esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Dimensional Scanning of Circularly Polarized Beams via Array-Fed Fabry-Perot Cavity Antennas
###### Abstract
In [35, 36], the typical highly reflective PRS is designed as a layered superposition of dielectric substrates and metals. Specifically, in [36], two separated patches decoupled by a metallic plane are considered in a transmitting-receiving scheme, with the bottom one exhibiting a high reflectivity necessary for high elevation selectivity, and the upper shaped one responsible for polarization conversion. On the other hand, electronic beam-control methods, as part of beam shaping, have been proposed in [37, 38, 39, 40], where the dimensions of the design elements are tuned to adjust the modal features of the supported leaky-wave solutions. A classic strategy to perform electronic beam steering consists of exploiting the standard array theory. Simple free-space arrays may require a large number of elements to obtain a fine direction tuning, suffering from parasite cross-talk phenomena among elements, grating lobes, and requiring complex feeding systems [41]. Embedding multiple sources in resonant cavities has been proven effective in [42, 43], in terms of array thinning, simplification of the feeding scheme, and grating lobes mitigation. In this work, we combine the concepts of polarization conversion and electronic array-based beam steering to obtain a novel polarization-conversion metasurface (PCM) with an increased axial-ratio (AR) elevation stability and an enhanced azimuthal symmetry of the unit cell. The main goal of this study is to design an FPCA capable of radiating highly directive CP pencil beams in any desired direction within a certain angular range by exploiting both the leaky-wave frequency scanning behavior and the use of multiple feeders. This paper is organized as follows. In Section 2, the theoretical background and the PCM design strategy are presented. The latter is exploited in Section 3, where the element patterns of an optimized, original unit cell are obtained through computationally efficient full-wave simulations based on the reciprocity theorem. The consequence of embedding multiple sources is then illustrated in Section 4 for a few case studies. The final remarks and future perspectives are discussed in Section 5.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 5 Design
### Opportunistic Forwarding  
The main aim of using opportunistic forwarding is to exploit multi-reception benefits for increasing network reliability and network throughput. However, the forwarders need to coordinate each other to prevent duplicate packet forwarding. The coordination mechanism involves listening transmissions from other relays and prioritizing relay:  
* Opportunistic listening: A relay always listens to transmissions by other relays. If the higher prioritized relays do not forward a packet, the relay can forward it to the gateway.
* Priority forwarding: The priority mechanism is a critical aspect for preventing redundancy and collision. As per our mechanism, relay priority is based on its Received Signal Strength Indicator (RSSI). The network server is responsible for managing this priority. The network server collects this RSSI and saves it to the correct relay id in the priority list. Because of different RSSI measurements in every transmission, the network server always takes the RSSI average before sorting the priority list. The gateway sends this priority list before sending the feedback message because the packet information list already occupies feedback capacity. After receiving the feedback message, the relays forward the received packets as per these priority rules.  
Figure 4 illustrates the transmission of feedback messages and forwarding messages. The Y-axis represents the distance between each device and the gateway. Consequently, relay 1 has a higher priority than relay 2. First, an end-device transmits (T) 3 packets via LoRa communication. Relay 2 receives (R) all packets successfully, while relay 1 only receives packets \(P1\) and \(P2\). Because the gateway does not receive any packets, it sends an empty feedback message. After relay 1 receives the feedback, it begins to transmit packets \(P1\) and \(P3\) while relay 2 continues to wait and listen. Relay 2 suppresses forwarding of \(P1\) and \(P3\) as it overhears the forwarding of them by relay 1, but it forwards \(P2\) as there is no forwarding of \(P2\) by other relays. The next feedback message contains \(P1\) and \(P2\) without \(P3\) because it was lost during forwarding transmission. Lastly, only relay 1 forwards packet \(P3\).  
Figure 4: Example to illustrate transmission priority and feedback message.
