esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise Control in Air Mechanical Ventilation Systems with Three-Dimensional Metamaterials
## 1 Introduction  
The efficient development of the mechanical ventilation systems inside buildings has been carried out especially during the latest years given the unsustainable temperature increase in the climate change. The concerns about this matter are especially accentuated during the summer seasons due to the climate tropicalization that enhances the level of heating and humidity. The installation of air-cooling systems became necessary to have optimal thermo-hyrometric conditions inside residential units, although one of the matters during their operation is the noise of the mechanical elements, such as the fans. The rotating components necessary to activate fans for moving the air flow inside duct lines, and the refrigerating systems (e.g., compressors, cooling fans) are the cause of generating noise [1,2]. On this basis, acoustic filters and attenuators have been studied to be employed in order to limit unpleasant effects and annoyance. One of the methodologies conventionally used for noise attenuation is the filling of the external cases with absorbing material. This solution is not always suitable due to the space requirement, and it is considerably expensive. Furthermore, it is not favorable under a load losses perspective due to motion speed that is higher in practice than the theoretically calculated, with a consequent overall increase in noise levels. To be in line with the environmental requirements, the noise emission of the mechanical ventilation system shall be controlled since a third of the global population is subject to levels of nuisance above the limits established by the World Health Organization (WHO), which define the cause of hearing loss and health diseases, which are sometimes also permanent [3]. On this basis, noise control inside residential living environments is of primary importance.  
Previous research studies have already investigated that the high level of annoyance is due to spectral tones caused by the rotation of fans inside conduits whose frequency is in function of the number of blades.  
This paper deals with an experimental research study focused on the disposition of filters to be placed inside the cases of the mechanical units. The filters consist of spheres staggered between piled layers in order to obtain the attenuation of noise created by mechanical ventilation systems. The proposed filters are composed of a three-dimensional structural frame metamaterial where the singular elements are spheres.
## 5 Simulation Results and Analysis
### Performance Comparison on the AWGN and Rayleigh Fading Channels  
Figures 3-7 show a comparison of the the performance of the proposed AMR approach on the AWGN and Rayleigh fading channels for the BPSK, QPSK, QAM, 16-QAM, and 64-QAM modulated signals at lower SNR values and with varying numbers of samples. Figure 3 illustrates the comparison of the PRA for the BPSK modulated signals at an SNR of 0 dB with different numbers of samples. The bar graph shows that, for every number of samples considered, the PRA for the AWGN was much better than that for the Rayleigh fading channel model. The lowest accuracy was 92%, which was for the Rayleigh fading case when the number of samples was 512 at an SNR of 0 dB.  
Figure 4 shows the percentage recognition accuracy for the QPSK modulated signals. With 512 samples, the lowest accuracy of 94% was achieved for the QPSK signals, which was better than the BPSK signal recognition. The super cumulant feature for QPSK was optimized at lower SNRs and with fewer samples.  
Figure 4: PRA for QPSK signals.  
Figure 3: PRA for BPSK signals.  
The comparison of the percentage recognition accuracy for the QAM modulated signals is shown in Figure 5. As shown in Figure 5, the PRA for the Rayleigh fading channel scenario was less than 90% and approximately 100% with 512 and 4096 samples, as compared to the AWGN channel, in which the PRA was approximately 100% with 2048 samples. Similar results are shown in Figures 6 and 7 for the 16-QAM and 64-QAM modulated signals. The PRA was 100% with 4096 samples for the case of the Rayleigh fading channel and for the AWGN scenario.  
As can be seen from the results shown in Tables 3 and 4 and Figures 3-7, the proposed AMR approach gave better results for smaller sample sizes at lower SNRs, which was the main motivation behind this research. The utilization of a novel concept of the linear combination of the HOCs, rather than the use of the GA-optimized cumulants, actually caused the samples to be far apart in the decision regions, thus making it much easier to recognize the different modulation samples more accurately.
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSS Based on Array Elements
#### 4.1.1 Basic Element Type FSSs
However, the double or multilayer FSS structures face errors resulting from fabrication process, sensitivity in response, and also a substantial amount of insertion loss at the  
Figure 5: Typical shapes of FSS elements [2]. resonance arising from strong effects of the localized electric field within the lossy dielectric medium. Table 2 presents different types of FSS structures utilizing some basic type and advanced elements showing their nature of filter and number of layers.
# An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA
## 3 LDPC Codes in 5G-NR
Figure 2: Structure of 5G NR base graphs; \(-1\) entries are represented as blank, and “Hp(i,j) \(\geq\) 0” are represented as dots. Figure 3 provides a comparative analysis of BG1 and BG2, clarifying their operational characteristics within communication systems. By contrasting these base graphs, key performance indicators such as achievable encoding rates (R) and supported message bit sizes (K) can be determined, aiding in the selection of optimal parameters for specific coding applications.
