esa-sceva/satcom-chunk-collection huggingface dataset:
# Placental Transcriptome Adapations to Maternal Nutrient Restriction in Sheep
## 2 Results
### Differentially Expressed Genes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**NR SGA 1 vs. 100\% NRC** & \multicolumn{4}{c}{**NR NonSGA vs. 100\% NRC**} & \multicolumn{4}{c}{**NR SGA vs. NR NonSGA**} \\ \hline
**Gene** & **Log2 FC 2** & **FDR 3** & **Gene** & **Log2 FC** & **FDR** & **Gene** & **Log2 FC** & **FDR** \\ \hline _MSTN_ & \(-\)3.10 & 0.03 & _HNRNRPK_ & \(-\)6.28 & 0.01 & _HNRNRPK_ & \(-\)5.53 & 0.02 \\ _CST2T_ & \(-\)2.78 & 0.08 & _UHE2D3_ & \(-\)3.36 & \(<\)0.01 & _MSTN_ & \(-\)3.74 & 0.03 \\ _HLA-DQA1_ & \(-\)1.98 & 0.03 & _CST2T_ & \(-\)3.17 & 0.02 & _FEMMA_ & \(-\)2.35 & 0.09 \\ _SLC5 AS5_ & \(-\)1.92 & 0.07 & _GBT_ & \(-\)2.74 & 0.01 & _EF1 TA1_ & \(-\)1.91 & 0.06 \\ _COL19 AI_ & \(-\)1.87 & 0.01 & _PPAI_ & \(-\)2.23 & 0.09 & _AMER3_ & \(-\)1.56 & 0.10 \\ _IL2 IR28B_ & \(-\)1.52 & 0.01 & _OLEM1_ & \(-\)1.81 & 0.09 & _HLA-DDB_ & \(-\)1.28 & 0.07 \\ _GPRN2_ & \(-\)1.12 & 0.02 & _APLP1_ & \(-\)1.44 & 0.01 & _MulC4_ & \(-\)1.14 & 0.07 \\ _CD74_ & \(-\)1.01 & _+0.01_ & _HLA-DDB_ & \(-\)1.38 & 0.09 & _SL2T/26_ & \(-\)0.77 & 0.06 \\ _FZD2_ & \(-\)0.85 & 0.05 & _HLA-DQA2_ & \(-\)1.04 & 0.01 & _WAN78_ & \(-\)0.76 & 0.05 \\ _MMP9_ & \(-\)0.75 & 0.10 & _NOT1 MM_ & \(-\)0.61 & 0.10 & _JAM2_ & \(-\)0.73 & 0.08 \\ _P115_ & \(-\)0.73 & +0.01 & _SH3G1_ & \(-\)0.44 & 0.01 & _APOE_ & \(-\)0.67 & 0.06 \\ _LRATD1_ & \(-\)0.50 & 0.08 & _MCMS_ & \(-\)0.35 & 0.05 & _PAM_ & \(-\)0.54 & 0.02 \\ _NTE213_ & \(-\)0.35 & 0.08 & _GINS2_ & 0.34 & 0.09 & _FAM3C_ & 0.35 & 0.06 \\ _TTBBS1_ & \(\)0.58 & 0.10 & _TCTC21B_ & 0.43 & 0.10 & _ZN/642_ & 0.53 & 0.04 \\ _CD163_ & \(\)0.65 & 0.06 & _PAM_ & 0.50 & 0.01 & _APO3_ & 0.87 & 0.06 \\ _NDUIA4L2_ & \(\)0.76 & 0.08 & _JMY_ & 0.50 & _G100B_ & 1.04 & 0.02 \\ _PSS12_ & \(\)0.82 & 0.01 & _MRC1_ & 0.51 & 0.03 & _MYOTA1_ & 1.28 & 0.06 \\ _PLACKL1_ & \(\)0.91 & 0.03 & _RNASE1_ & 0.57 & 0.09 & _DOK5_ & 1.31 & 0.07 \\ _CATCSO3_ & \(\)1.78 & 0.08 & _FOL3_ & 0.60 & 0.01 & _EVA1A_ & 1.32 & -0.01 \\ _RTS19_ & \(\)1.92 & 0.07 & _FULL1_ & 0.66 & 0.01 & _TNFSF11B_ & 2.07 & 0.06 \\ _SPATS1_ & \(\)2.31 & 0.08 & _SCCB_ & 0.67 & 0.03 & _GFRA2_ & 2.28 & 0.02 \\ _SLC5A12_ & \(\)2.53 & 0.08 & _NP1 PAI_ & 0.78 & 0.09 & _SIC10A6_ & 2.77 & 0.06 \\ _ZCIAHV1L_ & \(\)0.80 & 0.09 & _SVOPL_ & 0.83 & 0.01 & & \\ _HLA-DDB_ & \(\}\)1.35 & 0.05 & & & & \\ _RPL6_ & \(\}\)1.46 & 0.01 & & & & \\ _PDE6B_ & \(\}\)2.13 & 0.01 & & & & \\ \hline \hline \end{tabular} \({}^{1}\) 100% NRC: well-fed controls; NR NonSGA: nutrient-restricted normal-weight fetuses; NR SGA: nutrient-restricted low-weight fetuses. \({}^{2}\) Log2 FC: log2 fold change. \({}^{3}\) FDR: false discovery rate.  