## 3 Proposed Method
### Distortion Correction Network  
We grayscaled all image patches used for training. The picture directly taken by the camera was a three-channel color picture. It contained the information of an RGB (red, green, and blue) three-channel, while the gray image we want only needed to retain the brightness information of the image; therefore, the color information was discarded. For the bridge crack images to be processed in this paper, the brightness information of grayscale images could fully express the overall and local features of the image; furthermore, the grayscale processing of the image greatly reduced the amount of calculation for subsequent work. \[Gray=R\times 0.299+G\times 0.587+B\times 0.114 \tag{1}\]  
Since the distortion correction itself is a reconstruction process, we constructed a full convolution neural network for this task. Because we needed to maintain the details in the whole image while reconstructing the image, we used the method of cascading detail layers to fuse the low-dimensional detail features with the high-dimensional features [20]. As shown in the figure, after weighing the operation speed and reconstruction accuracy, we adopted the following network structure as the  
Figure 4: Flowchart of the entire method. distortion correction terminal. The receptive field of the neural network should be large enough to cover the entire field of view (FOV), and the network should be deep enough. The input image size of our system was \(408\times 568\). All the network structures in this system were determined through the tradeoff between accuracy and speed. The first layer was a convolution layer that filtered the input with 64 kernels and each filter was of size \(7\times 7\times 1\). The output image, which was as large as the input image, could be obtained using the transposed convolution decoding region [21]. The structure of the network is shown in Figure 5. The method of skipping connections could be simply realized with backpropagation without gradient vanishing. Although models with a higher resolution may lead to higher simulation accuracy, a longer computation time is required. Table 1 shows the detailed structure of the distortion correction network. Based on the relationship between the pixels, we defined a set of loss functions. Here, we used two groups of loss functions to represent the learning effect of the distortion correction network, and these loss functions were as follows. The reconstruction loss function was calculated using:  
\[L_{pixel}=\frac{1}{hw}{\sum\,{}^{i}_{i}{\sum\,{}^{w}_{j}}\rho(I_{1}(i,j)-I_{2} (i+V^{x}_{i,j},j+V^{y}_{i,j}))}, \tag{2}\]  
where \(i\), \(j\) represent the values in the horizontal and vertical coordinates of the pixels in the time spectrum diagram \(I\), respectively, \(V^{x}\) and \(V^{y}\) are the estimated pixels in the horizontal and vertical
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 2 Background  
In this chapter we introduce the SCS-HK coding and the FD jamming schemes, which are the basis for this work. We highlight the role of jamming for security, and address FD concepts along with its main disadvantage of self-interference, and how it can be canceled. We also address practical issues such as the criteria for selection of the jamming power level, the energy cost of jamming, and the decoding strategies that can be used by Eve to combat the jamming and attempt to retrieve the information.
# Approximate Networking for Universal Internet Access
## 2 Approximate Networking Technologies
### Approximate Networking: Old Wine in a New Bottle?  
We do not claim that approximate networking is a new invention. Taking a broad view, we see that many established existing technologies are in fact examples of approximate networking. Indeed, delay-tolerant networking (DTN), information-centric networking (ICN), the concept of lowest-common denomination networking (LCD-NET) [24], the use of caching and opportunistic communication are all approximate networking solutions in this classical sense. The User Datagram Protocol (UDP) protocol approximates the transport service provided by Transmission Control Protocol (TCP), but it trades reliability for performance gains. We can also have approximate networks that provide only a tenuous approximation of the quality, nature, or quantity of the Internet: e.g., servicesthat rely on data mules (e.g., DakNet [25]) are only infrequency connected to the Internet; there other also services (such as Outernet [26] and Internet in a Box [27]) that approximate the Internet experience without actually connecting to the Internet.  
The novel aspect of approximate networking is that it can leverage advances in hardware-based approximate computing developments. In particular, researchers can utilize cross-layer approximation across the computing stack (refer to Figure 1), where the stack contains, in addition to the networking, programming languages, compilers, operating systems/databases, and hardware architectures. For instance, approximate programming languages (such as EnerJ, EnerC, etc.) can be used to specify the critical and the non-critical aspects of computation (e.g., EnerJ is a general purpose programming language built as an extension to Java that exposes hardware faults in a safe, principled manner; results have shown large amounts of energy savings with slight sacrifices to QoS). The same general idea applies to networking devices (such as switches and routers) where not all operations are equivalent--some aspects have to be precise, while others can be approximate. Approximate computing can be used to differentially specify the critical parts of the program and the less critical parts depending upon their inherent resilience properties (see resilience characterization in [14]); in this manner, approximate networking can have greater support from the hardware in implementing context-appropriate approximations.  
