esa-sceva/satcom-chunk-collection huggingface dataset:
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 4 System Structure  
SMT assembly defects can be classified by CNN, a deep neural network that has shown high performance in image recognition and processing. Figure 4 shows the structures of CNN for SMT assembly defect classification. In both cases, the probability \(P(d_{i})\in[0,1]\) of the occurrence of each defect type \(d_{i}(\mathrm{i}=1,\cdots,k)\) can be obtained. The defect type \(d^{*}\) for the input image is defined as:  
\[d^{*}=\underset{d_{i}\in D}{\mathrm{argmax}}P(d_{i}) \tag{5}\]  
Figure 4a is an assembly defect classification system having a single-stream CNN that uses the color channel images (\({}^{I}R\), \({}^{I}G\), and \({}^{I}B\)) of each chip component image \(I\) as inputs. Although the chip component image includes both the package and solder regions, it cannot exclude the silk line background. Figure 5 shows the PCB patterns included in the background region of the chip component image.  
Figure 4b shows the structure of the assembly defect classification system with the dual-stream CNN. The inputs included the two solder region images (\(S_{1}\in\ \mathbb{R}^{P\times 3Q}\) and \(S_{2}\in\ \mathbb{R}^{P\times 3Q}\)) extracted from the chip component image \(I\), and the color channel image (\({}^{S_{1}}R\), \({}^{S_{1}}G\), and \({}^{S_{1}}B\); and \({}^{S_{2}}R\), \({}^{S_{2}}G\), and \({}^{S_{2}}B\)). Note that the solder region image does not include the silk line, which disrupts the feature extraction of CNN.  
Figure 4: Input and output structures of convolutional neural networks (CNNs) for defect classification: (a) single-stream CNN; (b) dual-stream CNN.  
Figure 5: PCB background patterns in chip component images. The silk line is printed on the PCB to provide additional information about the component. Silk lines can adversely affect defect classifications because of their similarity to solder region.  
As described in the Introduction, most defect features are distributed in the solder region in the component, and as such the two solder regions can be used to classify both the package and solder defects. Figure 6 shows the solder region image based on defect type, wherein the defect feature is distributed in the solder region. Removing unnecessary PCB patterns can reduce the chance of misclassification in solder region extraction, and thereby increase its accuracy. In addition, the size of the chip component image can also be reduced. This optimization simplifies the CNN internal structure, reducing the overall weight capacity and computational time.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements
#### 4.2.10 Calculation of Playout Time for Data Packets  
\[t_{p}=t_{s}+d_{off} \tag{7}\]  
In Figure 8 there are two receivers, both are having the same playout time although the packet received time is different but received time does not matter as the actual playout time calculation is based on sent time and the end to end delay offset. Sent time and delay offset are same for all the receivers. The varying values are received time, one-way delay and idle time. The variation of received time and one-way delay is compensated by the idle time.
# Construction of Nonblocking Wavelength/Space Switches with AWGs and WSSes
## 4 Space-Wavelength Tradeoffs
### Hardware Complexity  
We compare the number of WCs, AWGs, and space switches required in the AWG-based and the WSS-based architectures. Among them, WCs are perhaps the most costly. Assume \(n\), \(r\), and \(N\) are given, the exact formula for the number of WCs required can be derived as follows.  
1. AWG-based approach presented in this paper: In the decomposed topology, there are \(2s+1\) stages of WC modules, and each stage has \(nr\) WCs, where \(r\) can be any of \(k_{1}\times k_{2}\times\ldots\times k_{s}\) for \(k_{i}\in\{1,2,3,\ldots,N\}\), \(1\leq i\leq s\). In total, the number of WCs is \((2s+1)\cdot nr\).
2. WSS-based approach with \(n<r\): In the topology, there are \(r/n\) WSSes of size \(n\times n\) in the middle stage, and each such \(n\times n\) WSS uses 2-(\(n-1\)) WC modules of size \(n\times n\), where each \(n\times n\) WC module uses \(n\) WCs. In total, the number of WCs is \((r/n)\cdot 2(n-1)\cdot n=2r\cdot(n-1)\).
3. WSS-based approach with \(n\geq r\): In the topology, there are 2-(log, \(n\)) stages of WC modules, and each stage has \((r-1)\) WC modules of size \(n\times n\), where each \(n\times n\) WC module uses \(n\) WCs. In total, the number of WCs is \(2\cdot(\log,n)\cdot(r-1)\cdot n\).  
