esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hydrogen Production Technologies
### Background Concepts
#### 3.1.4 Biomass  
Being a renewable organic resource, biomass usually includes forest and agriculture crop residues and animal and other organic solid waste [47], and it can be used to produce hydrogen, along with other byproducts, by gasification. As seen below, in Equation (4), this process converts organic carbonaceous materials into carbon mono-/ dioxide and hydrogen, at high temperatures, without combustion and with a controlled amount of oxygen or steam intake.  
\[\mathrm{C}_{6}\mathrm{H}_{12}\mathrm{O}_{6}+\mathrm{O}_{2}+2\,\mathrm{H}_{2} \mathrm{O}\ \xrightarrow{\mathrm{}}2\,\mathrm{CO}+4\,\mathrm{CO}_{2}+8\,\mathrm{H}_{2} \tag{4}\]  
_Note: Actual biomass has a highly variable composition and complexity, with cellulose being one major component; the reaction above uses glucose as a substitute._  
Carbon monoxide then reacts with water to form more carbon dioxide and more hydrogen via a water-gas shift reaction (Equation (3)), and special membranes separate the hydrogen from this gas stream.  
Pyrolysis is a particular type of biomass gasification technology that uses no oxygen. This is because, in general, biomass does not gasify as easily as coal, producing other hydrocarbon compounds in the gas mixture exiting the gasifier.  
As a result, an extra step must typically be taken to reform these hydrocarbons to yield a clean syngas mixture of hydrogen, carbon monoxide, and carbon dioxide. Then, just as in the gasification process for hydrogen production, a shift reaction happens (with steam) that converts the carbon monoxide to carbon dioxide--hydrogen is produced and then separated and purified.
## 2 Design and Fabrication
### Fabrication of the RSOA  
The schematic of the RSOA structure is shown in Figure 2, with the names of each part of the epitaxial structure marked in black font. From bottom to top, the epitaxial structure of the device includes the N-type InP substrate, N-InP cladding layer, lower waveguide layer, quantum well active region, upper waveguide layer, P-InP cladding layer, and InGaAs ohmic contact layer. The active region consists of three strained InGaAsP quantum wells, each with an 8 nm thickness. The use of aluminum-free InGaAsP for the active region helps to extend the lifespan of the RSOA.  
To achieve stable spontaneous emission in the RSOA, in addition to depositing a coating layer with extremely low reflectance on the output facet, the optical waveguide can also be designed with an inclined or curved shape [47]. According to Reference [48], the light-emission angle of the RSOA chip is 7\({}^{\circ}\). The completed chip is shown under a microscope in Figure 3. During the fabrication of the RSOA, a double-ridge waveguide  
Figure 2: Schematic of the RSOA’s structure.  
structure, as illustrated in Figure 2, was formed using photolithography and etching processes, with an etching depth of 2.1 \(\upmu\)m. A 300 nm SiO\({}_{2}\) insulating layer was then deposited over the entire upper surface of the chip using plasma-enhanced chemical vapor deposition (PECVD). Through photolithography and etching, the SiO\({}_{2}\) dielectric layer above the ridge waveguide was removed to form the electrical injection window. The P-side metal layer (Ti/Pt/Au) was deposited via magnetron sputtering. The substrate was subsequently thinned to approximately 140 \(\upmu\)m and metallized. Finally, the facet coatings of the RSOA were completed using cleaving and sputtering processes. A wideband low-reflectance coating was applied to the front facet, while a high-reflection coating was applied to the rear facet.
## 4 Key Indicators Measurement and Data Description  
### Green Technology Innovation  
Currently, there are two core means of measuring green technology innovation: including input and output indicators at the same time, or only using a single output indicator. The input-output dual index model generally takes the productivity of both input and output as the proxy variable of green innovation evaluation with the consideration of the restraints in resources and environment. For example, [PERSON] et al. [57] divided the input variables into the input of research and development laborers, capital investment of research and development, and input of energy, and set the output variables as expected output and unexpected output. Then, they employed the improved super-SBM (Slack Based Measure) DEA (data envelopment analysis) model to measure the productivity of green innovation. However, [PERSON] and [PERSON] [63] pointed out that the inclusion of input indicators and output indicators at the same time would lead to the double counting of technological innovation measures. Regarding the method of using a single output indicator, which generally adopts the patent data, although there are some flaws such as ignoring the quality and economic impact of innovation, this approach has been considered reasonable by many scholars [64; 65; 66; 67] because the change in the number of patents is determined by knowledge indicators such as investment in research and development and scientific research personnel, and patents the advantages of availability, measurability, and comparability in both time and space. In addition, it is noticeable that the examination process of invention patents is very strict, often taking 1 to 2 years to obtain authorization, and it is easily influenced by external issues such as the efficiency and preference of the patent licensing agency. Furthermore, the number of invention patent applications is more suitable to denote the current capabilities of regional green technology innovation. Hence, the quantity of green patent applications in prefecture-level cities per 10,000 people is finally used in this article as a proxy variable for green technology innovation. It is the sum of the number of patents, green invention patent applications, and green utility model patent applications.
