esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Compressive Properties
#### 3.3.1 Compressive Test Results for Chiral Structures  
According to the compression test results, the highest compressive stress (0.85715 MPa) in the chiral structures occurred in the HG specimen. The maximum compressive stress of the uniform specimen was 0.81773 MPa, while the compressive stress of the RG specimens was 0.8067 MPa. The lowest compressive stress (0.22529 MPa) in chiral lattice structures occurred in the VG specimens. The highest elastic modulus was observed in the RG specimen, while the Young modulus of the uniform specimen was lower than the elastic modulus of the HG and RG specimens (Table 4). The compressive stress of the VG specimen was 72.4% lower than the uniform specimen, while the elastic modulus was only 3.5% lower.  
Hence, the strain value of the VG specimen (\(\varepsilon=0.0376\)) was very low compared to the other specimens. From the stress-strain curve in Figure 9, the low-density region in the bottom layer of the VG specimen deformed rapidly. This rapid deformation caused the specimen to reach maximum stress at a very small strain rate. The reason is that the elastic modulus is relatively higher than the other structures. The fact that the elastic modulus of the horizontal (7.2848 MPa) and radially graded specimens (7.3443 MPa) in the chiral design were higher than the uniform specimens (6.0109 MPa) indicates that the gradient structure provided a more balanced load distribution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Max. Comp.**} & \multicolumn{1}{c}{**Energy**} & \multicolumn{1}{c}{**Young’s**} & \multicolumn{1}{c}{**Strain at Max.**} & \multirow{2}{*}{**SEA**} \\  & \multicolumn{1}{c}{**Stress**} & \multicolumn{1}{c}{**Absorption**} & & & **Modulus** & & **(KJ/kg)** \\  & & **(Mpa)** & **(KJ)** & **(Mpa)** & **(Mpa)** & **(Mm/mm)** & **(KJ/kg)** \\ \hline \multirow{3}{*}{Re-entrant} & Uniform & 0.40694 & 2.489 & 2.574631 & 0.1580576 & 715.229 \\  & Vertical & 0.07614 & 4.292 & 2.4
# Biography of Muscle Tension Dysphonia: A Scoping Review
## 3 Results
### Diagnosis
#### 3.2.3. Acoustic Study of the Voice  
Twelve of the thirty articles in this group analyze the acoustic analysis of patients with MTD. Variations in certain acoustic parameters have been described in comparison to patients with MTD and healthy subjects or patients with other pathology. An increase in the fundamental frequency, f0, attributed to an increase in the tension and subglottal pressure, and a reduction in high frequencies, attributed to the lack of relaxation in the laryngeal muscles [36], have been described. In addition, variations have been reported in other acoustic parameters, such as shimmer, jitter, NHR (noise to harmonic; Table 2 is a summary of articles about the diagnosis ratio), and other noise-related parameters. However, the effect size of these parameters is very variable and not constant [37].  
The cepstral peak prominence (CPP) is regarded as an acoustic measure of voice quality and the severity of dysphonia. Shim et al. [38] found that patients with MTD had significantly lower cepstral peak prominence (CPP) and CPP F0. They also had a significantly higher cepstral and spectral index of dysphonia (CSID) and a low- to high-frequency spectral energy ratio. Patients with MTD have also been found to have lower values of smoothed cepstral peak prominence (CPPs).  
Several studies agree that acoustic analysis by itself is not useful in differentiating patients with MTD from healthy patients [39; 40].  
Relative fundamental frequency (RFF) is a parameter extracted from acoustic analysis that is being studied as a diagnostic indicator of MTD [41; 42]. It measures changes in the fundamental frequency at transitional moments in the voice. Stepp concludes that altered values in the RFF of the cycles immediately preceding the production of a voiceless consonant (onset) and those immediately following (offset) in patients with hyperfunctional voice patterns can be interpreted as high levels of muscle tension, while Roy finds onset RFF values to be more sensitive.
# RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression
## 2 Background
### PixelCNN  
The autoregressive model decomposes the joint density distribution into a product of conditional distributions for multiple elements. Its formula is described as follows:  
\[\mathrm{p}(x)=\prod_{i=0}^{N}\mathrm{p}(x_{i}|x_{<i}) \tag{1}\]  
The autoregressive model demands a strict context structure, where for each element, only the preceding pixel information can be used to predict the current pixel's density distribution. Models like MADE (masked autoregressive model estimator) [6], NADE (neural  
Figure 1: Sample images of pulsar candidate bodies derived from FAST observations and processed using the PRESTO software.  
autoregressive model estimator) [7], and RANDE (real autoregressive model estimator) [7] implement this probability prediction function using neural networks. Image data contain spatial structural information, and simply flattening images into 1D sequences can lead to a significant loss of spatial information.  
Hence, to address this, ([PERSON] et al., 2016) [8] introduced the deep generative model PixelCNN. It employs convolutional neural networks to capture structural information and models the pixel probability distribution of natural images in a z-scan order. To achieve autoregressive dependencies, [PERSON] defined two types of masked convolutional layers, as illustrated in Figure 2. The convolution kernel of a 2D convolutional layer is multiplied by a mask matrix, which constrains network connectivity relationships, ensuring compliance with autoregressive requirements. In Figure 2a, Type A convolution masks the lower-right part of the convolutional kernel information. By using Type B convolution multiple times, as shown in Figure 2b, we can expand the receptive field and extract information from the left and upper positions of the current location.  
PixelCNN leverages the advantages of convolutional neural network operations. Convolutional layers are efficient at extracting spatially correlated information and can be parallelized for processing. This ensures both model training and data processing speed. However, PixelCNN also has some issues and limitations. The inherent characteristics of convolutional networks determine that PixelCNN can model local correlated information effectively, yet it struggles to efficiently utilize dependencies over longer distances. Research indicates that enlarging the receptive field is crucial for enhancing model performance. To achieve this, PixelCNN stacks multiple masked convolutional layers. However, this approach also presents the following problems:  
1. Stacking multiple network layers increases the model's parameters. As the network depth increases, the convergence speed of the model slows down, and it can even lead to a decrease in model performance or instability.
2. Despite enlarging the receptive field, the local nature of CNNs makes the model focus more on neighboring information, often neglecting crucial information from distant areas.
# Analysis of Passive RFID Applicability in a Retail Store: What Can We Expect?
## 3 Influence of Retail Store Products and Propagation Phenomena on a Tag Sensitivity
### Retail Store Results
#### 3.3.2 Retail Store Measurement
This agrees with the simulated measurement scenario (Figure 13). Again, Higgs 2 shows the worst performance regarding the number of responses as well as their variation along the measurement trajectory, in relation to both Higgs 3 and Higgs 4. When Higgs 4 is attached to a retail product, the number of responses decreases, and the variation of its responses increases due to multipath propagation and material effects discussed earlier. If the product is larger in size, then the deterioration of tag antenna properties is more significant (see Figure 3 and Table 3) which causes degraded response with large variations in the number of responses along the trajectory (lower graph in Figure 16). The responses of a tag attached to low-loss dielectric material are given in Figure 17 for a pack of plastic plates and trash bags. Both products are made of plastic and show an equal number of responses per second (Table 6). The results show a similar pattern of \(N_{ACK}\) along a trajectory but still different response drops are noticed caused by small differences in multipath channel and influence of products on tag antenna properties due to their different size and shape. To sum up, the measurement results showed the highest response of Higgs 4 and when attached to product, the degraded multipath pattern with lower response for water products in relation to plastics. Figure 16: Sniffer measurements of tag responses when attached on water products of different size. \begin{table}
\begin{tabular}{l l} \hline
**Tagged Product** & **Average Number of Reads Per Second** \\ \hline bottle of water 1 1 & 10 \\ canister of water 6 1 & 4 \\ pack of plastic plates & 35 \\ pack of plastic trash bags & 35 \\ \hline \end{tabular}
\end{table} 
Table 6: Impini reading results along the measurement trajectory of a Higgs 4 tag attached to different products. Figure 15: Sniffer measurements of tag responses when attached on cardboard.