We select a switch configuration that can be constructed by both approaches. Table 1 tabulates the results for the case of \(n=64\), \(r=64\), and \(N=32\). Under this case, the AWG-based approach requires five stages to construct and it requires many more WCs than the WSS-based approach. Note that the number of space switches required in the WSS-based approach is based on the implementation shown in Figure 8. The number of WCs required, however, is not affected by the technologies used to implement the WSS.  
Table 2 tabulates the results for the case of \(n=64\), \(r=8\), and \(N=32\). In this case, the selected \(r\) is much smaller. Under this condition, the AWG-based approach will perform much better, in terms of the WCs required. This is because the number of stages needed in the AWG-based architecture is only three. However, the WSS-based approach requires more stages to be implemented.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(n=64\), \(r=64\), \(N=32\) & AWG-Based Approach & WSS-Based Approach \\ \hline Number of WCs & 24,576 & 8064 \\ Number of 32 \(\times\) 32 AWGs & 8 & 0 \\ Number of 32 \(\times\) 4 AWGs & 32 & 0 \\ Number of 4 \(\times\) 32 AWGs & 32 & 0 \\ Number of 64 \(\times\) 64 space switches & 0 & 192 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the AWG-based approach and WSS-based approach with \(n=64\), \(r=64\), \(N=32\).
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 1 Introduction
Microgravity can be transiently achieved with several non-orbiting facilities based on free-fall, which include drop towers, parabolic flights, and small rockets, which are widely used for short-term experiments. Long-term microgravity experiments can be performed on orbiting facilities, despite the constraints and limitations of spaceflight missions. The first modular Space station was the Russian Mir, which was deorbited in 2001, and, for the past two decades, scientists have been able to use the ISS. Smaller orbiting facilities have been available since 1957 (i.e., biosatellites) and, more recently, CubeSats have been used to test hypotheses in constant, real microgravity. Each of these platforms is preferably used in specific biological systems. This is determined by experimental design factors, like level and quality of microgravity, duration, and sample size limitations, that must be taken into consideration when designing experiments and interpreting results. The aim of this review is to give a general overview of the currently available facilities for the study of microgravity. An attempt was made to critically discuss the advantages and limitations of each class of facilities, giving recommendations for appropriate selection of samples and experimental design. In particular, we highlighted the practical aspects of biological payload integration on the ISS.
## Appendix C Proof of Theorem 3  
From expression (49), IP at eavesdropper \(E\) is rewritten as follows:  
\[IP_{E\to D_{n}}(t)=1-\Pr\biggl{\{}\min\biggl{\{}\min_{i=\{N, \ldots,n\}}\biggl{\{}R_{R_{r}-x_{i}}^{\gamma(t)}\biggr{\}},\min_{i=\{N,\ldots,n\}}\biggl{\{}R_{E-x_{i}}^{\gamma(t)}\biggr{\}}\biggr{\}}\geq\mathcal{R} \biggr{\}}. \tag{13}\]  
After the algebraic transformation, we obtain IP at eavesdropper \(E\) as follows:  
\[IP_{E\to D_{n}}(t)=1-\min_{i=\{N,\ldots,n\}}\biggl{\{}\Pr\biggl{\{}|h_{S,R_{ r}}|^{2}\geq\frac{\gamma}{(1-\lambda_{R_{r}})\beta_{n}\rho}\biggr{\}},\] \[\quad\quad\quad\Pr\Biggl{\{}|h_{R_{r},E}|^{2}\geq\frac{\gamma \Bigl{(}\delta\eta\lambda_{R_{r}}\rho\sigma_{S,R_{r}}|h_{R_{r},E}|^{2}+1\Bigr{)} }{\eta\lambda_{R_{r}}\rho\sigma_{S,R_{r}}\beta_{n}},|h_{R_{r},E}|^{2}\geq 0 \biggr{\}}\Biggr{\}}\] \[=1-\min\Biggl{\{}\int\limits_{\frac{\gamma}{(1-\lambda_{R_{r}}) \beta_{n}\rho\sigma_{S,R_{r}}}}^{\infty}\frac{1}{\sigma_{S,R_{r}}}\exp\biggl{(} -\frac{x}{\sigma_{S,R_{r}}}\biggr{)}dx,\] \[\quad\quad\quad\int\limits_{0}^{\infty}\int\limits_{\frac{\gamma }{(\eta\lambda_{R_{r}}\rho\sigma_{S,R_{r}+1})}{\sigma_{R_{r},E}\sigma_{R_{ r},E}}}^{\infty}\frac{1}{\sigma_{R_{r},E}\sigma_{R_{r},E}}\exp\biggl{(}-\frac{x}{ \sigma_{R_{r},E}}-\frac{y}{\sigma_{R_{r},E}}\biggr{)}dxdy\Biggr{\}}\] \[=1-\min\Biggl{\{}\underbrace{\exp\biggl{(}-\frac{\gamma}{(1- \lambda_{R_{r}})\beta_{n}\rho\sigma_{S,R_{r}}}\biggr{)}}_{K},\frac{\beta_{n} \sigma_{S,R_{r}}\sigma_{R_{r},E}}{\frac{\beta_{n}\sigma_{S,R_{r}}\sigma_{R_{ r},E}+\delta\gamma\sigma_{S,R_{r}}\sigma_{R_{r},E}}}_{U}\exp\biggl{(}-\frac{ \gamma}{\eta\lambda_{R_{r}}\rho\beta_{n}\sigma_{S,R_{r}}\sigma_{R_{r},E}} \biggr{)}_{V}\Biggr{\}}. \tag{14}\]  
