esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of an Optically Transparent Microwave Absorber Based on Coding Metasurface
## 3 Experimental Verification  
In order to verify the absorptance properties of the proposed optically transparent absorber, samples were fabricated and measured in a microwave darkroom. The total size of the sample was 240 mm \(\times\) 240 mm. The upper ITO film with a thin layer of resistance of 110 \(\Omega\)/sq was deposited on the PET substrate by magnetron sputtering, and the bottom 8 \(\Omega\)/sq ITO film was fabricated in the same way. Then, the upper resistive film was patterned by laser ablation of the ITO coating. In order to improve the optical transparency at the same time, a PMMA structure was used in our construction, which also helps to widen the application field. Finally, the upper PET layer and the lower PET layer were adhered to a 5.6 mm PMMA layer, respectively, using transparent double-sided adhesive. The sample optical transparency of the absorber is shown and experiment in Figure 11.  
It can be seen in Figure 11a that the prepared experimental samples have good optically transparent properties. Figure 11b depicts the experimental arrangement used for measuring the proposed optically transparent absorber. Figure 11c shows the test environment of the sample. One of the corner antennas acts as the source (transmitting antenna) and the other acts as the end (receiving antenna), where both antennas are connected to a vector network analyzer (VNA).  
Figure 10: Monostatic RCS magnitude for the main lobe of the proposed absorber versus frequency for horizontally polarized normally incident waves.  
The transmittance (\(S_{21}\)) and reflectance (\(S_{11}\)) were measured under two different conditions: a metal plate, and the designed absorber. Then, the absorptance is calculated using the following equation [26]:  
\[A(\omega)=1-(|S_{11}|^{2}_{sample}-|S_{11}|^{2}_{metal})-(|S_{21}|^{2}_{sample}- |S_{21}|^{2}_{Air}) \tag{4}\]  
The environmental effects can be subtracted using this method, and accurate measurements can be obtained. The experimentally obtained absorptance curve of the sample and \(S_{11}\) are shown in Figure 12. To improve the measurement accuracy, the main flap of the antenna should point to the center of the absorber during each measurement.  
Figure 11: (**a**) Optical transparency experimental sample. (**b**) Schematic of the arrangement used for measuring the proposed optically transparent absorber. (**c**) Photograph of the test environment.  
Figure 12: (**a**) The variation of absorptance of the experimental sample. (**b**) Reflection coefficient of the experimental sample as a function of frequency.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 2 Design Methodology  
The geometry of the proposed SWB monopole antenna with triple band-notched characteristics is depicted in Figure 1, its related parameters are indexed in Table 1, and the configuration is detailed analysis has been given in [16]. The antenna was implemented on a Rogers RT-Duroid 5880 substrate with dielectric constant of 2.2, tan \(\delta\) = 0.0009, and thickness of 1.57 mm. A procedural evaluation of the presented antenna was carried out in order to analyze the behavior of the designed antenna at particular steps. The main purpose of describing the present section is to explain the technique for obtaining three notched interfering bands at the coinciding frequencies. This section also clarifies the outcome of each notch-creating configuration on the functioning of the reference antenna. Each individual stage illustrates the characteristics of the antenna in terms of VSWR.  
In phase 1, a microstrip-fed structural monopole antenna for SWB application is examined. In the second phase, design and exploration of the SWB antenna with a single notch-band is exhibited. In the third phase, a printed SWB antenna is described with dual-band notch characteristics at two unique interference bands. In the final phase, a planar SWB antenna with triple band notch properties is introduced. The notch bands can be independently tuned to reject individual frequency bands.
## 4 Results and Discussion
### AM-HDGAT for Text Performance Evaluation
#### 4.3.2 Feature Fusion Analysis  
Our ablation study examining the contribution of different feature types reveals that the combination of high-dimensional semantic features and low-dimensional statistical features through mutual-attention yields optimal results. Semantic features alone achieve an F1-score of 0.8456, statistical features alone achieve 0.7823, while their fusion achieves 0.8712, demonstrating the complementary nature of these feature types.