## 3 Structure Design and Optimization
### Mechanical Properties and Allowable Values for AlSi10 Mg  
The parts were manufactured on a single laser EOS M400 printer. The choice of the single-laser model machine was made to reduce design risks typical of imperfect alignments in multi-laser systems. The mechanical properties for AlSi10 Mg declared by EOS for a M400 single laser with a 90 \(\upmu\)m layer thickness are shown in Table 5, with the heat-treated values referring to T6 heat treatment [61]. The decrease in the material's tensile strength, yield strength and elastic modulus after the heat treatment is due to microstructural coarsening: a reduction in grain boundary area limits the motion of the dislocation during deformation [62]. Conversely, an increase in ductility is obtained by thermal treatment, making this alloy more suitable for applications that require high ductility. The increase in grain size reduces the micro-hardness above all after the solution heat treatment which is partially recovered by a microstructure refinement in the T6 [63].  
A target value is necessary for the subsequent optimization process and must be chosen accordingly to a safety factor. The yield strength of 220 \(\pm\) 20 MPa could suggest a value of 150 MPa resulting in a factor of 1.33 to take into account the relatively high variability.  
However, the geometry of the trays is mainly composed of thin-wall structures, and cannot be subjected to the T6 treatment. A Stress Relieve treatment, consisting of annealing for 2 h at 300 \({}^{\circ}\)C, was chosen to minimize residual stresses from the PBF-LB/M process, thus modifying the obtainable material strength. A tensile test was performed to investigate this expected change. The test results for specimens placed vertically and horizontally with respect to the building platform are shown in Figure 11. The Young modulus was about 67 GPa with the highest variation in the vertical specimens. The thermal treatment allowed to increase the as-built elongation at break above all for the horizontal specimens. The measured tensile and yield strength were markedly reduced with respect to the T6 state.  
Figure 10: FEM simulation of the new upper (**a**) and lower (**b**) junction points.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mechanical Characteristics** & **Unit** & **As Built** & **Heat-Treated** \\ \hline Tensile strength & MPa & 410 \(\pm\) 40 & 325 \(\pm\) 20 \\ Yield strength (Rp 0.2\%) & MPa & 240 \(\pm\) 40 & 220 \(\pm\) 20 \\ Elongation at break & \% & 5 \(\pm\) 2 & 9 \(\pm\) 2 \\ E-Modulus & GPa & 65 \(\pm\) 5 & 65 \(\pm\) 5 \\ Hardness (DIN EN ISO 6506-1) & HBW & 120 \(\pm\) 5 & 95 \(\pm\) 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: AlSi10 Mg mechanical properties.  
This is not a surprising result since the used layer thickness of 90 \(\upmu\)m is declared as having low Technology Readiness Level by EOS GmbH. The yield strength, in particular, was about 167 MPa. Thus, in order to have a margin of safety the target value for the optimization was considered equal to 130 MPa.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 3 Bistatic RCS Characteristics  
To select and design proper calibration targets, knowledge of the bistatic RCS characteristics of calibration targets is indispensable according to the requirements listed in Section 2. The term RCS is a measure of power scattered in a given direction when a target is illuminated by an incident wave from radar. The RCS can be expressed by the electric field strength [20]:  
\[\sigma=\underset{R\rightarrow\infty}{\lim}4\pi R^{2}\frac{\left|E_{r}\right|^{2 }}{\left|E_{in}\right|^{2}} \tag{4}\]  
where \(E_{in}\) is the electric field strength of the incident wave impinging on the target, \(E_{r}\) is the electric field strength of the scattered wave at the radar, and \(R\) is the range from the radar to the target.  
This definition applies to both monostatic and bistatic target RCSs. However, the target RCS depends on the transmitting and receiving direction. Therefore, the bistatic RCS is generally not equal to the monostatic RCS. Figure 3 depicts both monostatic and bistatic radar configurations. A monostatic system uses a single location for both the transmitter and receiver, whereas a bistatic system locates the transmitter and receiver separately, and the angle between the transmitter and receiver is the bistatic angle.  