Observing expression (14), IP at eavesdropper \(E\) referred to \(IP_{E\to D_{n}}(t)=1-\min\{K,UV\}=1-UV\) since the impact of the jamming signal. IP at eavesdropper \(E\) then tends to floor \(IP_{E\to D_{n}}(t)=1-\min\{K,UV\}=1-U\) since \(V\to 1\) when SNR \(p\to\infty\). The major aim of this study is to improve secrecy performance for IoT network. It means that expression \(U\) in (14) needs to tend to zero (\(U\to 0\)). To reach this aim, the study proposed a pre-defined bit-rate threshold as given (54) and a PS factor as given (55). As a result, the IP at eavesdropper \(E\) tended to one approximately at almost SNR as shown in Figure 5b.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 3 Results
### Complete Tag
#### 3.2.1 Low Power Consumption Scenario  
The results of this test scenario are shown in Figure 7. In Figure 7a, the voltage drop over time can be seen, and is represented until the working limit of 2.7 V. Figure 7b shows the current associated to the low power mode and Figure 7c shows the transitions associated to the current when this scenario is running.  
Figure 6: The voltage in the supercapacitor collected with mechanical energy. For every time that we take the measurement, there is a value for SYS (yellow points), a value for BAT (light green line) and the state of the device, called System On/Off (blue line). The system never turns on because neither SYS nor BAT reach the 2.7 V needed, which is marked by the orange line.  
Figure 7: Tag power consumption measurements in the low power consumption scenario. (**a**) The voltage drop in supercapacitor without external source. (**b**) Extraction of the low power mode and the average of the current consumption. Here it can be seen that this mode lasts the 0.5 s. (**c**) Extraction of the first 20 s of graphic (**a**), but in this case, the current draw of the tag is represented. The peaks represent when the transceiver changes to reception mode every 0.5 s.  
With a voltage of 4.2 V in the supercapacitor, the consumption in reception mode is around 7 mA and the low power mode is around 31.85 \(\upmu\)A, which, considering the theoretical values, are the expected values. In terms of energy, taking into account that, in our test, the time in low power mode is 0.5 s, the energy needed for the low power mode is 66.89 \(\upmu\)J. The peaks of reception use 14.7 mJ for the other 0.5 s.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 5 Future Recommendations  
### Distributed Temperature Sensing  
The OBS techniques explored so far have been designed for a point or single-unit junction temperature measurement. A typical commercial power circuit contains two to tens of IGBTs or SiC MOSFETs to capture the semiconductor devices, where distributed temperature sensing (DTS) could be a viable solution for such applications. Among the three optical techniques discussed in Section 3, the FBG technique is the most suitable approach for DTS of power switching circuits due to its size, maintainability, and overall cost compared to IRC and TSOP. As depicted in Figure 12, an array of IGBTs or SiC MOSFETs can be monitored concurrently with several FBG sensors on a single optical fiber cable. The fiber is carefully laid so that the sensing portions of the fiber grating are situated on the device's chip. Each FBG sensor is uniquely identified based on its center wavelength so that once the light is allowed to pass through the fiber, each FBG sensor  
Figure 11: Calibration setup for an FBG-based sensing technique.  
reflects at a designated Bragg wavelength based on the junction temperature of the device. Moreover, several sections of such fiber could be combined at the fiber flange. The fiber flange will provide an interface for connecting multiple fibers to form a distributed system for commercial applications; this interface could be made passive such that the light is transmitted, and the signal processing is shifted to the central monitor through an optical link such as free space communication (FSO). Otherwise, a photodetection circuit could be embedded to handle the data processing within its locality.
# Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage
## 2 Prerequisite Knowledge  
### FTVd Model  
We assumed that the linear mathematical model of an observed degraded image is expressed as follows [24]:  
\[\mathbf{G}=\mathbf{H}\ast\mathbf{F}+\mathbf{N}. \tag{1}\]  
For the sake of simplicity, it was assumed, in this study, that the processed image is an \(N\times N\) square matrix, which can be easily generalized to an image of size \(M\times N\). \(\mathbf{G}\in\mathbb{R}^{N\times N}\) is a degraded image matrix, \(\mathbf{F}\in\mathbb{R}^{N\times N}\) represents an original unknown clear image matrix, \(\mathbf{H}\) is a blur kernel matrix, \(\ast\) represents the two-dimensional convolution operator, and \(\mathbf{N}\in\mathbb{R}^{N\times N}\) represents additive noise, such as Gaussian noise and impulse noise. For impulse noise, a discrete ROF model is constructed using the L1 norm, where the FTvd model is expressed as  
\[\underset{F}{min}\left\{\|\mathbf{H}\ast\mathbf{F}-\mathbf{G}\|_{1}+\mu\text{R}(\mathbf{F})\right\}, \tag{2}\]  
where \(\|\cdot\|_{1}\) represents the Euclidean L1 norm. In Equation (2), \(\|\mathbf{H}\ast\mathbf{F}-\mathbf{G}\|_{1}\) is the fidelity term using the L1-norm, and \(\text{R}(\mathbf{F})\) is the regularization. In FTVd mode, \(\text{R}(\mathbf{F})=\|\mathbf{K}_{\mathbf{h}}\ast\mathbf{F}\|_{1}+\|\mathbf{K}_{\mathbf{v}}\ast\mathbf{F} \|_{1}\) is the anisotropic total variation (ATV) regularization term, \(\mathbf{K}_{\mathbf{h}}=[-1,1]\) and \(\mathbf{K}_{\mathbf{v}}=[-1,1]^{T}\) are the horizontal and vertical convolution operators, respectively, and \(\mu\) is a regularization parameter higher than zero, which serves to balance the fidelity term and the regularization term.  
As is shown in Equation (1), FTVd employs the ATV as the regularization and the L1 norm to depict the sparsity of impulse noise. However, ATV assumes the processed image to be piecewise constant. As a result, staircase artefacts occur. In addition, the L1 norm is the convex relaxation of the L0 norm. It cannot express the sparsity of impulse noise precisely. Considering the disadvantages of FTVd, the TGV regularization is employed to replace the ATV regularization, and the Lp-pseudo-norm is adopted to depict the statistic characteristics of impulse noise.
# Improving Medical X-ray Report Generation by Using Knowledge Graph
###### Abstract  
In clinical diagnosis, radiological reports are essential to guide the patient's treatment. However, writing radiology reports is a critical and time-consuming task for radiologists. Existing deep learning methods often ignore the interplay between medical findings, which may be a bottleneck limiting the quality of generated radiology reports. Our paper focuses on the automatic generation of medical reports from input chest X-ray images. In this work, we mine the associations between medical discoveries in the given texts and construct a knowledge graph based on the associations between medical discoveries. The patient's chest X-ray image and clinical history file were used as input to extract the image-text hybrid features. Then, this feature is used as the input of the adjacency matrix of the knowledge graph, and the graph neural network is used to aggregate and transfer the information between each node to generate the situational representation of the disease with prior knowledge. These disease situational representations with prior knowledge are fed into the generator for self-supervised learning to generate radiology reports. We evaluate the performance of the proposed method using metrics from natural language generation and clinical efficacy on two public datasets. Our experiments show that our method outperforms state-of-the-art methods with the help of a knowledge graph constituted by prior knowledge of the patient.  
