esa-sceva/satcom-chunk-collection huggingface dataset:
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 7 Major Challenges and Barriers to Market
### Cost of Large-Scale Manufacturing  
Adequately recellularizing organ-sized constructs such as the liver or heart with enough cells to replicate native organ functionality will require billions of cells [196; 197]. For instance, clinically relevant contractile function in an engineered whole heart would require a quantity of cells on the scale of the 2-3 billion cardiac muscle cells found in the native heart, and even then that is less than a third of the number of total cells in the heart [197]. In addition to the cost of the cell and relevant culturing equipment, large-scale manufacturing of tissue using conventional tissue engineering techniques would require a large number of personnel that would need to be trained in cell culture techniques, requiring time and money. An approach that may be able to improve upon conventional tissue culture methods and expand them to large-scale manufacturing is the use of advanced robotics in manufacturing. Not only could robotic manufacturing produce a larger scale of cells without the need for training personnel, but fabrication could be performed in a closed system, reducing the risk of contamination and thus further saving costs by mitigating the need to discard contaminated cells. In addition, using machine learning algorithms and imaging techniques, manufacturing robots could be trained to identify cells in a culture that have successfully undergone genetic modification or differentiation and then isolate them for expansion, improving the efficiency of stem cell-derived cells for tissue engineering. However, implementing novel manufacturing approaches in engineered tissues would require extensive regulations for developing protocols and investigating the safety of the process.  
A majority of the published preclinical research studies involving decellularization and recellularization have been unable to establish the cell numbers needed to restore original organ function. [PERSON] et al., were able to recellularize a liver with roughly 5-20% of the original native hepatic mass in their preclinical animal model [40]. [PERSON] et al. were able to repopulate a decellularized rat liver with roughly 20 million iPSCs, which were able to differentiate into hepatic cells [198]. However, this only corresponds to less than 5% of the whole rat hepatocyte cell count. The cost of these studies were thousands of dollars. In order to make whole organ engineering, these numbers would have to be scaled to orders of magnitude higher, which would be extremely expensive. In order to decrease contamination and improve functionality, more money would have to be spent on improving quality control metrics.
## 3 GPS SDR Design for CRPA Using General-Purpose Parallel Processors
### GPU-Based Parallel Correlator
#### 3.2.1 Memory Space on a CUDA Device  
Before explaining the correlator design in Figure 5, we briefly review the memory space on a CUDA device. Figure 6 illustrates a host computer and a graphics card, or \"device\", with the GTX 480 GPU. In order to process data in the memory of a host computer using GPU, the data must be copied over to the global memory of a device. The data bus for the GTX 480 GPU is PCI-E 2.0 \(\times\) 16 whose maximum bandwidth is 16 GB/s. Once the data is copied from host memory to device global memory, multiprocessors in GPU access the data in global memory for processing. The theoretical maximum global memory bandwidth of the GTX 480 GPU is 177.4 GB/s [44]. This number is calculated in Equation (6) based on its specification. Its memory interface width is 384 bit. Memory clock is 1,848 MHz and it is a double data rate (DDR) memory:  
\[384\,\mathrm{bit}\times\frac{1}{8}\mathrm{Byte}/\mathrm{bit}\times 1848\, \mathrm{MHz}\times 2=177.4\,\mathrm{GB}/\mathrm{s} \tag{6}\]  
Local memory is also placed in DRAM and it is for large structures and arrays or spilled registers. If a GPU code, or \"kernel\", requests more than available registers, those variables are placed in local memory.  