At present, there have been many studies on monostatic RCSs for different types of targets, but little information on bistatic RCSs. The TCR is widely used as a calibration target in monostatic SAR radiometric calibration, but the characteristic of its real bistatic RCS, especially the exact bistatic RCS pattern, is not clear in bistatic SAR calibration. In order to solve this problem, we considered two approaches in the following: one based on the monostatic bistatic equivalence theorem, and the other was electromagnetic simulation at the X-band (9.6 GHz), and the TCR had inner-leg dimensions of 0.4 m to acquire stable RCS characteristics within the optical region.
## 5 Experimental Scenarios and Results
### ADR-Det Stage 2 Evaluation Using Selected Datasets  
In this experiment, three evaluation data sets have been fed as input to the ADR-Det stage 2 part of the algorithm:  
* 500 ADR images
* 250 non ADR images
* Total of 250 images, where 200 were non-ADR images and 50 were ADR images  
The ability of stage 2 accurate ADR detection has been evaluated using the following experimental cases since the ADR-Det stage 2 part of the algorithm is the most critical for detecting trucks carrying ADR signs. Therefore, the evaluation results per dataset have been performed regarding the CNN's detection accuracy and presented in the following subsections.
## 4 Discussion on Enabling Technologies
### Electrical Configuration  
The turbine shaft rotational speed is in the range of 20,000 rpm which would require an appropriate choice of a step-down gearbox to speeds of 1800 rpm or 3600 rpm for coupling the turbine to a synchronous generator or for the requirement of separating the unknown speed of the compressor which is not designed yet. Not only is the selection of the gearing configuration dependant on shaft speed but also on the power scale; [PERSON] et al. [45] report that General Electric (GE) has gearing configuration for power ratings reaching 60 MW. The availability of a gearbox providing a speed reduction from 12,000 rpm to 3600 rpm for a 50 MW turbine scale is also disclosed by [PERSON] et al. [52]. Geared turbine generator sets are widely available for steam turbines with power output up to 40 MW\({}_{e}\) as reported by GE and Mitsubishi Hitachi [53,54]. Gearbox manufacturers can provide information on the applicability of having a speed reduction ratio of around 6 from near 20,000 rpm for a 60-70 MW\({}_{e}\) turbine.  
Besides the mentioned technologies which need to be developed for successful use of the radial turbine, there are some operational design considerations reported for sCO\({}_{2}\) turbines which require careful attention. Transient operation of the turbine might induce an overspeed risk and loss of electrical load due to the small size of the rotor. The necessitated temperature reduction near the turbine inlet, to safe margins tolerated by seals and bearings, incurs high thermal stresses in that region that needs addressing via further stress analysis.
###### Abstract  
The Saemangeum seawall, located on the western coast of Korea, is 33.8 km long and is known as the longest embankment in the world. The Saemangeum project is underway for road, railway, and port constructions for internal development. In the Saemangeum area, suitable granular soil for embankment material is difficult to obtain. However, silty clay is widely distributed. In this study, a series of model-bearing capacity tests were conducted as a basic study for using clayey soils as embankment materials. The model bearing capacity tests were carried out using a standard metal mold and a customized metal box. The test results showed that clayey soil, with normal moisture content (NMC), exhibited a large deformation and low bearing capacity. However, when the clay was well-compacted, with optimum moisture content (OMC), it exhibited a higher bearing capacity than dense sand. In addition, when crushed gravel and composite geotextiles were placed in the clayey soil with NMC, the bearing capacity was higher than that of dense sand. From the viewpoint of the bearing capacity, it is considered that clayey soil can be used as an embankment material when clay, crushed gravel, and composite geotextiles are properly combined.  
bearing capacity; soft clay; composite geotextiles; embankment material; reinforced clay; bearing capacity ratio
## 5 AI-Empowered Communication with Perceptual Constraint
### Source-Channel Coding for Perceptual Oriented Communications  
Current digital communication systems are designed based on [PERSON]'s source-channel separation theorem [95], which posits that there is no loss of optimality in applying separate source coding followed by channel coding in the asymptotic infinite block length regime, provided that the source and channel statistics are ergodic and the distortion measure is additive. As shown in Figure 3a, the source-encoded data are transmitted through the wireless channel after channel coding and modulation. The receiver reverses these procedures by first demodulating and decoding the channel code, followed by decompression to reconstruct the original data.  