radiology report; computer-aided diagnosis; prior knowledge; knowledge graph; deep learning Article
## Materials and Methods
### Cell Viability and Magnetic Migration of SPION-Labeled D1 MSCs  
The cytotoxicity of SPIONs was determined by the 3-(3_A_-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) assay, as described previously [25]. D1 mouse bone marrow stromal cells (\(1\times 10^{3}\) per well) were seeded into 96-well plates in 100 \(\upmu\)L culture medium at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\). After overnight incubation, the supernatant was removed, and fresh medium containing varying concentrations of SPIONs (0.1, 1, 5, 10, 50, 100 \(\upmu\)g/mL) was added. Twenty-four hours later, the surviving cells were stained with MTT and quantified by measuring the absorbance at 540 nm. The MTT assay results were plotted as the mean \(\pm\) S.D. of three experiments.  
To analyze the internalization of SPIONs, D1 cells were fixed and observed with a Bio-TEM. The fluorescence of SPION-internalized D1 MSCs was measured with a FOBI (Fluorescence-labeled Organism Bioimaging Instrument). Our SPIONs (100 nm) and commercial SPIONs (NEO-LIVETM Magnoxide675, Biterials, Seoul, Korea) were prepared, and the migration efficiency was measured at different times after dispersing the cells by using a 1.5 T magnetic bar. Additionally, SPION-internalized D1 MSCs were evaluated on days 1, 3, 5, and 7 with a confocal fluorescence microscope and toxicity, maintenance, and monitoring points of intracellular SPIONs were analyzed.  
Figure 3: **(A)** Size and shape of SPIONs after interaction with and uptake into D1 MSCs. A final concentration of 50 \(\upmu\)g/mL SPIONs was used to label D1 MSCs by incubation for 24 h. After fixation, samples were analyzed by electron microscopy. **(B)** Most SPIONs were in the cell membrane and nuclear membrane. **(C, D)** Some SPIONs were observed in the cell nucleus.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 3 FSO-Based Backhauling for NTNs
### Physical Layer Design
The article also introduced a mixed RF/FSO relay using quasicyclic-LDPC and space-coupled LDPC codes, emphasizing their importance in cooperative optical and hybrid RF/FSO transmission. The results indicated enhanced reliability and transmission in cooperative RF/FSO systems using these codes compared to the existing approaches. Additionally, [PERSON] et al. (2016) addressed the design of hybrid RF/FSO systems for HAPS-aided relay satellite communication. They investigated rate adaptation and switching between FSO and RF links. Their study highlighted the benefits of buffering for compensation of the random fluctuations caused by UAV instability. Furthermore, the application of a MIMO system was examined within the realm of airborne-based FSO systems. In particular, [PERSON] et al. [127] presented an extensive channel model encompassing hovering UAVs, which established connections via FSO links. This model factored in various elements such as weather conditions, UAV mobility, and atmospheric turbulence. [PERSON] and [PERSON] in [128] introduced a statistical misalignment model and power-efficient transceiver configurations for bi-directional MIMO vertical FSO links. They addressed the challenges of atmospheric fading and pointing errors in MIMO FSO systems with multiple channels. The proposed approach employs a symmetric transceiver structure to enhance transmission performance, particularly for NTNs. This technique offers a solution for designing power-efficient FSO systems for NTNs by mitigating pointing errors and optimizing transmission performance. [PERSON] et al., in [129], proposed a novel power allocation methodology for optical satellite communication using MIMO FSO technology. They considered a geostationary satellite with multiple transmitters and an optical ground station with multiple receiving terminals. Their methodology addressed atmospheric turbulence effects, optimizing the network capacity under power constraints.
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 3 Traffic Status Prediction  
A number of traffic modeling studies have been proposed to predict traffic conditions according to the length of the prediction period, such as short-term prediction and long-term prediction. The characteristics of each prediction require a different modeling process to suit the temporal components and the targeted prediction period. Defining the length of the prediction period will help to decide the best technique to adopt for the traffic  
Figure 1: Structure of the survey.  
modeling study. Short-term and long-term predictions are used to define the time intervals in traffic flow prediction. Short-term prediction involves a short range of time periods such as seconds, minutes, hours, days, or weeks, while long-term prediction involves a long range of time periods such as several months or several years [10].