A representative Taxonomy of approximate networking concepts is shown in Figure 3. A summary of some sample works related to the various facets of approximate networking taxonomy highlighted in the taxonomy presented in is also presented in Table 1 for illustrative purposes.
###### Abstract  
This study investigates the capacity, in terms of energy, of waffle-flat-plate (WFP) structures with hysteretic dampers subjected to biaxial seismic actions. A numerical model was developed and calibrated with the experimental results obtained from shake-table testing carried out on a WFP specimen subjected to biaxial seismic loads. Then the WFP system was retrofitted with hysteretic dampers--slit-plate dampers (SPDs)--and the numerical model was subjected to different sets of ordinary ground motion records to attain different seismic performance levels (SPLs). Each set of records was applied in a sequence of scaled seismic simulations until the SPL of near collapse was achieved. The capacity in terms of input energy and dissipated energy are presented for the different SPLs, taking into account the differences observed under unidirectional and bidirectional seismic loadings. Furthermore, the level of damage (i.e., accumulated plastic deformations), the level of ductility and the relationship between them--expressed as equivalent number of cycles--are also shown for both the WFP system and the hysteretic dampers. The seismic capacity of the WFP system is found to be significantly enhanced by the inclusion of hysteretic dampers.  
bidirectional seismic loadings; hysteretic dampers; waffle-flat-plate structure; energy dissipation capacity; energy input +
Footnote †: journal: Applied Sciences
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 4 Cislunar Dynamics (Constellation Scenario)
### Solar Radiation Pressure (SRP)  
The in-depth understanding of the Solar Radiation Pressure (SRP) to be included in the simulation for the proposed constellation design [32] provides a clear and concise approach towards modelling the same as below:  
The governing equation for SRP is given as:  
\[a_{SRP}=-\frac{\phi}{c}\frac{A}{m}C_{R}\cos(\theta)\frac{1}{2}\big{(}C_{R_{o}} +C_{RIR}\big{)}\
u \tag{11}\]  
where \(\phi\) is the Solar Radiation Pressure; \(c\) is the speed of light; \(A\) is the area covering the satellite surface; \(m\) is the mass of the satellite; \(C_{R}\) is the scaling parameter which functionsas a deficiency absorbent; \(\theta\) is the angle between satellite surface normal and the incident radiation, \(C_{R_{v}}\) is the visible portion of the solar spectrum, \(C_{R_{lk}}\) is the infrared part of the solar spectrum, and \(\
u\) is the shadow spectrum.  
The radiation pressure of the Sun in the vicinity of the Earth is  
\[\phi=\left(\frac{1 AU}{r_{\odot}}\right)^{2}P_{\odot,1 AU} \tag{12}\]  
The reason for adding this expression is to determine and make the Sun's proximity to the Earth each time when the SRP is being calculated onboard each satellite of a constellation. The motive here to make sure that the alignment is correct while the operations are on.  
The AU is the astronomical unit; \(r_{\odot}\) is the instantaneous distance from the Sun to the satellite and the Sun position, and \(P_{\odot,1 AU}\) is the solar radiation pressure at 1 AU. This leads to another important equation:  
\[\phi_{S}=\left(\frac{1 AU}{r_{\odot}}\right)^{2}\ast P_{\oplus,1 AU}(t) \tag{13}\]  
SRP remains crucial for both Earth and Moon orbits. Hence, utilizing the equations mentioned above, a detailed analysis of the SRP impact on both Earth and Moon orbits is done and will be elaborated in the sections below.