###### Abstract
Therefore, our process is efficient in terms of both time and cost as supported by the simulation result where our proposed scheme SVM-UAV-ARMONT (SVM, UAV-based aerial monitoring) has been compared with state-of-the-art UAV-assisted aerial methods for monitoring agricultural lands based on the performance metrics--the percentage of successful detection (PSD), composite energy consumption (CEC), and average delay (ADL) with respect to a number of devices deployed. Results show that SVM-UAV-ARMONT saves a significant amount of energy and time while generating a huge successful detection percentage. The rest of the article is organized as follows. Related work is discussed in Section 2; materials and methods are discussed in Section 3; history-based trajectory planning is mentioned in Section 4; simulation results and discussion are in Section 5; finally, a conclusion and the possible future scope are mentioned in Section 6.
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 3 Problem Description and Modeling  
The target can be represented by multiple types of visual features, including edge [37], intensity [38], color [39], and texture [40]. Using the complementary features of these multi-sources, information can significantly improve the target identification performance [39; 41; 42; 43]. These different visual features are sampled from different feature spaces. Since the view data are extracted from the same object of interest, these data are interrelated, i.e., each view datum can be obtained from the sparse representation of a part of the samples under the view. In practical applications, gradient direction histogram (HOG) [44], local binary pattern (LBP) [45] and scale-invariant feature transformation (SIFT) [46; 47] are highly expressive perspective features. Therefore, the robustness of the target identification problem is enhanced by combining the above multiple perspectives and training multiple samples at the same time. To express the scheme more intuitively and conveniently, the flow chart of the multi-view and multi-task learning method is presented in Figure 1.  
Assuming that \(s_{l}\) and \(x_{l}\) represent the state variables and observation variables at time \(t\), respectively, the target identification problem can be described as the estimation of a posteriori state probability \(p_{n}(s_{l}|s_{l-1})\) through a limited set of \(n\) particles with importance weights \(\omega_{1}\), which \(x_{1:t}=\{x_{1},\cdots,x_{t}\}\) are the previously observed \(t\) frames of images. Particle samples \(s_{l}^{i}\) are sampled from the importance distribution \(q(s_{l}|s_{1:t},x_{1:t})\), i.e., the simplification of state transition probability \(p(s_{l}|s_{l-1})\). In addition, the importance weight of particle \(i\) is updated through the observation probability, i.e., \(w_{i}^{i}=w_{i-1}^{i}p(x_{l}|s_{l}^{i})\)  
The sparse representation of feature \(x\) can be reconstructed by the regularization \(\ell 1\) problem to minimize the error [3]:  
\[\min_{w}||MW-X||_{2}^{2}+\lambda||W||_{1} \tag{1}\]  
where \(M=[D,I,-I]\) is a complete dictionary composed of target template set \(D\) and positive and negative background template sets \(I\) and \(-I\). Each column is a target template in \(D\), and the candidate region pixels are modified in the column vector. Each column in the background template set is a unit vector with one non-zero element.  
\(W=[a^{T},e^{+T},e^{-T}]^{T}\) is composed of target coefficient and positive and negative background coefficients \(e^{+T},e^{T}\). Each column in \(a^{T}\) represents a classification plane.  
The probability of this observation is derived from the reconstruction error of \(x\)  
\[p(x|y)=\frac{1}{\gamma}\exp\{-\alpha||Da-x||^{2}\} \tag{2}\]  
Figure 1: Target identification schematic of multi-view multi-task algorithm.  
where \(a\) is obtained by solving (1), \(\alpha\) is constant and is used to control the shape of the Gaussian kernel, and \(Y\) is the normalization parameter.