While the optimality of separation holds for distortion-perception measures under assumptions of perceptual measures, including sub-decomposability, it has been demonstrated that separation is no longer optimal when common randomness between the encoder and decoder is unavailable [32]. Moreover, in practice, idealized assumptions, such as memorylessness, are rarely met [96], rendering separation-based digital communication systems suboptimal [97]. This motivates the adoption of joint source-channel coding (JSCC) to enhance end-to-end performance in practical communication systems (see Figure 3b). Recently, in the context of semantic communications [98], deep-learning-based JSCC methods, such as DeepJSCC [13], have achieved remarkable results by learning mappings directly from training data (both source and channel) [13; 99; 100; 101; 102; 103; 104]. Furthermore, a hybrid scheme that combines SSCC and JSCC has been proposed in [105] (see Figure 3c), demonstrating significant potential in mediating between the two systems.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method (\(R\approx 0.2\) bpp)** & **PSNR (\(\uparrow\))** & **MS-SSIM (\(\uparrow\))** & **LPIPS (\(\downarrow\))** \\ \hline BPG & 33 & 0.96 & 0.20 \\ Attention-based [15] & **34** & **0.97** & 0.14 \\ VAE-based [93] & 31 & 0.95 & 0.20 \\ GAN-based [7] & 32 & **0.97** & **0.06** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of compression methods on the CLIC2020 dataset.
## 2 Materials and Methods
### Satellite Data
Monthly photosynthetic active radiation (PAR) at 1\({}^{\circ}\) resolution grid was downloaded from the NASA Langley Research Centre, Cloud and Earth's Radiant Energy System (CERES, Ed4.1), including adjusted surface PAR both direct and diffuse fluxes under all sky conditions. The total PAR was computed as the sum of both direct and diffuse PAR, and then used to remove the effects of PAR on SIF by normalizing after aggregating into the SIF grid resolution:  
\[SIF_{PAR}=\frac{SIF}{PAR} \tag{2}\]  
To assess the relationships between vegetation variables and meteor-environmental drivers under drought stress, MODIS daytime land surface temperature (LST, MYD11C3, Collection 6) at monthly scale was included in this study, collected from NASA Earth Observation data (Available online: [[https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)]([https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)) (accessed on 12 August 2020)). Similarly, best quality data remained through eliminating pixels with quality control flag. Global monthly precipitation at 0.1\({}^{\circ}\) resolution grid was based on Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG, Version 6, Final \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Source** & **Resolution** & **Data** \\ \hline solar-induced chlorophyll fluorescence (SIF) & GOME-2 (MetOp-A) & Monthly/daily, 0.5\({}^{\circ}\) & 2007–2019 \\ solar-induced chlorophyll fluorescence (SIF) & GOME-2 (MetOp-B) & Monthly/daily, 0.5\({}^{\circ}\) & 2013–2019 \\ solar-induced chlorophyll fluorescence (SIF) & TROPOMI (Sentinel-5 & Monthly/daily, 0.05\({}^{\circ}\) & 2018–2019 \\ enhanced vegetation index (EVI) & MCDIS (Aqua) & Monthly/16-day, 0.05\({}^{\circ}\) & 2007–2019 \\ gross primary productivity (GPP) & FluxSat (MODIS and & & \\ FLUXNET 2015) & Monthly, 0.5\({}^{\circ}\) & 2007–2019 \\ photosynthetic active radiation (PAR) & CERES (Aqua) & Monthly, 1\({}^{\circ}\) & 2007–2019 \\ land surface temperature (LST) & MODIS (Aqua) & Monthly, 0.05\({}^{\circ}\) & 2007–2019 \\ Precipitation & IMERG (GPM) & Monthly, 0.1\({}^{\circ}\) & 2007–2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of satellite-based datasets. run, 2007-2019) was collected from NASA Precipitation Processing System (Available online: [[https://pps.gsfc.nasa.gov/](https://pps.gsfc.nasa.gov/)]([https://pps.gsfc.nasa.gov/](https://pps.gsfc.nasa.gov/)) (accessed on 12 November 2020)).