\end{table} 
Table 1: Differentially expressed genes in placentomes at day 70 of pregnancy.
# Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows
###### Abstract  
Acoustic shadows are common artifacts in medical ultrasound imaging. The shadows are caused by objects that reflect ultrasound such as bones, and they are shown as dark areas in ultrasound images. Detecting such shadows is crucial for assessing the quality of images. This will be a pre-processing for further image processing or recognition aiming computer-aided diagnosis. In this paper, we propose an auto-encoding structure that estimates the shadowed areas and their intensities. The model once splits an input image into an estimated shadow image and an estimated shadow-free image through its encoder and decoder. Then, it combines them to reconstruct the input. By generating plausible synthetic shadows based on relatively coarse domain-specific knowledge on ultrasound images, we can train the model using unlabeled data. If pixel-level labels of the shadows are available, we also utilize them in a semi-supervised fashion. By experiments on ultrasound images for fetal heart diagnosis, we show that our method achieved 0.720 in the DICE score and outperformed conventional image processing methods and a segmentation method based on deep neural networks. The capability of the proposed method on estimating the intensities of shadows and the shadow-free images is also indicated through the experiments.  
Keywords: ultrasound images; shadow detection; shadow estimation; deep learning; auto-encoders; semi-supervised learning +
Footnote †: journal: Medical Image Processing
## 1 Introduction  
Recently, mixed reality (MR) interaction has emerged as a significant research focus [1]. The proliferation of consumer-grade MR devices has spurred the application of MR technology across diverse fields [2, 3, 4]. As MR's natural interaction techniques mature, tailoring design and evaluation methods to specific application scenarios and user needs is crucial for enhancing user immersion and satisfaction [5].  
Immersive analytics merges MR with natural and embodied user interaction, offering three-dimensional information presentation, expanded display spaces, and the integration of physical references with corresponding data [6]. This method demonstrates utility in education, scientific visualization, immersive workspaces, and data embedding, among others, surpassing traditional graphic visualizations in supporting user exploration and comprehension of complex 3D data [7]. Substantial research has been dedicated to developing visualization methods for assorted data types within MR environments [6, 8, 9], including immersive analytics applications in collaborative work [10].  