## 8 Conclusions  
This study considers the degradation of the geomaterial medium associated with the plastic flow, reflecting the actual response of the interaction of pile groups and the geomaterial, which is normally ignored by the classical design of pile foundations. The main conclusions of the study are as follows:  
* Due to the application of the cyclic torsional loading, there is a progressive deterioration of strength and stiffness of the surrounding geomaterial, resulting in significant degradation of the pile group capacity.
* A cyclic degradation parameter for plastic strain and a logarithmic degradation parameter for twist and shear have been identified as a function of frictional characteristics of flow-controlled geomaterial which in turn depend upon the number of cycles, plastic strain, and dilation. The flow-controlled cyclic degradation parameters for plastic strain (\(D_{f}^{\prime\prime},D_{f}^{\prime},D_{f}\)) and logarithmic degradation parameters for the twist (\(a_{t}\), \(b_{t}\)) and shear (\(a_{s}\), \(b_{s}\)) have been classified and recommended for designing pile groups.
* The twist decreases with an increase in the number of cycles and dilation captured for the pile group (2,2). The peak twist logarithmically decreases and then becomes asymptotic after 30 cycles of loading in a flow-controlled geomaterial.
* The plastic strain increases with an increase in the number of cycles for the pile group (2,2). The peak plastic stain has been obtained as 43.44, 27.34, and 14.19% for 2\({}^{\circ}\), 6\({}^{\circ}\), and 12\({}^{\circ}\) dilation, respectively.
* The 3D numerical model on the pile group (2,2) and the single pile have been compared to the result of the experimental data of the single pile. The peak shear stress for pile group (2,2) and a single pile for normalized cyclic torque are found in the range of (5-8.2) kPa at dilations of 2\({}^{\circ}\) to 12\({}^{\circ}\) in the present analysis.
* The peak shear stress for the pile group (2,2) logarithmically decreases and then asymptotically converges after 60 cycles of loading in a flow-controlled geomaterial which in turn is a function of the number of cycles and dilation.  
Conceptualization, S.M. and A.T.; methodology, S.M.; software, S.M.; validation, S.M.; formal analysis, S.M.; investigation, S.M.; resources, S.M.; data curation, S.M.; writing--original draft preparation, S.M.; writing--review and editing, S.M. and A.T.; visualization, S.M. and A.T.; supervision, A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data, models, and code generated or used during the study appear in the submitted article.  
The authors are thankful to Delhi Technological University for infrastructural support for conducting this research work.
## 4 Measurement Processing
### Kernel Estimation and Weighting Function  
Even though the weight matrix described in Section 4.2 might be constructed by hand, this is not a practical approach. Weight matrices can be calculated fully automated using the kernel estimation approach.  
The key idea behind the kernel estimation is to guess the probability density function of a data set by adding density kernels of each data point to a global distribution. A full explanation of how to calculate these kernel density functions can be found in [PERSON] [24].  
The received distribution can be used to generate local weights for each data point. The authors of [PERSON] et al. [25] provide a method to calculate these weights according to the density of the output data set [25]. Nevertheless, the provided function can be applied on the input data as well, making it possible to evenly distribute the impact of areas with more data points than others on the regression coefficients.  
An analysis of different kernel functions and their impact on the global density function can be found in [PERSON] [26]. It thereby seems that the chosen kernel function is less important than the bandwidth, which in essence defines the width of a kernel [26].  
In this research, a gaussian kernel with bandwidth of \(0.1\,\mathrm{m}\,\mathrm{s}^{-2}\) as kernel function is applied. The weights for each data point are calculated as follows.  
\[w=\left\{\begin{array}{ll}1-\alpha p&\text{if }p\geq 2.5\\ 0.5&\text{if }p<2.5\end{array}\right. \tag{8}\]  
with \(\alpha=0.2\) and \(p\) as value of the kernel density distribution for the specific acceleration. These values ensure that no point is weighted with a lower importance than 50%.  
The accelerated regression curves are obtained via an Levenberg-Marquardt based an optimizer of the \(SciPy\,1.8.0\)[27] python library. The constraint \(|e|<0.1\,\mathrm{dB}(\mathrm{A})\) leads to a non-linearity in the equation system. This system is not solvable as closed form so that an optimizer is used.  