DRAM access has high latency due to limited bandwidth, but on-chip memory access is much faster. The latency of uncached on-chip shared memory is roughly 100 times lower than the latency of global memory (Section 3.2.2 of [43]). On-chip registers also have low latency. Therefore, a basic optimization strategy is to minimize high latency memory access such as host-to-device memory copy and global memory access. Memory latency is not usually a problem for CPU because it has a sophisticated and optimized cache hierarchy. GPU is optimized for high processing throughput instead and it has weakness on tasks requiring high memory access but low computational intensity. In terms of processing throughput, the theoretical maximum throughput of 32-bit floating-point operations on the GTX 480 GPU is 1.34 Tflops (Tera floating-point operations per second), which is about ten times higher than the throughput of modern CPUs (See Figure 1-1 of [36]). The theoretical maximum throughput of GTX 480 is calculated in Equation (7) based on its specifications [44]. Each CUDA core can perform one 16-bit multiply-and-add operation (_i.e._, two 16-bit floating-point operations) per clock cycle:  
\[480\,\mathrm{cores}\times 1401\,\mathrm{MHz}\times 2\,\mathrm{flop}/ \mathrm{core}/\mathrm{cycle}=1.34\,\mathrm{Tflops} \tag{7}\]
## 2 FE Model and Validation
### Influence of Shear Link Arrangement  
As shown in Figure 10, a uniformly distributed spring model (Figure 10a) and an integrated spring model (Figure 10b) were built to compare the influence of the shear link arrangement. Note that the spring in the integrated spring model is a superposition of the springs in the uniformly distributed spring model, i.e., \(k_{\text{ISM}}=\sum\limits_{i=1}^{n}k_{\text{UDSM}i}\) and \(F_{\text{yISM}}=\sum\limits_{i=1}^{n}F_{\text{yUDSM}i}\), where \(k_{\text{ISM}}\) and \(F_{\text{yISM}}\) represent the initial stiffness and yield strength of the spring in the integrated spring model, respectively, while \(k_{\text{UDSM}i}\) and \(F_{\text{yUDSM}i}\) represent the initial stiffness and yield strength of each spring in the uniformly distributed spring model, respectively; \(n\) is the number of springs.  
Comparing the results of the two models (Figure 11), it can be concluded that the shear link arrangement has little impact on the hysteretic performance of the hybrid bridge pier. The reason for this phenomenon is that the relative shear displacement of each shear link is subjected to the identical rigid-body motions of the columns. Therefore, the analysis and discussion of this study are based on the integrated spring model. Note that the integrated spring model is more convenient for computing, while uniformly distributed shear links will be adopted in reality to significantly reduce the difficulties in construction.  
Figure 10: Different spring models. (**a**) Uniformly distributed spring model; (**b**) integrated spring model.  
Figure 9: Column axis coordinates at maximum lateral displacement.
## 3 Holdback Bar Device Connection Relationship and Contact Mechanics Model
### Contact Mechanics Model  
In the dynamics simulation analysis software, various contact relations in the holdback bar are analyzed by the contact mechanics module. The contact model is used for holdback bar model building. The contact forces include: the normal force perpendicular to the contact surface, and the frictional force within the contact surface.  
The normal force model of the contact surface is developed using Hertzian contact theory [24], in which the normal contact force is described by the contact stiffness and the displacement of the contacting object under the force. Here, a rigid sphere in contact with an elastic plane body of radius R is first considered. The Hertzian pressure distribution on the elastic surface is  
Figure 4: Sketch of holdback bar connection relationship and kinematic pairs.  
\[p=p_{0}\bigg{(}1-\frac{x^{2}}{R^{2}}\bigg{)}^{1/2} \tag{7}\]  
When a cylinder with radius \(R_{1}\) and a cylinder with negative radius \(R_{2}\) are in parallel contact, as shown in Figure 5, the two cylinders in parallel contact are the same as two spheres in contact, and the relationship between the contact radius and the indentation deformation \(d\) is  
\[a\approx\sqrt{Rd}\qquad\text{where: }R=\frac{R_{1}R_{2}}{R_{2}-R_{1}} \tag{8}\]  
The relationship between the normal force \(F\) between the two cylinders and the pressure-in deformation \(d\) is linear, and \(k\) is the contact stiffness.  
\[F=\frac{\pi}{4}E^{{}^{\prime}}Ld=kd\qquad\text{where: }k=\frac{\pi}{4}E^{{}^{ \prime}}L \tag{9}\]  
The maximum normal contact pressure between two cylinders is:  
\[p_{0}=\frac{E^{{}^{\prime}}d}{2a}=\frac{\pi E^{{}^{\prime}}}{8}\bigg{(}\frac{ d}{R}\bigg{)}^{1/2} \tag{10}\]  
If both objects are elastomers, \(E_{1}\) and \(E_{2}\) are the modulus of elasticity of the two objects, and \(v_{1}\) and \(v_{2}\) are the Poisson's ratios of the two objects, then the expression \(E^{{}^{\prime}}\) can be modified as follows  
\[\frac{1}{E^{{}^{\prime}}}=\frac{1-v_{1}^{2}}{E_{1}}+\frac{1-v_{2}^{2}}{E_{2}} \tag{11}\]  
The Karnopp friction model is used in the friction model of the contact surface [25], as shown in Figure 6, which is a simplified model of viscous-slip friction motion, making the expression of friction at both dynamic and static states more simplified, and the calculation of this model is simple and in line with the actual situation of friction motion when solving engineering problems.  