# A Novel ICT Framework for Sustainable Development Goals
## 2 Background of the Problem and Our Contribution
### Research Design
The goal of the framework is therefore to indicate the key concepts of a phenomenon, which, when organized and connected, constitute, in our case, an ICT framework for SDGs, that serves as a support or guide for the building of something that expands the framework into a useful product such as an ICT platform. To build a conceptual framework, [PERSON] [71] suggests a multi-phase qualitative process consisting of eight phases: (1) Mapping the selected data sources, (2) Extensive reading and categorizing of the selected data, (3) Identifying and naming concepts, (4) Deconstructing and categorizing the concepts, (5) Integrating concepts, (6) Synthesis, resynthesis, and making it all make sense, (7) Validating the conceptual framework, and (8) Rethinking the conceptual framework. Examining all phases including an overview of the literature on SDGs from sustainability, governance, data science, and ICT (the phase 1), reading and clustering the data by discipline and importance to the above three research questions (the phase 2), after reading the selected data and identifying and labeling the concepts (the phase 3), the last phase involves thinking again about the conceptual framework and finalizing the ICT framework for SDGs. The framework consists of a variety of diverse concepts grouped in three modules (data, sustainability, and governance) as discussed in Section 5. In general, the concepts of the data module refer to the statistical, social, and sensor data at three levels of granularity (spatial, temporal, and decision making); the sustainability module consists of the concepts aimed at mapping the SDGs at the local level, ensuing an inclusion of the stakeholders, providing priority ranking of the SDGs, sharing and managing the knowledge, delivering integrated assessment models, and implementing the SDGs at the local level; and finally, the governance module refers to such concepts as the adaptive governance, trustworthy governance, network governance, fair governance, collaborative governance, and accountable governance. The term ICT in our conceptual framework highlights the fact that the framework is shaped by ICT, that is, it is enhanced by digital technologies that master digital representation, computing, and adapting (learning) related to SGDs. As business-systems transition analysis [75; 76; 77] and meta-analyses [78; 79] reveal that digital technology is the trigger and the catalyst for a fundamental societal transition and economic growth, we think that the ICT framework for SDGs we propose here could offer a basis for improved implementation of SDGs.
## 2 Ultrasonic Nonlinearity Parameter  
When a monochromatic ultrasonic wave is transmitted through a material, the transmitted ultrasonic signal is received after propagating through the material. The incident ultrasonic wave is distorted during propagation through the material owing to its nonlinear interaction with the material, leading to second-order harmonic generation in the initially monochromatic ultrasonic wave. Thus, the received ultrasonic signal contains not only the fundamental frequency component but also the second-order harmonic frequency component. The ultrasonic nonlinearity parameter, \(\beta\), which physically denotes the efficiency of the second-order harmonic generation, is determined from the ratio of the second-order harmonic displacement amplitude to the square of the displacement amplitude of the fundamental frequency component as follows [1; 2]:  
\[\beta=\frac{8A_{2}}{k^{2}{x{A_{1}}^{2}}} \tag{1}\]  
where \(A_{1}\) and \(A_{2}\) are the displacements of the fundamental and second-order harmonic frequency components after they have propagated through the material, respectively; \(k\) is the wavenumber of the propagating ultrasonic wave, and \(x\) is the propagating distance of the ultrasonic wave. This parameter \(\beta\) is the absolute nonlinearity parameter. However, when \(k\) and \(x\) are constant in the entire nonlinearity parameter measurement process, \(\beta\) can be replaced with the relative ultrasonic nonlinearity parameter \(\beta^{\prime}\), as shown in Equation (2).  
\[\beta^{\prime}=\frac{{A_{2}}^{\prime}}{{A_{1}}^{\prime 2}} \tag{2}\]  
where \({A_{1}}^{\prime}\) and \({A_{2}}^{\prime}\) are the received signal amplitudes of the fundamental and the second-order harmonic frequency components, respectively, regardless of whether there is a displacement [12], thus, the relative nonlinearity parameter can be measured more easily than the absolute nonlinearity parameter, and it is useful for comparison before and after damage occurs in a material. Note that when we measure the relative nonlinearity parameter, the measurement conditions (e.g., equipment, frequency, types of specimens, and thicknesses of specimens) should be kept constant throughout the nonlinearity parameter measurement process for enabling relative comparisons.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 2 Principles and Classification of Reflector Antennas
### Types and Characteristics of Reflector Antennas
#### 2.2.2 Parabolic Cylindrical Reflector Antennas  
The primary difference between a parabolic cylindrical reflector antenna and a rotational parabolic reflector antenna lies in their formation: a rotational parabolic reflector is generated by rotating a parabolic curve around its axis, while a parabolic cylindrical reflector antenna is formed by translating a parabolic curve along its normal direction to create a cylindrical surface. Parabolic cylindrical reflectors focus in one direction and produce a fan-shaped beam in the other, making them suitable for scanning radar systems with mechanical or electronic beam steering.  