Thus, this study centers on immersive analytics as its primary application scenario. To improve users' abilities to perceive, recall, and comprehend complex data, visual analytics systems may need to allow users to achieve retrieval, deletion, and other controls over complex data information by way of inputting the data information. This includes adjusting, retrieving, and inputting complex information like colors and coordinates in data visualizations. To more concretely illustrate the significance and application context of our research, we envisioned a prevalent scenario within educational and scientific visualization contexts wherein students or researchers were tasked with exploring and analyzing three-dimensional datasets through mixed reality (MR) technology. In this scenario, adjusting the RGB color values of data visualizations became essential for users to effectively differentiate data layers or emphasize particular data points. Traditional numerical input methods, such as keyboards or standard sliders, may prove unintuitive or cumbersome within an MR setting, thus diminishing user immersion and interaction efficiency. Present studies on data input in MR environments predominantly examine homogeneous methods like keyboarding [11; 12] and clicking [13]. This study identified a gap in systematic and comprehensive research on data input tasks within immersive analytics, particularly concerning interaction efficiency and user experience [5], noting that numerical information plays a crucial role in data visualization, notably in scientific contexts [14], with this gap partially limiting visual analytics development [10]. Addressing this gap, this study concentrated on numerical input challenges, devising two multimodal interaction techniques and comparing them to traditional gesture or keyboard inputs in MR devices like HoloLens2. The objective was to explore and assess numerical input methods for immersive analytics in MR, aiming to bridge existing research gaps and advance MR technology's application in data analysis and visualization.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 6 Conclusions  
This paper provides an analysis of the use of evolutionary algorithms to design complex systems that consist of multiple subsystems. It utilizes an inventory of components that are made available for each subsystem. The mathematical formulation for designing such a system is presented as a multidimensional multiple-choice knapsack problem (MMKP). The number of possible designs for a MMKP problem increases exponentially as the number of subsystems and number of options available for each subsystem increases. As such, the use of stochastic methods such as evolutionary algorithms (EAs) that mimic the metaphor of natural biological evolution provides a way to solve the problem in linear time complexity. A CubeSat design problem is presented as an example and an evolutionary algorithm is successfully applied in finding near-optimal designs. The approach has the potential for producing designs that maximize a mission-specific cost function while minimizing cost and mass. Traditionally, mission concepts are created by a team of engineers by conducting trade studies for mass, cost, volume, performance, and risk. Our approach of automated design using evolutionary algorithms for trade space selection speeds up the design process. Moreover, it provides us with a better basis for detailed system architecture, and hence, better design decisions can be made with confidence. This method generates a set of near-optimal designs, which provides multiple designs to start with and potentially presents vast improvements over a solution obtained through engineering judgement and point design alone. Our EA approach also shows improvement over other stochastic optimization methods including particle swarm optimization (PSO) and simulated annealing (SA). Although our approach shows a credible pathway to identify and evaluate many more candidate designs, these optimized designs will need further study to determine their implementation feasibility.  
The contributions to this article are the following: conceptualization, J.T. and H.K.; methodology, H.K. and J.T.; software, H.K.; validation, H.K.; formal analysis, H.K.; writing--original draft preparation, H.K.; writing--review, H.K. and J.T. and editing, J.T.; visualization, H.K.; supervision, J.T.; project administration, J.T.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by National Aeronautics and Space Administration, grant number 80 NSSC19M0197.  
The authors declare no conflict of interest.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion
### Video Streaming Scenario  
Achieving reliability and high throughput is a challenge in IoT networks, especially over wider areas. Such networks could enable firmware updates over the air or high-throughput applications like video streaming. We evaluated the feasibility of reliable and high throughput traffic over IEEE 802.11 ahby considering a video streaming scenario. In this scenario, the stations represent IP motion cameras with configurable parameters, including motion probability, duration to record if motion was detected and the data rate of the stream to be transmitted. This subsection presents attainable data rates of IP cameras on top of IEEE 802.11 ah and the scalability of such a network (cf., Section 4.2.2) along with a description of the simulation setup (cf., Section 4.2.1).
# Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network
## 2 Related Studies
### Position of This Research  
A MANET is formed in the network layer. It consists of a network connection establishment phase and a packet communication phase. Applications communicate using this packet communication. The transport layer may be used as a middle layer. As mentioned in Section 2.1, existing studies mainly use packet loss rate, throughput, and packet delay to indicate network QoS, and coding rate, frame rate, and delay jitter to indicate video streaming QoE. They use the number of nodes, node moving speed, remaining battery level, and the number of hops as parameters that define the topology of a MANET. However, no indicator of route stability in MANETs has been proposed. The position of the present research relative to the existing studies is shown in Figure 1. The present research studies metrics that can represent the route nonuniformity, which forms a foundation for packet communication and application communication in a MANET.
## 2 Experimental Testing
### Test Procedures  
The experimental procedures are as follows:  
1. A confining pressure of 0.1 MPa was applied to establish the initial system stabilization (about 24 h).
2. The confining pressure was increased to 2.5 MPa at a low rate of 1.68 kPa/min. The volume changes were monitored by a measuring system.