## 2 Materials and Methods
### Permeations and Selectivity Tests  
The vapor permeation measurement of the membrane adopted the American Society for Testing and Materials (ASTM) E96-95 method with a few changes. Before the test, the membrane was dried at 60 \({}^{\circ}\)C for 24 h, and a digital caliper was used to measure the thickness of the membrane. Then, the test cell was filled with a certain quality of penetrant (water, DMMP, or CEPS), and the membrane to be tested was placed over the top of the open-top testing cell and fixed with a fixing device to form a permeation cell, as shown in Figure 1. The obtained permeation cell was put into a convection environment box with constant temperature of 35 \({}^{\circ}\)C and humidity of 10% RH and weighed regularly. The weight loss (\(W\)) was calculated from the slope of the weight-time curve after the weight loss became constant. The vapor transmission rate (VTR, g m\({}^{-2}\) days\({}^{-1}\)) was calculated by Equation (4) and the vapor permeability (VP, mol m\({}^{-1}\) s\({}^{-1}\)) was calculated by Equation (5). The selectivity was defined as the ratio of vapor permeability of water to DMMP or CEPS, and the value of selectivity was calculated by Equation (6) [14]. The accuracy of the results was ensured by three parallel measurements.  
\[\text{VTR }=W/(t\times A) \tag{4}\]  
\[\text{VP }=\text{VTR}\times L \tag{5}\]  
\[\text{S}=\text{VP}_{water}/\text{VP}_{simulants} \tag{6}\]  
where \(A\) represents the tested area of the sample, \(t\) represents the test time, and \(L\) is the membrane thickness.
## 4 Economic Analysis of the IRES  
The life-cycle cost of the system is calculated as follows [1]:  
\[LCC=ICC+EREC+P_{V,O\&M}+P_{V,REP}+P_{V,FUEL} \tag{9}\]  
The initial capital cost (\(ICC\)) is considered as follows [1]:  
\[ICC=\begin{bmatrix}\left(C_{BMG,cap}\right)+\left(N_{PV}\times C_{PV,cap} \right)+\left(N_{BAT}\times C_{BAT,cap}\right)+\\ \left(C_{BDC-CC,cap}\right)+\left(C_{MEM,cap}\right)+\left(C_{WTA,cap}\right) +\left(C_{CHE,cap}\right)\end{bmatrix} \tag{10}\]  
The erection is considered as follows [1]:  
\[EREC=\begin{bmatrix}\left(N_{PV}\times C_{PV,rect}\right)+\\ \left(\left(N_{BAT}\times C_{BAT,rect}\right)\times\sum_{b=1}^{N_{r}}\frac{(1+ x)^{bN_{c}-1}}{\left(1+y\right)^{bN_{c}}}\right)+\\ \left(C_{BDC-CC,rect}\times\sum_{d=1}^{N_{r}}\frac{(1+x)^{dN_{c}-1}}{\left(1+ y\right)^{dN_{c}}}\right)+\left(C_{BMG,rect}\times\sum_{g=1}^{N_{r}}\frac{(1+x)^{gN_{c}-1}}{ \left(1+y\right)^{gN_{c}}}\right)\end{bmatrix} \tag{11}\]The O&M cost of the system is considered as follows [1]:  
\[P_{V,O\&M}=\begin{bmatrix}\left(N_{PV}\times C_{PV,\rho\&km}\right)+\left(C_{ BMC,\rho\&m}\right)\\ \left(N_{BAT}\times C_{BAT,\rho\&m}\right)+\left(C_{BDC-CC,\rho\&m}\right) \end{bmatrix}\times\sum_{i=1}^{N}\frac{(1+x)^{i-1}}{\left(1+y\right)^{i}} \tag{12}\]  
where \(y\) is defined as follows [1]:  
\[y=\frac{I_{nom}-x}{1+x} \tag{13}\]  
The replacement cost of the components is calculated as follows [1]:  
\[P_{V,REP}=\begin{bmatrix}\left(N_{BAT}\times C_{BAT,rep}\times\sum_{b=1}^{N_{ r}}\frac{(1+x)^{bN_{c}-1}}{(1+y)^{bN_{c}}}\right)+\left(C_{BMC,rep}\times\sum_{g=1}^{ N_{r}}\frac{(1+x)^{gN_{c}-1}}{(1+y)^{gN_{c}}}\right)+\\ \left(C_{BDC-CC,rep}\times\sum_{d=1}^{N_{r}}\frac{(1+x)^{dN_{c}-1}}{(1+y)^{dN _{c}}}\right)\end{bmatrix} \tag{14}\]  
where \(N_{r}\) is defined as follows [1]:  
\[N_{r}=int\bigg{(}\frac{N-N_{c}}{N_{c}}\bigg{)} \tag{15}\]  
The fuel cost is considered as follows [1]:  
\[P_{V,FUEL}=\left[\left(C_{BM}\times Q_{BM}\right)\right]\times\sum_{i=1}^{N} \frac{(1+x)^{i-1}}{\left(1+y\right)^{i}} \tag{16}\]