## 3 Results and Discussion
### Comparative Study between UV and Vis Light  
Finally, once the optimal operating parameters were verified, a comparative study was performed, whereby the same experiment was conducted using UV-Vis light. The turbidity and COD removal percentages obtained under UV-Vis irradiation are lower than those obtained under UV irradiation. The optimal responses achieved for this were 25.58% COD removal and 66.88% turbidity removal. This is possibly due to TiO\({}_{2}\) not being photoexcited by visible light due to its large energy band gap [22], and this is in line with the literature [45]. Due to turbidity caused by TiO\({}_{2}\) particles, additional post-treatment separation methods are required. Further studies making use of TiO\({}_{2}\) as a catalyst for photocatalytic wastewater treatment should include additional post-treatment filtration steps to recover the TiO\({}_{2}\) to ensure optimal removal of the nanoparticles and to enhance turbidity and COD removal.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 3 Challenge: Data Quality
### Semi-Supervised Learning
#### 3.2.3 Graph-Based Methods  
Datasets can be represented as graphs, where the nodes are the data samples, and the edges reflect the numerical distances between samples and even more information describing their relations, such as communications between the nodes. Graph-based SSL methods are normally based on the assumption that the close samples in the graphs should hold the same labels.  
[PERSON] et al. propose to learn the graph embeddings and then exploit them as inputs to the DNN classifiers [73]. The embeddings are the representations or encodings of the graph properties, e.g., node features that include the node attributes and the relationships with the neighbors. The graph embeddings are learned through enforcing the DNNs to predict the node context, i.e., neighboring node features, to enable the DNNs to capture the relations between the neighboring nodes. [PERSON] et al. apply such graph embedding learning SSL methods to steel plant energy prediction in [74]. The performance can increase up to 0.15 with the model correlation coefficient (it is MCC, which is used to represent the similarity between two variables) metric.  
Figure 8: Illustration of SSL with a generative model. All samples are mapped to the latent space (colored points indicate labeled samples from different classes, whereas uncolored points are unlabeled samples). The unlabeled samples are classified according to their distances (dashed lines in the graph) to the labeled samples (cf. clustering). The example point should be green, as the closest labeled neighbors are green.  
[PERSON] et al. apply graph neural networks (GNNs) for semi-supervised node classifications, in which the GNN inputs are the sub-graphs (part of nodes and their edges) and the outputs are the predicted labels of the nodes, as shown in Figure 9. GNNs are neural networks that are specifically applied to graph processing and good at representing the node features, edges, and sub-graphs. The model is trained in a supervised fashion through minimizing the wrong predictions for the labeled nodes.  
SSL is essential in industrial deep learning applications since the labels are often costly to obtain. Graph-based SSL methods have wider application domains since the datasets can be not only individual instances but also natural explicit graphs, e.g., communication networks, in which there are information exchanges between the nodes.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 4 Experiments  
### Experimental Settings  
This study utilizes the PyTorch 1.7.1 deep learning framework to construct the model and implements the complete training and inference pipeline in Python 3.9.7. To enhance the model's generalization ability and training performance, we preprocess the images before training, applying random cropping, rotation, and normalization. The images are resized to 360 \(\times\) 360 \(\times\) 3. The network is trained and evaluated on an NVIDIA GRID V100 GPU, with a batch size of 2 and a total of 100 training epochs.  
To validate the effectiveness of our method, we conduct evaluations on three different datasets: Div2k [40], MS-COCO [41], and CelebA [42]. Div2k, commonly used for super-resolution image reconstruction tasks, consists of 1000 high-resolution images. We randomly select 800 images for training, 100 for validation, and 100 for testing. For MS-COCO and CelebA, we randomly select 1000 images for training, 1000 for validation, and 200 for testing for each dataset.  
The secret information is represented by randomly generated binary vectors. In practical applications, these vectors can be replaced with encoded text or binary images. By tuning the hyperparameters of the loss function, we can effectively control the contribution of different loss components to the total loss, thereby accelerating model convergence.  
