esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectroscopic Studies of Laser-Based Far-Ultraviolet Plasma Light Source
## 2 Experimental Setup
It has to be noted that the resolution of the spectrometer was not enough to resolve all weak spectral features as listed in the NIST database. The main spectral features of Si plasma emission mainly originated from doubly and triply ionized charge states (Si III-Si IV). Particularly, two strong spectral peaks about the wavelength of 140 nm are produced by two resonance transitions between the ground and singly excited states of 2p\({}^{6}\) 3s (\(J=1/2\)\(-2\)p\({}^{6}\) 3p (J = 3/2) (the wavelength of \(\lambda\approx\) 140 nm) and 2p\({}^{6}\) 3s (J = 1/2) \(-2\)p\({}^{6}\) 3p (J = 1/2) (\(\approx\)140.5 nm) in Si IV. Here, the J is the total angular momentum. \begin{table}
\begin{tabular}{l l l l} \hline
**States** & **Transitions (Lower-Upper Levels)** & **Observed Wavelengths (nm)** & **Transition Probabilities (I/s)** \\ \hline Si II & 3s2\({}^{2}\)3p + 3s3p\({}^{2}-\)3p\({}^{3}+\)3s2\({}^{3}\)d & 125.1–126.5 & (0.47–3)\(\times\)10\({}^{9}\) \\  & 3s2\({}^{3}\)p - 3s2\({}^{2}\)4s & 152.6–153.3 & (0.4–0.8) \(\times\)10\({}^{9}\) \\ \hline Si III & 3p\({}^{2}-\)3s6p & 123.5 & 2.77 \(\times\)10\({}^{9}\) \\  & 3s3p \(-\) 3p\({}^{2}\) & 128–130.3 & (0.5–2.1) \(\times\)10\({}^{9}\) \\  & 3s3d \(-\) 3p3d & 134–134.3 & (0.7–0.9) \(\times\)10\({}^{9}\) \\  & 3s4s + 3s3d \(-\) 3p4s + 3p3d & 136.1–136.7 & (0.8–1) \(\times\)10\({}^{9}\) \\  & 3s3p \(-\) 3p\({}^{2}\) & 141.7 & 2.2 \(\times\)10\({}^{9}\) \\  & 3s4p + 3s3d + 3s4s \(-\) & 143.3–143.6 & (0.4–0.9)\(\times\)10\({}^{9}\) \\  & 3s3d \(-\) 3s4f & 150–150.2 & (1.7–2.1) \(\times\)10\({}^{9}\) \\ \hline Si IV & 2p\({}^{6}\) 3s \(-\) 2p\({}^{6}\) 3p & 139.4 & 0.88\(\times\)10\({}^{9}\) \\  & 2p\({}^{6}\) 3s \(-\) 2p\({}^{6}\) 3p & 140.3 & 0.86 \(\times\)10\({}^{9}\) \\ \hline \end{tabular}
\end{table} 
Table 1: The observed Si spectral lines (Figure 1) according to the NIST database [22]. Figure 1: Comparison of the spectral irradiance of the standard Deuterium (D\({}_{2}\)) lamp (gray shaded area, right-hand scale) with a typical recorded calibrated spectral irradiance of a Si plasma at a distance of 100 cm from the target over the \(\approx\)123–164 nm wavelength region generated by 1.064 μm laser, 10 ns FWHM pulses at 100 Hz repetition rates (black line, left-hand scale). The laser irradiance was \(\approx\)2.5 \(\times\) 10\({}^{11}\) W cm\({}^{-2}\) (the laser energy \(\approx\)230 mJ). The strong Si spectral lines are identified using the NIST atomic database [22] as listed in Table 1.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 4 Experiments
### Low-Light Enhancement Results  
We evaluated our network on the LOLv1 [45] and LOLv2 [46] datasets. The LOLv2 dataset is divided into synthetic (LOLv2-syn) and real (LOLv2-real) datasets. According to Retinex theory, our model is equipped with an illumination estimator [56] at the forefront. The quantitative and qualitative comparison results with previous enhancement models are shown in Table 6 and Figure 5, respectively. Although our method shows slightly reduced performance on LOLv1 compared to previous approaches, it demonstrates outstanding performance on both the synthetic and real datasets of LOL-v2, even with significantly fewer parameters (2.45 million). As shown in Figure 5, our method restores low-light images with color and texture similar to the target images.