When the velocity is within \(\pm\Delta V\), the friction model is expressed as static, or viscous, and when the velocity \(V_{r}>\pm\Delta V\), the model will switch from the viscous state to the sliding state, and the friction force in the sliding state is smaller than that in the viscous state.  
Figure 5: Two cylindrical objects in parallel contact.
## 2 Problem Description
### Assumptions  
In order to develop analyses and research quickly, we make the following assumptions:  
1. Visibility of Satellites and Ground Stations: Each satellite will maintain visibility with at least one ground station during its orbital period, ensuring that data transmission can be completed within the planned time window.
2. Uninterrupted Data Transmission: Communication between satellites and ground stations is assumed to be free from interference or blockages, ensuring the stability and continuity of data transmission.
3. Stable Power Supply: All satellites and ground stations are assumed to have a stable power supply. Specifically, satellites are considered to have sufficient power from batteries and solar panels to support long-duration continuous transmission tasks.
4. Independence of Tasks: Each data transmission task is independent, with no dependencies between tasks.
5. Constant Transmission Rate: The data transmission rate between satellites and ground stations is assumed to be constant, unaffected by environmental changes or equipment performance fluctuations, ensuring the predictability of task planning and execution.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 4 Our Proposed Architecture for Smart Solar Plant  
The conventional method of solar plant monitoring was either through manual inspection or a wired sensor network. The next revolutionary step was centralized IoT-based architecture. The performance of these solutions was seriously limited because of labor- and time-intensive work [51]. Furthermore, scalability, reliability, and cost were some of the other factors undermining the aforementioned solutions. However, recent advancements in technology (hardware, software, communication, and cloud computing) have made it possible to offer high-performance and cost-efficient monitoring systems. An IoT-based solar plant monitoring system consists of numerous geographically spread sensing devices. These devices sense their surroundings and transmit information periodically to a server for processing, storage, and analysis. Low cost, reliability, scalability, and security are  
Figure 2: This figure describes the general architectural layout of IoT-based solar plant monitoring systems. It illustrates the four primary layers: the perception layer, which consists of end devices; the network layer, which lists the communication technologies for transmitting the collected data; the support layer, which involves gateways and data management; the application layer, which offers data visualization and monitoring tools.  
some of the important considerations. For example, [PERSON] et al. [5] reported that a fully functional 500 MW solar plant will have 2,500,000 solar panels (with each panel rated to produce 200 W). Sensing each solar panel's current, voltage, and temperature will generate nearly 8 Gb/s of data. As a result, the transmission, management and storage of this huge amount of data can be a challenging task.  
Based on this context, a monitoring solution has been proposed based on edge computing paradigms. The proposed edge computing paradigm, integrating both IoT and cloud computing paradigms, is shown in Figure 3. At the perception layer, end devices are responsible for sensing each solar panel's electrical parameters. The data from the end devices are sent to the edge layer. The edge nodes are responsible for filtering the data from the end devices and fetching meteorological parameters. In the third layer, the cloud platform is responsible for data storage, processing, analysis, and information dissemination.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 3 Centroid Computation  
Once the salient region is acquired, the centroid of space debris in the CCD focal plane can be obtained by a simple energy-weighted average method. The overall aim of space debris detection is to compute the pixel coordinate of the centroid of space debris, which can be further used to evaluate the performance of the proposed object region extraction method. The principle of centroid computation is to assign different weights to the point-source pixels of the extracted salient region depending on the intensity. The weighted centroid algorithm is summarized as follows:  
Figure 5: Illustration of the constrained self-attention (CSA) network. The video clip includes four frames as examples in this figure.  