3. The water was thus injected into the samples from the top and bottom. Then, the pore pressure and confining pressure were simultaneously increased by 0.2 MPa at the rate of 1.68 kPa/min.
4. Simultaneous increases of both the confining pressure to 3.5 MPa and pore pressure to 1.0 MPa were applied. The loading rate was controlled at 0.2 kPa/min.
5. Then Skempton's coefficient B was checked to determine the saturation degree of the samples by the following method: * Closing the drainage valve. * Increasing confining pressure of 0.2 MPa. * Measuring the increase in the corresponding pore pressure. * Decreasing the confining pressure down to 3.5 MPa and calculating [PERSON]'s coefficient B. * Checking [PERSON]'s coefficient B repeatedly until it was greater than 0.85.
6. Keeping the drainage valve open, the effective stress coefficient of claystone was tested in line with the following steps: * Increasing the confining pressure by \(n\) MPa (\(n\) = 0, 0.1, 0.14, 0.16, 0.18, 0.2, 0.22), and the pore pressure by 0.2 MPa simultaneously. Then the confining pressure and pore pressure were restored to the previous state (\(\sigma_{c}\) = 3.5 MPa, \(\sigma_{p}\) = 1.0 MPa). * Observing and recording the deformation of samples.  
In addition to the above test of the effective stress coefficient, this study also attempted to explore the influence of other potential factors (the neutral stresses and loading rate) on the effective stress coefficient. Different loading rates (0.01 kPa/s, 0.02 kPa/s, and 0.04 kPa/s) and a new pressure increment (\(\Delta\sigma_{c}\) = \(\Delta\sigma_{p}\) = 0.5 MPa) were tried in the additional tests.  
The variations of confining pressure and pore pressure are illustrated in Figure 3, and the controlled laboratory temperature is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mineral Composition** & **Illite** & **Kaolinite** & **Illite-Smectite Mix Layer** & **Chlorite** & **Quartz** & **Calcite, Dolomite** & **Feldspar** \\ \hline Proportion (\%) & 15 & 15 & 20 & 25 & 15 & 6 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The main composition of the claystone [1].  
Figure 3: A detailed diagram of the test procedure.  
Usually, [PERSON]'s coefficient B is below one [25; 26]. Therefore, it was assumed that claystone was saturated as long as [PERSON]'s coefficient B greater than 0.85. Therefore, sample one and sample two were considered to be saturated. It is worth mentioning the following: (1) the claystone studied in this paper can reach saturation in about 20 days [27]; (2) the saturation of the samples were determined after all the tests, and the results showed that the samples were saturated.
## 4 AI Applications and Their Impact
### Textile Sorting and Recycling  
#### 4.2.1 Recycling-Oriented Sorting  
[PERSON] et al. [24] explored the integration of near-infrared (NIR) spectroscopy with CNNs for classifying post-consumer textile waste. Their system categorized textiles into seven distinct classes, including cotton, wool, and polyester, achieving a classification accuracy exceeding 90% for fabrics that were composed of 100% of a single fiber. Despite its effectiveness, the system struggled with sorting blended textiles and fabrics which is a strong limitation given most of the consumer fashion and textile products are composed of blended fiber types.  
