
## 3 Experiment and Characterisation
### Nondestructive Testing of the Impact-Induced Damage
#### 3.3.2 Digital Image Correlation (DIC)  
Using a calibrated three-dimensional (3D) DIC system, the dent depth of the impacted specimens can be successfully determined [28]. Two 8-bit \"Point Grey\" cameras with \"XENOPLAN 1.4/23\" lenses were used, each of which has a resolution of 5 MP. For each specimen, on average, 15 speckle pattern images were recorded by the system using VicSnap 8 software from Correlated Solutions Inc. Each image had a size of (2048 \(\times\) 1194) pixels, which corresponded to a field of view of approximately (120 \(\times\) 70) mm\({}^{2}\). Vic-3D 8 software was then used for the postprocessing of the captured images.