# Post-Filtering of Noisy Images Compressed by HEIF
## 6 Conclusions and Future Work  
Our paper concerns the lossy compression of single-channel noisy images with the use of post-filtering after decompression to improve the image quality for the HEIF coder. This approach was compared with image compression in the optimal operation point if it existed, or for similar values of quality factor. It was shown that a positive effect of post-filtering exists for images of different complexity as well as different intensities of the noise. However, this positive effect varies: it can be large if a compressed image has a simple structure and the noise intensity is quite large and if the CR for a given QF is considerably smaller than the QF for the OOP. An opposite situation takes place if a given QF is quite close to the QF in the OOP. Because of this, we recommend reaching the compromise solutions presented in Table 6 depending on the noise variance for an 8-bit representation of data. This allows compression to be carried out with further post-filtering after decompression in fully automatic mode (this is important for practice). The output PSNR and PSNR-HVS-M were in this case considerably better than the PSNR and PSNR-HVS-M in the OOP, but the attained CR was 1.5-2 times smaller than for the QF that corresponded to the OOP.  
There are many unsolved problems for HEIF. For example, it is desirable to carry out studies for cases of signal-dependent noise, multichannel images, and the classification of RS, CV, and medical data. The problem is that the variety of noise properties in multichannel images of different origin is so immense that we do not expect general solutions. At the same time, prominent solutions for particular situations can be achieved by the 3D compression of all components jointly or components properly combined in groups [41].  
Conceptualization, A.S. ([PERSON]), V.G. and B.V.; Methodology, V.L., A.S. ([PERSON]) and V.G.; Software, V.R. and S.K.; Validation, S.K., A.S. ([PERSON]), and V.R.; Formal analysis, B.V. and V.L.; Investigation, A.S. ([PERSON]); Writing--originaldraft preparation, V.L. and A.S. ([PERSON]); Writing--review and editing, B.V., V.G. and A.S. ([PERSON]); Visualization, S.K. and V.R.; Supervision, V.L. and B.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# First Approach in Analysis of Tool Wear When Milling Additive Manufacturing (AM) Parts
## 5 Conclusions  
Our conclusions for L-PBF parts using IN718 are as follows:  
* Flank wear land width: The width of the flank wear land is larger for support structures compared to solid material for all measurements (Figure 2). Wear increases with the increasing number of travel paths.
* Comparison of flank wear land width: When comparing the milling of support structures to solid material, the width of flank wear land is larger in the former after comparable cutting volumes. This suggests that tools are subjected to a higher load related to abrasive and surface attrition wear mechanisms when machining support structures (Figure 3).
* Tool wear mechanisms: the milling of support structures results in both abrasive wear and surface spalling, in contrast to the abrasive wear observed when milling solid material (Figure 4).
* Bending moments: bending moments during the milling of support structures vary significantly depending on the position of milling paths within the specimen, indicating the presence of strong vibrations during machining and different material removal rates compared to the machining of solid material (Figure 5). Our conclusions for WAAM parts using 304H are as follows:
* Flank wear land width: The width of the flank wear land is relatively larger when milling solid material at path lengths of 640 and 1280 mm. However, after a path length of 1920 mm, the width of the flank wear land remains constant for both areas of the AM material (Figure 6).
* Tool wear mechanisms: tool wear during the milling of WAAM parts is dominated by an abrasive mechanism, with break-outs at the cutting edge corner observed frequently (Figure 7).
* Bending moments: Bending moments during the milling of the surface layer are lower, attributed to reduced material removal compared to solid material milling. The higher tool wear on the surface layer suggests a higher hardness compared to the solid layer below (Figure 7).  