The surface of a parabolic cylindrical reflector is defined by the following equation:  
\[z=\frac{y^{2}}{4f}-f \tag{12}\]  
where \(z\) is the distance from the focal plane, \(f\) is the focal length, and \(y\) is the horizontal dimension. A basic configuration of a parabolic cylindrical reflector is shown in Figure 3a; the shape is constant along the \(x\)-axis, and the feed is typically a line source placed along  
Figure 2: Offset parabolic reflector antenna structure.  
the focal line. As we can see from Figure 3b, the beam of the linear feed is also linear, and at any cross-section, the beam direction is consistent with the rotating parabolic surface.  
The design principles of parabolic cylindrical reflector antennas are similar to those of parabolic reflectors, with the primary distinction being that the energy from the feed propagates in a cylindrical wavefront rather than a spherical one. Consequently, parabolic cylindrical reflectors exhibit a simple structure and unidirectional focusing characteristics, though they tend to be larger and offer reduced flexibility. In SAR applications, parabolic cylindrical reflector antennas can be employed in stripmap-mode SARs to achieve high-azimuth-resolution imaging while maintaining a certain level of range coverage.  
This antenna type is ideal for stripmap SAR and ground surveillance, as it naturally generates a wide beam in one direction, offering wide coverage. However, the lack of focusing in the orthogonal direction limits its use in spotlight or high-resolution imaging modes. Further beamforming is often needed to enhance performance.
## 5 Experimental Results  
### Calibration  
The calibration of the RF array system is essential for optimal beamforming performance and was carried out in two distinct phases. Initially, the calibration focused on the ADCs, utilizing open-source routines previously developed by CASPER group members for this hardware. The second phase addressed the digital correction of mismatches in the microwave front end. Relative gain and phase mismatches of the IF outputs for each chain were determined in relation to a reference chain using a 5.71 GHz reference carrier input. The relative gain differences were compensated using digital multipliers, and gain scale factors were calculated by the measuring average power values of each ADC output. Ideally, for phase difference compensation, fractional delay filters are required, as the RF chains could have smaller delays than the sampling period value. As an interim solution, integer delay filters were designed and implemented in the hardware to mitigate phase differences.
###### Abstract  
The roughness and tortuosity of fractures are essential parameters affecting the fluid flow in a jointed rock mass. This paper investigates the influence of fracture roughness and tortuosity on fluid seepage behavior. A rough fracture surface was characterized by means of three-dimensional scanning and three-reconstruction technology, and the roughness and tortuosity of rock fractures were calculated. Hydraulic tests were conducted on deformed sandstone fractures with a self-made fracture seepage device, and the variation in the seepage flow was analyzed in rough fractures. The experimental results showed that the seepage flow of fluid decreased non-linearly with the increase in fracture roughness. Under different normal pressures, the friction resistance coefficient and tortuous resistance coefficient decreased with the increase in the Reynolds number. The friction resistance coefficient model and tortuous resistance coefficient model were used to quantitatively analyze the influence of fracture tortuosity and roughness on fluid flow, respectively. A modified model of the frictional resistance coefficient, considering fracture tortuosity and roughness, was established, which clearly expresses the law that with the increase in fracture tortuosity and roughness, the seepage flow of fluid decreases, and the head loss increases. The results of this research can provide a theoretical and experimental basis for studying fluid seepage behavior in deformed sandstone fractures.  
Keywords:roughness; frictional resistance; tortuosity; Reynolds number; seepage flow +
Footnote †: journal: Applied Sciences
## 2 Materials and Method
### Remote-Sensing-Based Carbon Flux Model
#### 2.2.4 Water Scalar  
Water status \(f\) (_W_) is another important environmental factor that affects the photosynthesis rate. \(f\) (_W_) is usually calculated using different variables in LUE models, such as soil water content in Glo-PEM [6]. These variables are only observed at a few of points in a particular area. The process of converting from point data to spatially gridded data leads to uncertainties in LUE model results. The normalized difference water index (NDWI) is a remote sensing data based index that can reflect the water content of vegetation [29]. [PERSON] [9] used the NDWI to calculate water scalar of LUE model. We also used this formula to estimate \(f\) (_W_).  
\[f(W)=\left(1+NDWI\right)/\left(1+NDWI_{max}\right) \tag{7}\]  
where _NDWI\({}_{max}\)_ (dimensionless) is maximum NDWI during the peak of the growing season.