Figure 6: The space positions of space debris at different motion speeds. The space debris cross three consecutive frames at two different speeds, where \(v_{2}>v_{1}\).  
\[x_{o}=\frac{\sum\limits_{\begin{subarray}{c}x=m_{1},y=n_{1}\\ m_{2},n_{2}\\ \sum\limits_{x=m_{1},y=n_{1}}^{m_{2},n_{2}}\\ \sum\limits_{x=m_{1},y=n_{1}}^{m_{2},n_{2}}\\ \end{subarray}}xI(x,y)}{\sum\limits_{\begin{subarray}{c}x=m_{1},y=n_{1}\\ m_{2},n_{2}\\ \sum\limits_{x=m_{1},y=n_{1}}^{m_{2},n_{2}}\\ \end{subarray}}I(x,y)} \tag{5}\]  
\[y_{o}=\frac{\sum\limits_{\begin{subarray}{c}x=m_{1},y=n_{1}\\ m_{2},n_{2}\\ x=m_{1},y=n_{1}\end{subarray}}^{m_{2},n_{2}}yI(x,y)}{\sum\limits_{\begin{subarray}{ c}x=m_{1},y=n_{1}\\ m_{2},n_{2}\\ x=m_{1},y=n_{1}\end{subarray}}^{m_{2},n_{2}}I(x,y)} \tag{6}\]  
where \((x_{o},y_{o})\) is the weighted centroid coordinate of the pixel location \((x,y)\). \(x\) and \(y\) are the pixel locations of the salient region. \(I(x,y)\) is the pixel intensity value at the pixel location \((x,y)\). \(m_{1},m_{2},n_{1},n_{2}\) denote the pixel ranges of the salient region. The accuracy of the centroid computational method is sensitive to background noise [7]. However, this effect can be neglected because the saliency detection method outputs a segmentation mask from the background without any noise.
## 5 Experiments and Results
### Performance Comparisons between CWC with Different Parameters and PIMOC
As is shown in Figures 3-5, with the control parameter \(\eta\) increasing, the prediction accuracy of the PIs is improved, while the width is larger and the reliability becomes worse, which is just the same as in Table 1. An unreasonable choice of \(\eta\) may easily cause some excellent solutions unreserved in the next iteration. From Equation (5), it can be seen that, in CWC, the quality of the PIs is assessed impartially only based on a single synthetical criterion. While in PIMOC, some excellent solutions are reserved by the Pareto dominance strategy avoiding the choice of \(\eta\), and the assessment is carried out according to both PIMOC, both in accuracy and reliability. Then we can see that the performances of PIMOC in Figure 6 are better than all those of CWC in Figures 3-5. Figure 2: Comparative results between CWC and PIMOC. CWC: coverage width-based criterion; PIMOC: PIs’ multi-objective optimization criteria; PICP: PI coverage probability. Figure 4: Prediction intervals with CWC ABC-based when \(\eta=50\). CWC: coverage width-based criterion; ABC: artificial bee colony; PICAW: PIs covered-normalized average width; PICP: PI coverage probability. Figure 5: Prediction intervals with ABC-based CWC when \(\eta=100\). CWC: coverage width-based criterion; ABC: artificial bee colony; PICAW: PIs covered-normalized average width; PICP: PI coverage probability. Figure 3: Prediction intervals with CWC ABC-based when \(\eta=10\). CWC: coverage width-based criterion; ABC: artificial bee colony; PICAW: PIs covered-normalized average width; PICP: PI coverage probability.
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
###### Abstract  
The radiometric capability of on-orbit sensors should be updated on time due to changes induced by space environmental factors and instrument aging. Some sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have onboard calibrators, which enable real-time calibration. However, most Chinese remote sensing satellite sensors lack onboard calibrators. Their radiometric calibrations have been updated once a year based on a vicarious calibration procedure, which has affected the applications of the data. Therefore, a full evaluation of the sensors' radiometric capabilities is essential before quantitative applications can be made. In this study, a comprehensive procedure for evaluating the radiometric capability of several Chinese optical satellite sensors is proposed. In this procedure, long-term radiometric stability and radiometric accuracy are the two major indicators for radiometric evaluation. The radiometric temporal stability is analyzed by the tendency of long-term top-of-atmosphere (TOA) reflectance variation; the radiometric accuracy is determined by comparison with the TOA reflectance from MODIS after spectrally matching. Three Chinese sensors including the Charge-Coupled Device (CCD) camera onboard Huan Jing 1 satellite (HJ-1), as well as the Visible and Infrared Radiometer (VIRR) and Medium-Resolution Spectral Imager (MERSI) onboard the Feng Yun 3 satellite (FY-3) are evaluated in reflective bands based on this procedure. The results are reasonable, and thus can provide reliable reference for the sensors' application, and as such will promote the development of Chinese satellite data.  
radiometric capability; reflective bands; CCD; VIRR; MERSI; radiometric stability; radiometric accuracy +
Footnote †: journal: Sensors
## 2 Experimental Research and Analysis  
### The Establishment of the End Connection Cable Model  
In this paper, a single-core silicone rubber insulated cable with a cross-section of 240 mm\({}^{2}\) is simulated and studied. The specific layout and structure size of the cable box connected to the end of the permanent magnet wind turbine are shown in Figure 1.  