Conceptualization, A.P., R.H. and F.S.; methodology, A.P., R.H. and F.S.; software, K.S.; validation, A.P., R.H. and F.S.; formal analysis, K.S., M.A. and D.K.; investigation, K.S., M.A. and D.K.; resources, K.S., M.A. and D.K.; data curation, K.S., A.P., R.H. and F.S.; writing--original draft preparation, K.S. and A.P.; writing--review and editing, A.P. and F.S.; visualization, K.S.; supervision, R.H., F.S. and A.P.; project administration, F.S. and R.H.; funding acquisition, R.H., F.S. and A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are contained within the article.  
The authors declare no conflicts of interest.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 5 Experimental Data Validation
A detail for the time from 5 s to 5.5 s is shown to demonstrate the continuity characteristic of the synthesizer’s output. the forward scatter direction, the incoherent scattered field dominates. It should be noted that the resulting time delays of the incoherent scattered fields reflect the small separation of the tree canopies. Figure 13 then shows the normalized CIR characteristics for the NLoS case achieved at the flight time 25 s. Now the direct signal between Tx and Rx is not shown, as it is actually blocked by a canopy. This is represented by the strong coherent scattered field with the lowest time delay which results in the attenuation behind the canopy. The other coherent scattered field from the second closest tree is negligible as it is outside the forward scatter direction. The incoherent scattered fields have about the same powers as in the LoS case, as these were not assumed to be blocked by other canopies. The longer maximum delay in the NLoS case is simply a consequence of the particular geometry. Figure 12: Generated CIR for the experimental scenario under LoS propagation conditions as achieved at the flight time 5 s. The direct signal (black line) dominates while the coherent fields (blue lines) scattered from the two canopies closest to Rx are negligible. The total incoherent scattered fields from each canopy (red dashed lines) are stronger than the coherent scattered fields as they dominate in other than the forward scatter direction. The power levels and time delays are normalized with respect to the direct signal between Tx and Rx. Figure 13: Generated CIR for the experimental scenario under NLoS propagation conditions as achieved at the flight time 25 s. The direct signal is blocked and, thus, not shown. The coherent field (blue lines) scattered from the canopy closest to Rx dominates and is responsible for the attenuation behind the canopy. The coherent field contribution from the second closest canopy is negligible. The total incoherent scattered fields from each canopy (red dashed lines) have levels about the same as in the LoS case, as they are not supposed to be blocked by any canopy. The power levels and time delays are normalized with respect to the direct signal between Tx and Rx. As Tx was at the farthest distance from the trees at the end of the alley, the observed maximum time delay is longer than in the LoS case. In summary, the CIR characteristics can also be applied to the physical layer performance analyses where bit error rate (BER), word error rate (WER), etc., can be assessed for different modulation and coding schemes. The granularity of the CIR can be improved by further splitting the canopy contributions into those from each voxel, should this be required by the application. This resolution improvement does not affect the overall link's SNR.
## 5 Experimental Progress
### Introduction of the Test Satellite Constellation
#### 5.1.1 Communication Capability of the Satellite  
The satellite communication payload is equipped with 16 V-Ka forward link transponders with a bandwidth of 450 MHz, and 16 Ka-V return link transponders with a bandwidth of 400 MHz. The satellite adopts the transparent forwarding mode to communicate with the gateway station and satellite terminal.  
During the task, the Z-axis (normal direction of the user antenna mounting surface) of the satellite is controlled to point to the user end position on the ground, to enable longer continuous communication. Through multi-satellite relays, broadband continuous communication can be realized for a specific terminal in a designated area for more than 30 min, which can meet the demand for near real-time bandwidth transmission of remote sensing information [36]. The rotation ranges of antennas on the satellite are listed in Table 1.  
Due to power and thermal limitation, broadband communication payload cannot work continuously in orbit. Therefore, the satellite calculates the working time of the communication payload based on the uploaded user end position (longitude, latitude, and elevation), and then implements power on and off of the payload equipment according to the control sequence of the satellite attitude and QV antennas.