[PERSON] et al. [22] developed a hybrid CNN-LSTM model for textile classification, categorizing fabrics into four recycling pathways: mechanical, chemical, upcycling, and downcycling. The dataset included 10,000 labelled images from second-hand stores and fabric shops, as well as online. While reporting high accuracy, the model's reliance on balanced datasets may not reflect real-world imbalances in textile waste streams, which could limit its scalability in large-scale industrial recycling systems. However, the process of categorizing fabrics as mechanical, chemical, upcycling, and downcycling was not described in detail, and it is unclear what the criterion was for each. It was also unclear how the classification of the fabrics for the four different resulting pathways was determined, suggesting a knowledge gap between those investigating AI solutions and the practicalities of current system requirements within the F&T sector.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 5 Discussion
### Impact of Human Activities  
The ARB is home to the world's largest tropical rainforest. Its dense vegetation releases massive amounts of water into the atmosphere through transpiration: it is estimated that the Amazon rainforest releases up to billions of cubic meters of water into the atmosphere each day via this process. The moisture entering the atmosphere increases air humidity, providing sufficient water vapor conditions for precipitation formation. However, human activities in the ARB have become increasingly frequent in recent decades, particularly deforestation, which has led to a sharp reduction in the area of the Amazon rainforest. Data from Brazil's National Space Agency show that between August 2018 and July 2019, the deforested area of the Amazon rainforest reached 13,235 square kilometers, a year-on-year increase of 22%, marking the highest figure since the same period in 2005-2006 (14,286 km\({}^{2}\)) [73]. From 2019 to 2022, the situation of forest loss in the ARB deteriorated drastically, with the deforested area increasing by 75% compared to the average of the previous decade [74]. The disappearance of large areas of tropical rainforest has significantly weakened vegetation transpiration, leading to a substantial reduction in the amount of water released into the atmosphere by the Amazon rainforest through transpiration. This in turn has lowered atmospheric humidity, hindering precipitation formation and thereby exacerbating the severity of the drought.
# Bending Performance of Concrete Sandwich Walls with Actual Boundary Conditions
## 4 Finite Element Modeling
### Analysis of FEM Results  
The comparison results between the FEM and test results are demonstrated in Figure 13. It can be concluded that the load-displacement curve of FEM coincided well with the test results, indicating that the FEM was able to reasonably predict the bending performance of the concrete sandwich wall. ]  
Figure 14 presents the stress distributions of reinforcements in specimen WP. It showed that the reinforcement wire mainly appeared to yield in the middle span and its supports. The maximum stress was about 500 MPa. On the other hand, the steel truss parallel to the span direction exhibited yielding mainly in the middle span, and the maximum stress of the steel truss was 464 MPa, lower than that of the reinforcement wire. However, the steel truss perpendicular to the span direction showed relatively lower stress than the truss which was parallel to the span direction. Figure 15 displays the stress distributions of reinforcements of specimen WPO. It could be noticed that only a small portion of reinforcements in the wire achieved their yield stress, mainly located with the middle and 1/3 span near the opening. Comparatively, the reinforcements in steel truss parallel to the span direction showed higher stress levels with the maximum stress value of 439 MPa mainly distributed on the web reinforcements. Moreover, the strengthen reinforcements around the opening showed relatively high stress levels, indicating that the stress concentration appeared around the opening. It could be concluded that the reinforcements in the concrete sandwich walls did not all reach their yield strength, indicating that the two layers of concrete could not work together fully in the concrete sandwich wall. Therefore, it is necessary to discuss the composite action between the two layers of the concrete for the concrete sandwich wall.  
Figure 12: Finite element model of sandwich wall with reasonable mesh sizes. (**a**) Concrete layer. (**b**) Reinforcements.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 4 Simulation Results
### Hidden Layers  
The hidden layer structure is depicted in Figure 2. Three types of hidden layers were compared. Type 1 features a three-layer structure with an input layer, two hidden layers, and an output layer, each with 16 and 32 nodes. Types 2 and 3 are five-layer structures with input and output layers, along with four hidden layers. The nodes in type 2 are (16, 32, 48, 32), while in type 3, they are (32, 128, 64, 20). \(\Delta_{SNR}\) represents the difference in SNR between two user signals at the receiving end. When \(\Delta_{SNR}=5\) dB or \(\Delta_{SNR}=7\) dB, Figures 3 and 4 show the BER performance of them.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline \(N\) & 2 \\ \(K\) & 2 \\ Modulation Mode & QPSK \\ Hidden Layers & 2 \\ Trainnig Sample Size & 200,000 \\ Regularization Parameter \(\lambda\) & 0.01 \\ Learning Rate \(\alpha\) & 0.02 \\ Batch Size & 100 \\ Epoch & 100 \\ \hline \end{tabular}
\end{table}
Table 1: Training setting in numerical tests.  
From Figures 3 and 4, networks with five hidden layers perform better than networks with three hidden layers. When the network structures are identical, those with more neuron nodes exhibit better performance. Nevertheless, the performance difference between three-layer and five-layer network structures is not significant. Considering computational complexity and energy consumption, we opt for the three-layer structure.