For efficient utilization of XGS-PON mobile front-haul, while avoiding collisions between packets transmitted from different RRUs at the same time, the DBA mechanism is used at the OLT side to govern the assignment of transmission opportunities (i.e., grants). In XGS-PON standard, the DBA engine at OLT assigns the transmission grants to the ONUs/RRUs based on their buffer occupancy reports or their transmission containers, which are also known as T-CONTs (note: in this paper we assume that the ONU has only one T-CONT, as in [7]; therefore, in this paper we will use the term ONU instead of T-CONT). Figure 1: Physical layer split based small-cell C-RAN architecture. In [8] we have shown that giving the grants to the ONUs based on their buffer occupancy reports, while adjusting the pre-determined limits for congested or heavily-loaded ONUs in the network, can efficiently accommodate the traffic variation of LTE/LTE advance networks (i.e., the actual RRUs traffic load) and attains a considerable delay performance improvement. As a continuation of this work, here we propose predicting the amount of ONU's buffer occupancy in advance based on historical buffer occupancy reports collected during BBU processing time. Based on these predicted reports, the DBA engine at the OLT prepares in advance the grants for each ONU minimizing the transmission latency for mobile front-haul without modifying the TDM-PON system and the BBU processing and protocols. We formulate the problem of predicting the future amount of ONU buffer occupancy (i.e., next ONU report) as a machine learning function approximation problem. Although there are multiple machine learning methods to solve such an approximation problem, we focus on artificial neural network (ANNs) as they are considered the most efficient tools to solve function approximation problems [11]. According to [PERSON] et al. [12], ANNs are able to estimate any arbitrary nonlinear function with any degree of accuracy required. For this reason, in this paper we adopt an adaptive-learning-based artificial neural network to solve the function approximation problem we mentioned earlier. To the best of our knowledge, there are no any prior studies that have applied the ANNs approach for traffic estimation of mobile front-haul networks. The remainder of this paper is organized as follows. The reported estimation-based bandwidth allocation problem is formulated as a machine learning function approximation problem in Section 2. In Section 3, we introduce an adaptive learning method to solve the function approximation problem and predict the upcoming ONU buffer occupancy report. In Section 4, we discuss the performance evaluation results of the proposed method. We conclude our paper in Section 5.
# Confidence Learning for Semi-Supervised Acoustic Event Detection
## 4 Results and Analysis
### The Effect of Hyperparameter \(\lambda\)  
As mentioned in [24], parameter \(\lambda\) greatly influences the threshold and distinctiveness of confidence. We adjusted \(\lambda\) in a small range to explore the effect of \(\lambda\). Figure 5 illustrates that all MT-AED models trained with \(\lambda\in[0.01,0.1]\) could generate confidence that is positively related to the accuracy of predictions. However, if \(\lambda\) is relatively large, the confidence values are aggregated. If \(\lambda\) is relatively small, a majority of confidence estimates possess the similar accuracy. Polyline \(y=x\) represents the ideal discrimination of prediction quality measurement. Therefore, the smaller the area between the curve and the Polyline \(y=x\), the better the discrimination of confidence. In our experiment, curve \(\lambda=0.03\) brought out the best distinction.  
Table 1 and Figure 5 demonstrate that a suitable combination of parameters \(\lambda\) and \(\alpha\) can produce more balanced detection results. INS errors were reduced under the premise of small fluctuations in DEL errors. As an example, when \(\lambda=0.1\), \(\alpha=1\), or \(\lambda=0.01\), \(\alpha=0.7\), the total number of events correctly predicted did not reduce while ER decreased significantly. These combinations increase the lower bound of confidence values, ensure the information contribution of samples with low confidence, and preserve the distinction of confidence.  
In practice, the joint usage of \(\lambda\) and \(\alpha\) is recommended to obey the following rules. First, \(\lambda\) is expected from 0.01 to 0.1; \(\alpha\) can be set from 0.3 to 1.0. Second, a small \(\lambda\) usually corresponds to a relative small \(\alpha\). The reason might be that a small \(\lambda\) leads to a broader distribution of confidence and a small \(\alpha\) forces the model to pay attention to more samples. The averaging of different models with different combinations can bring further improvement. Third, under the condition of fixed \(\lambda\), a larger \(\alpha\) is a better choice when the task is simple or the model has demonstrated relatively high performance.