Figure 4 shows the impact of the three different regression algorithms on the calculated curves on the measurement conducted with LSBEV in front drive mode and 255P. It is visible that the sole weighting of single data points does not impact the result for the displaced measurement. This is not surprising since the data points in this run are evenly distributed over the x-axis. Nevertheless, a constrained least square leads to different results in the lower acceleration range. This is physically more plausible than the other curves, which is why that algorithm is used further in this research to calculate the regression coefficients for accelerated noise.
# Analysis of SNR for High-Orbit Target Detected by Ground-Based Photoelectric System
## 1 Introduction  
Contemporary ground-based monitoring techniques mainly include optical telescopes and radars. Radar systems searching for a typical target at a high orbit must employ high-power transmitters, which are large and heavy [1; 2]. Furthermore, a radar system must emit signals actively, which can easily expose its presence and location [3]. In contrast, optical telescopes have the advantages of high resolution and the capacity to detect stealth targets, and they provide indications of satellite health, status, and activity. Ground-based imaging of targets in multiple infrared wavebands can also provide information from spatially resolved temperature maps that can be analyzed [4]. [PERSON] [5], using thermal information from the Space Surveillance Network Analysis Model database, developed a computer model to predict the infrared brightness of satellites. The predicted value can be compared with observed values of various satellites to enhance the stability of payloads. [PERSON] [6] developed a R-band filter optical telescope for observing brightness variations of satellites to determine the reflectance of artificial objects versus the sun phase angle. This work benefits the brightness changes of other unknown objects. [PERSON] [7] developed an analytical method for isolating the spectral radiances of solar and low-orbit satellites using an infrared photoelectric system. His model is useful for processing single-band radiometric data, as well as multispectral and hyperspectral data from the perspective of object detection. [PERSON] [8] used a large F-number mid-waveband infrared camera with a cold stop applied on a 1.23 m photoelectric telescope. It had detected the International Space Station and sometypical low-Earth orbit objects. Signal-to-noise ratio (SNR) is an important index for detecting whether a space object can be detected. High-orbit targets (HOT) usually have a small SNR, and are limited by weather factors, the distance from observer to object, etc. This paper developed a SNR model relating the orbit height, target temperature, atmospheric transmittance, sky background radiation, and many parameters of a 1.23 m optical system. The goal of our model was to determine whether a given satellite could be detected reliably at the necessary range by our system.  
The remainder of this paper is organized as follows. In Section 2, a SNR math model of the satellite is presented. In Section 3, we simulate the SNR of HOT at different zenith angles and infrared bands. In Section 4, we analyze the result of calculate values. In Section 5, a simple space target observation experiment of the K-band is built, and conclusions follow in Section 6.
## 4 System Development and Experimental Verification
### Experimental Verification
#### 4.2.4 System Overall Risk Warning Function Experimental Verification  
The experimental procedure mainly includes the following steps: First, let the vehicle equipped with a mechanical arm drive uniformly over the obstacle area (five times back and forth), and record the actual warning values. Secondly, under the warning trigger condition, send the target information of the mechanical arm posture adjustment. Then, the system refreshes the obstacle warning information in real time during the response adjustment and action execution of the mechanical arm, as well as after the action is completed, to monitor the effectiveness of the obstacle avoidance action. Finally, by comparing the offline measured values with the warning values, the accuracy and effect of the actual warning are evaluated.  
The experimental results are shown in Table 2: The table gives the system's warning and output values before and after the obstacle avoidance of the measured obstacle. From this, it can be seen that the average obstacle avoidance error output by the mechanical arm is between 1 and 2 cm, which meets the design accuracy requirements.  
In conclusion, through system development and experimental verification, the obstacle avoidance function of this system has been significantly improved compared to before. It shows that the new generation of high-speed rail tunnel detection vehicles can fully meet the intelligent obstacle avoidance needs during tunnel lining detection.  
Figure 20: Remapping results of contact network frame point cloud and camera image.