Figure 1 shows that there are two junction boxes: junction box 1 and junction box 2. Among them, 1U1 represents the first set of windings, the number of winding phases is the U (A) phase, and the first cable of this phase 1U2 is a cable in parallel with 1U1.  
Under the action of a high-frequency PWM wave, the wavelength of the pulse wave \(\lambda=v/f\) is smaller than that of the fundamental frequency (\(v\) is the transmission speed of the voltage wave, \(f\) is the frequency of the voltage wave). When the output end of the inverter is connected to the motor by cable, the cable length cannot be ignored or compared with the voltage wavelength, and the distributed parameters of the cable must be considered. The equivalent circuit of single-phase cable is shown in Figure 2. Among them, \(R_{0}\) is the resistance of unit length wire, \(L_{0}\) is the inductance of unit length wire, \(G_{0}\) is the insulation resistance of unit length, and \(C_{0}\) is the capacitance of unit length wire to ground.  
In the case of high frequency, due to \(R_{0}\ll\omega L_{0},\;\;G_{0}\ll\omega C_{0},\;R_{0}\) and \(G_{0}\) can be ignored and the uniform line can be regarded as a lossless line. For the lossless transmission line, the equivalent circuit diagram is shown in Figure 3:
## 4 Experiments
### Comparison with Previous Methods
#### 4.3.2 Long-Term Prediction  
Long-term prediction refers to forecasting within the range of (400, 1000) milliseconds, with a prediction interval of 40 milliseconds. Table 3 presents the performance of our model compared to other methods in long-term prediction tasks. A more recently method, termed CIST-GCN (Winter Conference on Applications of Computer Vision, WACV, 2024) [45], is introduced to replace AHMGCN. Our model evidently achieves excellent performance.  
The more recent CIST-GCN enhances long-term performance through context-aware graph convolutions; however, its average MPJPE at 560 ms is 77.8 mm, which is still higher than our 71.9 mm. The difference arises from the following factors: (1) CIST-GCN does not explicitly model dynamic features; and (2) our KD-fused mechanism dynamically adjusts the weights of kinematic and dynamic information via gating units, thereby mitigating long-term error accumulation. However, the prediction performance at 1000 ms is not as prominent. We will analyze the reasons for this in detail in Section 4.4.1.  
Figure 3 illustrates how the MPJPE changes over prediction timesteps--ranging from the short-term of [40 ms, 400 ms] out to the long-term of [440 ms, 1000 ms]--for 15 different actions in the Human3.6M dataset. The horizontal axis indicates the predicted future time (ms), while the vertical axis shows the MPJPE (in millimeters) at each prediction horizon. Each colored curve corresponds to a different action, and the black dashed line represents the overall trend obtained by averaging across all 16 actions. For short-term prediction (40 ms-400 ms), within this shorter range, the MPJPE rises only gradually for most actions, and the error differences between different actions remain relatively small. This pattern suggests that when sufficient historical trajectory information is available, our model can accurately capture short-term movements. Consequently, it can handle a variety of typical or simpler actions (such as Waiting or Phoning) with high fidelity.  
For long-term prediction (440 ms-1000 ms), as the prediction horizon extends, the overall error curves trend upward; however, the errors for different actions begin to diverge more noticeably. For instance, actions like Sittingdown or Discussion, which involve extensive posture changes or higher complexity, exhibit a sharper rise in MPJPE after about 600 ms. Looking at the black dashed line (the average across all actions), one can see that our method effectively curtails the rapid accumulation of error in the long-term range, resulting in a comparatively milder increase in MPJPE than many existing approaches.  
Figure 3: MPJPE of different actions at all prediction timesteps of our model.  
This performance is largely attributable to the dual-branch modeling of structural (joint) and dynamic features, as well as the adoption of a KD-fused Graph-GRU designed to capture both the spatial relationships within the skeleton and the temporal progression of the motion.