## 4 Results  
### Performance of the Improved WRT-WOFOST Model  
The WRT-WOFOST model was created to monitor the growth situation of rice under heavy metal stress. Before assimilating, the parameters of the WOFOST model were regionalized using climate data, soil data and rice growth data in the local area. Then, the WRT was measured during the different crop periods and was assimilated into the improved model. In this study, the PSO algorithm, which describes the process of assimilation, was used to optimize and iterate the WRT between the measured and simulated values [53]. The WRT assimilation cost function is as follows:  
\[P=\ \frac{1}{n}\sqrt{\sum_{i=1}^{n}(WRT_{Mi}-WRT_{Si})^{2}} \tag{9}\]  
where \(n\) is the number of \(WRT\) measured values, here, \(n\) is 3, \(WRT_{Mi}\) and \(WRT_{Si}\) are the measured data and simulated data of the \(WRT\) at the \(i\)th time, respectively.  
In the 1 km \(\times\) 1 km study area, there are other land types that are not used for rice planting. Hence, land classification was conducted before the assimilation process. After classification, the non-rice areas were excluded. Then, the WRT-WOFOST framework was applied to every pixel to obtain the stress factor \((f)\). The stress factor \((f)\) in the rice paddy of the two study areas describes the pollution level of each area (Table 3).  
The results indicate that the range of the stress factor agrees with the pollution level of each study area shown in Table 1. The heavy metal concentration of soil can influence the carbohydrate assimilation efficiency of rice [25; 26]. Therefore, the more polluted a plant becomes, the smaller the f values. According to the above analysis, stress factor \((f)\) has the potential to distinguish different heavy metal stress levels. Then, the stress factor is introduced into the WOFOST model to simulate the LAI during the entire rice growth period under heavy metal stress with a step-length of 1 day. The simulated LAIs by the optimized model for study area A and C were shown in the Figure 6.  
To determine the key periods to monitor heavy metal stress, the accuracy of the WRT-WOFOST model should be verified. The R\({}^{2}\) and MAE values were calculated by Formulas (1) and (2).  
Table 4 shows that when the optimized WOFOST model is applied to the two study areas, both have high precision. In study area A, the R\({}^{2}\) and MAE values are 0.988 and 0.214, respectively, and in study area B, the R\({}^{2}\) and MAE values are 0.97 and 0.199, respectively. Hence, it can be concluded that the WRT-WOFOST model exhibits a good performance in monitoring rice growth under heavy metal stress and can be used to further analyze the key period of assimilation.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### The Problem in the Context of Industry 4.0
#### 3.4.1 The Manufacturing Information Architecture
It would be impractical to formulate a unique security countermeasure for each of these scenarios. Instead, the problem needs to be addressed in the scope of the bigger picture. By analyzing each of these scenarios, certain common attack vectors can be identified that, if accounted for, could avoid the occurrence of many subsequent scenarios. Most of these attack vectors have one key aspect in common--the communications network between hardware and software platforms (especially IT and OT platforms). Furthermore, the use of cloud-based design increases the number of available attack vectors. If a hacker can obtain access to the communications network, an interception of data can be performed, and malicious data can even be injected into the communications network to disrupt the manufacturing process. Figure 5 illustrates the general context of the manufacturing information architecture and where the associated attack vectors are introduced. Therefore, an efficient and effective security solution for the communications network would form a very important and key integral part of the entire manufacturing ecosystem. It is thus a vital starting point for next generation manufacturing industries to introduce proper security measures in order to ensure a cyber-secure (and consequently, to a great extent, physically secure) manufacturing ecosystem.
## 4 Discussion
### Potential for Technology Deployment
#### 4.2.3 Capacity Building  
The preparation, processing and interpretation of resistivity surveys with the method implemented in this study require advanced skills: (i) a solid knowledge of the local geology and hydrogeology, to perform appropriate survey preparations and valid interpretations; (ii) a sound understanding of measurement physics, to be able to anticipate and explain subsurface resistivity responses; and (iii) an ability to write complex command files for software R2. Any gaps in these skills among the Malawian hydrogeologists or geophysicists (or other professionals) may be addressed through training, ideally via a combination of field work, lectures and workshops. Training should cover implementing technicians to reduce occurrence of common mistakes made in instrument calibration, method execution, hardware treatment (e.g., cable shortening) and data handling and processing. Ideally, too, training would rapidly evolve from international external delivery to in-house delivery via Malawian user organizations, i.e., trained Malawian university research users, start-up user companies established in Malawi assisting WASH drilling programmes and/or users or data decision makers in the Ministry.