In order to analyze the impact of the loss function hyperparameters \(\alpha\) and \(\beta\) and the number of iterations T on the model performance, we performed a sensitivity analysis, as shown in Table 2. The experimental results indicate that the larger the value of \(\alpha\) (e.g., 100), the better the image quality, but the decoding error rate increases. In addition, although increasing the number of iterations T helps to reduce the decoding error rate, too many iterations have a limited effect on improving the image quality instead of increasing the computational cost and affecting the efficiency of the model. In order to strike a balance between image quality, decoding accuracy, and computational efficiency, the model achieves the best performance with T = 10, step size \(\eta\) = 1, attenuation coefficient \(\gamma\) = 0.8, and loss weight \(\alpha\) = \(\beta\) = 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**(\(\alpha\), \(\beta\), T)** & **PSNR** & **SSIM** & **Error Rate** \\ \hline (1, 1, 10) & 38.92 & 0.95 & 0.00 \\ (0.1, 1, 10) & 36.23 & 0.93 & 0.00 \\ (100, 1, 10) & 40.86 & 0.97 & 0.18 \\ (100, 1, 20) & 40.24 & 0.95 & \(5\times 10^{-3}\) \\ (1, 10, 10) & 37.68 & 0.92 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance with different settings of the relative weights of each item in the loss function.
## 2 Numerical Calculation Methodology
### Numerical Schemes and Boundary Conditions  
A numerical simulation of the fan flow field was conducted using the Moving Reference Frame model available in the commercial software FLUENT v2023. The SST \(k\)-\(\omega\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Front impeller blade number & 14 \\ Rear impeller blade number & 13 \\ Blade tip clearance & 1.5 mm \\ Front impeller angular velocity & 2960 r/min \\ Rear impeller angular velocity & 2960 r/min \\ Impeller diameter & 710 mm \\ Hub diameter & 387 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of impeller.  
Figure 1: Schematic diagram of the numerical calculation domain for the ventilation fan.  
turbulence model was employed for this analysis due to its widespread application in fluid machinery simulations and enhanced capability to predict flow separation, vortex structures, and recirculation phenomena [22; 23; 24]. The SST \(k\)-\(\omega\) turbulence model mathematical formulation is presented as follows:  
\[\mu_{t}=\frac{\rho a_{1}k}{\max(a_{1}\omega,SF_{2})} \tag{1}\]  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_{i }}=P_{k}+\frac{\partial}{\partial x_{j}}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{k}} \bigg{)}-\rho\beta^{*}k\omega \tag{2}\]  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho \omega u_{i})=\alpha\frac{\omega}{k}P_{k}+\frac{\partial}{\partial x_{j}} \Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{\omega}}\bigg{)}\frac{\partial \omega}{\partial x_{j}}\Bigg{]}-\rho\beta\omega^{2}+2(1-F_{1})\rho\frac{1}{ \omega\sigma_{\omega,2}}\frac{\partial k}{\partial x_{j}}\frac{\partial \omega}{\partial x_{j}} \tag{3}\]  
\[\sigma_{k}=\frac{1}{F_{1}/\sigma_{k,1}+(1-F_{1})/\sigma_{k,2}} \tag{4}\]  
\[\sigma_{\omega}=\frac{1}{F_{1}/\sigma_{\omega,1}+(1-F_{1})/\sigma_{\omega,2}} \tag{5}\]  
where \(\mu_{t}\) is the turbulent viscosity, Pa-s; \(\mu\) is the dynamic viscosity, Pa-s; \(P_{k}\) is the generation rate of turbulent kinetic energy, kg/(m-s3); \(F_{1}\) and \(F_{2}\) are the mixing functions; \(k\) is the turbulent kinetic energy, m2/s2; \(\omega\) is the turbulent specific dissipation rate, s-1; \(\sigma_{k}\) and \(\sigma_{\omega}\) are the Prandtl numbers of \(k\) and \(\omega\), respectively; \(S\) is the constant term of the shear stress tensor; \(a_{1}=0.31\); \(\sigma_{\omega,2}=1.168\); \(\alpha=0.52\); \(\beta^{*}=0.09\); \(\beta=F_{1}\beta_{i,1}+(1-F_{1})\beta_{i,2}\); \(\beta_{i,1}=0.075\); \(\beta_{i,2}=0.0828\); \(\sigma_{k,1}=1.176\); \(\sigma_{k,2}=1\); \(\sigma_{\omega,1}=2\); and \(\sigma_{\omega,2}=1.168\).  
The inlet boundary condition was set as a pressure inlet with a relative pressure of 0 Pa. The outlet boundary was defined as a mass flow outlet, with the mass flow rate value derived from experimental data. No-slip wall conditions were applied to all walls. The solution method utilized a second-order scheme, and an interface was used to connect the rotating and stationary domains for data transfer. The residual convergence criterion was set at \(10^{-5}\).