# Implementation of a Cryogenic Facility for Space Debris Analysis
## 1 Introduction
Spacecraft passive thermal design allows thermal energy channels along the exterior so that appropriate temperatures are maintained in various elements of the spacecraft. * White paint. These paints tend to have low absorptance and high emissivity. These paints are used in spacecraft exteriors to decrease temperature. - Black paints. These paints typically have high absorptance and high emissivity. They are mainly used in the interior of satellites and also used in the exterior of satellites in optical satellite parts. - High-temperatures paints protect the spacecraft from high temperatures that occur, for example, during re-entry into the atmosphere. - Other paints (e.g., silver paints) with diverse requirements. - Multilayer insulation (MLI) materials
MLI is the ultimate solution in high-performance insulation. It specifically addresses all modes of heat transfer through the basic design of the system. For ground applications, MLI is often used inside a vacuum chamber, eliminating gas convection and minimizing gas conduction up to the molecular scale. Reflective shields are used to minimize the heat transfer by radiation proportional to the number of shields. Low-conductivity spacers are used to prevent the metallic-based reflective shields from touching one another and minimizing the conduction through the blanket itself. Much care is taken to design an MLI blanket such that it minimizes heat transfer in every possible manner, including edges, seams, and installation procedure. The environmental effects that cause degradation of MLI are primarily temperature cycling, solar radiation, particle radiation, and atomic oxygen [6; 7; 8]. While vacuum ultraviolet (VUV), from solar radiation, is abundantly present at all altitudes, atomic oxygen (ATOX) is of particular concern for LEO. MLI materials are made from polymer films, and ATOX reacts with this organic species and erodes the polymer. This phenomenon is known as ATOX erosion, and it is measured in loss mass. The key MLI specifications and material characteristics that are correlated with MLI materials' degradation, and therefore also correlated with debris generation, include thermo-optical (TO) properties; MLI stack configuration in terms of the number, thicknesses, and materials of MLI layers; and operational temperatures. This paper is structured as follows: Section 2 introduces previous research on the system. Section 3 explains the prototype design. Section 4 presents the prototype implementation. Section 5 focuses on experimental methods and results. Finally, Section 6 outlines conclusions.
# Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network
## 2 Preliminary
### Batch Normalization  
In the process of neural network iterative experiments, the distribution of features is constantly changing, so that the parameters in the convolutional layer must be constantly updated to adapt to the changing distribution. Obviously, this increases the difficulty of the test. In order to solve this problem, Google's DeepMind team proposed batch normalization (BN) [18]. Batch normalization technique is a trainable process, the essence of which is to normalize the feature distribution back to the standard normal distribution. As a technique for optimizing neural networks, the application of batch normalization can effectively reduce the shift of internal covariates, accelerate the convergence speed of CNN, and shorten the training time of the network. The calculation process of batch normalization can be expressed as follows:  
\[\mu=\frac{1}{N_{\text{batch}}}\sum_{n=1}^{N_{\text{batch}}}x_{n},\sigma^{2}= \frac{1}{N_{\text{batch}}}\sum_{n=1}^{N_{\text{batch}}}(x_{n}-\mu)^{2} \tag{1}\]  
\[\hat{x}_{n}=\frac{x_{n}-\mu}{\sqrt{\sigma^{2}+\varepsilon}},y_{n}=\gamma\hat{x }_{n}+\beta \tag{2}\]  
where \(x_{n}\) represents the \(n\)-th sample in a batch of input data and \(N_{\text{batch}}\) represents the batch size. \(\varepsilon\) is a constant fixed to \(1\times 10^{-6}\), which is used to avoid the case that the divisor is zero. \(\gamma\) and \(\beta\) are two learnable parameters for scaling and moving feature distributions.
# Life Cycle Assessment of Substitutive Building Materials for Landfill Capping Systems in Vietnam
## 3 Results and Discussions
### Soil Mechanics Result
The results in Table 9 show that mixtures RHA 1, RHA 3 and RHA 4 were feasible material mixtures to reach the quality requirements for class II landfill sealing systems. The mix RHA 2 values remained to be eligible for the quality requirements for class I landfill sealing systems. The mixes RHA 5, RHA 6, and RHA 7 did not comply with the quality requirements (Table 9). Further materials have been considered in the LCA study, particularly fly ash, which has already been proven in the past as a feasible substitutive building material for landfill sealing layers [16; 18; 59; 60]. Based on the pre-feasibility verification, the mix in Table 10 was adopted for further LCA assessment. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Mixture** & **w (-)** & **\(\rho\)s (g/cm\({}^{3}\))** & \(\rho\)Pr & **wPr** & **kf** & **FC** & **AC** & **Feasibility** \\ \hline RHA 0 & 0.1848 & 1.655 & 0.184 & 0 & n.m. & 7.64 & 60.50 & Not Applicable \\ RHA 1 & 0.0081 & 2.598 & 1.500 & 0.2155 & \(5.05\times 10^{-10}\) & 22.70 & 9.35 & Class II \\ RHA 2 & 0.0064 & 2.407 & 1.233 & 0.3125 & \(3.43\times 10^{-9}\) & 20.87 & 17.16 & Class I \\ RHA 3 & 0.0067 & 2.576 & 1.530 & 0.2065 & \(3.56\times 10^{-10}\) & 25.07 & 11.22 & Class II \\ RHA 4 & 0.1043 & 2.187 & 1.076 & 0.3405 & \(4.47\times 10^{-10}\) & 20.10 & 3.75 & Class II \\ RHA 5 & 0.0824 & 2.179 & 0.864 & 0.5148 & \(7.39\times 10^{-9}\) & 26.97 & 10.61 & Not Applicable \\ RHA 6 & 0.0968 & 2.175 & 0.998 & 0.4030 & \(2.04\times 10^{-8}\) & 29.11 & 16.67 & Not Applicable \\ RHA 7 & 0.1756 & 1.764 & n.m & n.m & \(1.22\times 10^{-4}\) & 18.88 & 44.64 & Not Applicable \\ \hline \hline \end{tabular} n.m–not measurable.  