# Metal Artifact Reduction in X-ray CT via Ray Profile Correction
###### Abstract  
In computed tomography (CT), metal implants increase the inconsistencies between the measured data and the linear assumption of the Radon transform made by the analytic CT reconstruction algorithm. The inconsistencies appear in the form of dark and bright bands and streaks in the reconstructed image, collectively called metal artifacts. The standard method for metal artifact reduction (MAR) replaces the inconsistent data with interpolated data. However, sinogram interpolation not only introduces new artifacts but it also suffers from the loss of detail near the implanted metals. With the help of a prior image that is usually estimated from the metal artifact-degraded image via computer vision techniques, improvements are feasible but still no MAR method exists that is widely accepted and utilized. We propose a technique that utilizes a prior image from a CT scan taken of the patient before implanting the metal objects. Hence, there is a sufficient amount of structural similarity to cover the loss of detail around the metal implants. Using the prior scan and a segmentation or model of the metal implant, our method then replaces sinogram interpolation with ray profile matching and estimation, which yields much more reliable data estimates for the affected sinogram regions. Experiments with clinical dataset obtained using surgical imaging CT scanner show very promising results.  
metal artifact reduction; computed tomography; prior; ray profile Article
## 1 Introduction
It is not ideal for clinicians or coaches to be burdened with trying to attach devices in a specific orientation with a high level of precision. This is also demonstrated where different operators are present (i.e., inter-operator error), as well as errors associated with removing and reattaching measurement devices (i.e., pre-post intervention/follow-up data collection attachment error). Therefore, the effects of different sensor attachment orientations on error amount to an important consideration. This is particularly important for 'before and after' comparisons, where confidence is needed in any observed change representing a true change of the human and not just an attachment error. To date, many studies have employed accelerometers to measure angles [20; 23; 24]. Despite this common use of accelerometers, it is not clear if initial sensor orientation angle has been considered in the quantification of the angles or ROM reported. For example, previously reported limits of agreement between gold standard methods and accelerometers, without correcting for initial orientation, range from \(-8.06^{\circ}\) to \(5.67^{\circ}\)[25] and \(-3.86^{\circ}\) to \(4.69^{\circ}\)[11], meaning that 95% of future paired observations are likely to fall between this range of error. These error ranges are large, and it is questionable as to whether this is a true reflection of the sensors' capabilities or a function of the initial sensor orientation as a source of error. Therefore, it is essential to understand the impact of the initial sensor orientation on the measurement of both peak angles and through-range angles. If this error can be quantified, clinicians, coaches, and researchers will be better able to understand the sensor's sensitivity in detecting change relative to the error. Moreover, if error can be corrected, then users can be confident that any values produced are correct and that these are immune to issues of attachment orientation. This study had the following aims:  
* Step 1: Systematically explore the effect of initial sensor orientation and the effect of movement velocity on the error of accelerometer-derived angles, including a method for predicting error based on initial orientation and velocity of motion. * Step 2: Explore the influence of a proposed algorithm designed to correct errors associated with initial sensor orientation. We hypothesize (a) that increasing initial sensor orientation will result in increased sensor error; (b) that increasing velocity will result in increased sensor error; and (c) that this can be reduced through mathematical correction.
# An Analysis of MPTCP Congestion Control
## 3 Path Selection  
A packet contains all the information required to reach a destination when it gets sent into a network. Network links used by a packet to move between routers/hops and reach a destination is referred to as a path. In the Internet, multiple paths can exist between a sender and a receiver. A sender must select a path to transfer new data onto [1].  
Originally, path selection took place at layer 3 (network layer). Path selection in layer 3 (routing) is made based on network conditions rather than endpoint and application requirements. Such decisions carry the risk of decreasing transport-layer performance. More recently, multi-homed devices have the ability to select the best path. For example, a path can be selected using dynamic scheduling and flow-control techniques [1].  
A multi-path scheduler can decide how much data and through which interface to distribute an application data stream [1]. Figure 1 shows a simple scheduler sending dataonto two different paths. As can be seen, the application sends data to the output queue. The scheduling mechanism selects the path through which the data gets transmitted.  
Having multiple paths can be beneficial depending on the available paths and their condition, whether they are disjoint or share a common bottleneck. End-hosts gather information about paths to help the scheduler make efficient decisions. Each layer might provide different information to the scheduler. For example, a scheduler can integrate with the transport layer and congestion-control algorithm to check available congestion on each path as well as the RTT to select a transmission path [1].