\end{table} 
Table 9: Soil mechanical properties of the material mixtures with rice husk ash.
## 2 Materials and Methods
### Data Sources and Preprocessing  
#### 2.2.1 Land-use data  
The land-use data were sourced from the annual China Land Cover Dataset (CLCD) provided by Wuhan University, with an annual time frame and a spatial resolution of 30 m. This study selected 21 periods of data from 2002 to 2022, with the dataset classifying land-use types into nine categories: cropland, forest, shrub, grassland, water bodies, snow and ice, barren land, impervious surfaces, and wetland. The study retained only pixels that were consistently classified as cropland and whose land-cover type remained unchanged from 2002 to 2022; all other pixels were excluded. Yearly cropland areas in Xinjiang were extracted using the select-by-attribute function in ArcGIS, with 1000 m resampling performed using the nearest-neighbor method (Table 1).
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 3 Framework
### Symmetry-based Defective Carrier Detection  
To address the challenge of the false positives caused by defective carriers, it is essential to consider the morphological differences between defective and normal carriers. Thus, we combine the carrier morphological features into our method. The intuitive idea is two-fold: (a) The carrier is circular in wastewater microscopic images. (b) The normal carrier is symmetrical along both its horizontal and its vertical midline. Based on these two observations, we develop a novel module suitable for reducing the false positives caused by defective carriers by evaluating the similarity between the corresponding positions of carriers, described below.  
Since the carriers are all circular in shape, we can equally divide each of them predicted by the detection head into \(n\) blocks, thereby obtaining localized carrier image block features \(F_{b}\in R^{\frac{h}{n}\times\frac{w}{n}\times c}_{h}\).  
Then we evaluate the similarity of symmetrically paired image blocks, respectively. This process generates similarity matrix \(S_{symmetry}\in R^{n\times n}\), which can be mathematically expressed as follows:  
\[S_{symmetry}=\frac{F_{b}^{i}\cdot F_{b}^{is}}{\|F_{b}^{i}\|\cdot\|F_{b}^{is}\|} \tag{5}\]  
where \(F_{b}^{i}\) represents the feature vectors of the \(i\)-th image block, with \(i\in[1,n]\). Furthermore, \(F_{b}^{is}\) represents the symmetrically positioned image block of \(F_{b}^{i}\) after undergoing flipping.  
Subsequently, in order to intuitively and conveniently determine whether the image block is symmetrical, we construct a matrix \(I\) for recognizing based on \(S_{Symmetry}\). The specific calculation formula is as follows:  
\[I=\left\{\begin{array}{ccc}1,&if&S_{symmetry}^{ij}>\theta\\ 0,&if&S_{symmetry}^{ij}<\theta\end{array}\right. \tag{6}\]  
where \(S_{symmetry}^{ij}\) represents the element in the row \(i\) and column \(j\) of \(S_{symmetry}\), and \(\theta\) corresponds to the preset carrier image blocks similarity threshold. If all elements in the matrix \(I\) approach 1 very closely, it indicates that the carrier is normal; therefore, we should retain its position information\((x,y,h,w)\). Conversely, the presence of any element in matrix \(I\) that significantly approaches 0 indicates that the observed carrier is not a symmetrical circle, which is a false positive caused by a defective carrier. By removing candidate frames containing such defective carriers, we can effectively refine the detection results and enhance the overall recognition accuracy.
# Management Platforms and Protocols for Internet of Things: A Survey
## 8 IoT Device Management Platforms
### RestThing  
The RestThing platform [104] is a Web service infrastructure based on REST with the purpose of hiding the devices heterogeneity and integrate devices into a network. This platform enables developers to build applications accessing physical and Web services, which are both manipulated by a REST-style interface.  
As shown in Figure 27, the RestThing elements are: (i) applications; (ii) RESTful API; (iii) service provider; (iv) adaptation layer; (v) embedded devices, and; (vi) Web resources.  
Figure 26: Illustration of the SmartThings architecture.  
The RESTful API transmitted the data between sensors, gateways and Web applications using three types of data formats: JSON, XML, and CSV. For access to RESTful objects, the HTTP protocol operations used are: the GET method, used to retrieve the current device state; the PUT method, used to modify this device state; the POST method, used to create a new device; DELETE to remove a device, and, in addition, the LIST method, which allows all devices connected to the platform to be listed.  
_Pros: It hides device heterogeneity and provides a way to integrate devices into Web applications._  
_Cons: Security and Device Discovery are challenges._  
Use Case  
The Monitor Temperature and Heartbeat application is the user interface in a smartphone that combines physical and Web resources in the RESTful API. The real-time data view is used to obtain current data from WSNs. The smartphone updates this information by sending a GET to the gateway. The device number of the temperature sensor is what gets current internal lab room temperature as used in Smart Health environments [105].
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## Appendix B Sampling Algorithm
### Conditional Distributions of Prior Parameters and Hyperparameters of the Spike Component  
To avoid reducibility of the chain, the binary variable \(z_{q,b}\) should be sampled from the conditional distribution marginalized with respect to coefficient \(x_{q,b}\). In the new augmented space, this can be easily achieved since coefficients \(x_{q,b}\) are uncorrelated given the auxiliary variables and so are the selection variables \(z_{q,b}\). The resulting conditional distribution for each selection variable is a Bernoulli distribution i.e.,  
\[z_{q,b}|\mathbf{x}_{/x_{q,b}},\tau,\mathbf{\Theta}_{/z_{q,b}}\sim\mathbf{B} \left(\frac{1}{1+p_{q,b}}\right) \tag{17}\]  
where \(p_{q,b}\) is given by  
\[p_{q,b}=\frac{\phi\Big{(}g_{|q|}\Big{)}s_{q,b}\exp\Big{(}\frac{1}{2\tau}s_{q,b }\|\mathbf{P}_{q,b}\mathbf{\Phi}(\mathbf{H}^{\top}\mathbf{y}+\mathbf{u})\|^{ 2}\Big{)}}{\Big{(}1-\phi\Big{(}g_{|q|}\Big{)}\Big{)}\gamma_{|q|}v_{b}} \tag{18}\]The latent variable \(\mathbf{g}\) does not depend on \(\mathbf{u}\) conditionally to \(\mathbf{x}\) and \(\mathbf{y}\), then its conditional distribution does not change after introduction of \(\mathbf{u}\) in the model. One possible way to sample from the resulting conditional distribution of \(\mathbf{g}\) is to use the data augmentation trick of probit model [66].
###### Abstract  
Multispectral imaging plays a key role in crop monitoring. A major challenge, however, is spectral band misalignment, which can hinder accurate plant health assessment by distorting the calculation of vegetation indices. This study presents a novel approach for short-range calibration of a multispectral camera, utilizing stereo vision for precise geometric correction of acquired images. By using multispectral camera lenses as binocular pairs, the sensor acquisition distance was estimated, and an alignment model was developed for distances ranging from 500 mm to 1500 mm. The approach relied on selecting the red band image as a reference, while the remaining bands were treated as moving images. The stereo camera calibration algorithm estimated the target distance, enabling the correction of band misalignment through previously developed models. The alignment models were applied to assess the health status of baby leaf crops (_Lactuca sativa_ cv. Maverik) by analyzing spectral indices correlated with chlorophyll content. The results showed that the stereo vision approach used for distance estimation achieved high accuracy, with average reprojection errors of approximately 0.013 pixels (4.485 \(\times\) 10\({}^{-5}\) mm). Additionally, the proposed linear model was able to explain reasonably the effect of distance on alignment offsets. The overall performance of the proposed experimental alignment models was satisfactory, with offset errors on the bands less than 3 pixels. Despite the results being not yet sufficiently robust for a fully predictive model of chlorophyll content in plants, the analysis of vegetation indices demonstrated a clear distinction between healthy and unhealthy plants.  
stereo camera calibration; image alignment; multispectral vegetation indices +
Footnote †: journal: Applied sciences  
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