esa-sceva/satcom-chunk-collection huggingface dataset:
# MCIDN: Deblurring Network for Metal Corrosion Images
## 4 Experiment
### Dataset Construction  
In this paper, we create a dataset containing images of blurry metal corrosion, primarily referencing the corrosion images collected by [PERSON] et al. [63]. According to the ASTM D1654 standard, corrosion ratings range from 0 to 10, with a rating below 5 indicating severe corrosion and a rating of 10 representing no corrosion, corresponding to the initial condition of the metal corrosion test. For this study, we select corrosion ratings between 5 and 9, as they provide a more representative and challenging range for evaluating corrosion effects. These images are meticulously selected by material scientists and categorized into 5 to 9 distinct corrosion ratings, resulting in a total of 600 images with 120 images per rating. The corrosion ratings are annotated based on over a decade of laboratory corrosion testing by material scientists, encompassing common corrosion scenarios encountered in daily life and offering substantial practical application insights.  
Our dataset, depicted in Figure 5, consists of images that contain one or two scratches. The images are labeled with varying corrosion ratings, where a higher rating signifies less severe corrosion. To increase the challenge of deblurring corrosion images, our dataset includes metal samples with various background colors to prevent the background from being overly monotonous. We then apply an algorithm to blur these images, creating pairs of blurred and sharp images for training our metal corrosion images deblurring model. As outlined in Table 2, the dataset is randomly divided into training, validation, and test sets with a ratio of 8:1:1, ensuring no overlap of images across these sets to promote model generalization to new images.  
Figure 5: Some low-quality and high-quality images of corrosion rating class from 5 to 9 in our dataset.  
As shown in Table 3, to balance the impact of each category on the model, our dataset for each corrosion rating comprises 120 images. This balanced approach aims to enhance the generalizability of the trained model.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Membrane Properties and Module Configurations
#### 5.1.3 Fiber Looseness  
Fiber movement plays an important role in determining the extent of particle deposition on submerged HFs in bubbling cross-flow operation. Greater fiber movement, which can be achieved by using looser fibers, enhances the back-transport of the foulants from the membrane and also results in physical contact between the fibers, both of which reduce fouling [16]. Tight fibers studied by [PERSON] et al. [15] showed a 40% faster rate of TMP rise compared to fibers with a 4% looseness, where the looseness percent is based on the difference between the fiber length and the linear distance between the fixed ends of the fiber relative to the fiber length; this implies that approximately 40% of the fouling mitigation brought about by the bubbles was due to movement of the loose fibers. Simulations by [PERSON] et al. [193] showed that fiber displacement and membrane surface shear are highly variable at different locations along the fiber and with time. In addition, increasing the fiber looseness from 0.5% to 1% increased the average surface shear by 50.4% (0.56-1.13 Pa) for fibers with the same diameter. [PERSON] et al. [194] found that increasing the looseness from 0% to 1% decreased the fouling tendency regardless of the bubble size, whereas increasing the looseness from 1% to 2% increased the fouling tendency. This suggests an optimum fiber looseness, whereby too much displacement may move the fiber away from the influence of bubbles. This effect will depend on the module geometry relative to the bubbling zone. It is also known that too much looseness can lead to fiber breakage.
# A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence
###### Abstract  
Targeted sequencing has been widely utilized for genomic molecular diagnostics and the emerging DNA data storage paradigm. However, the probe sequences used to enrich regions of interest have different hybridization kinetic properties, resulting in poor sequencing uniformity and setting limitations for the large-scale application of the technology. Here, a low-complexity deep learning model is proposed for prediction of sequencing depth from probe sequences. To capture the representation of probe and target sequences, we utilized a sequence-encoding model that incorporates _k_-mer and word embedding techniques, providing a streamlined alternative to the intricate computations involved in biochemical feature analysis. We employed bidirectional long short-term memory (Bi-LSTM) to effectively capture both long-range and short-range interactions within the representation. Furthermore, the attention mechanism was adopted to identify pivotal regions in the sequences that significantly influence sequencing depth. The ratio of the predicted sequencing depth of the actual sequencing depth was in the interval of 1/3--3 as the evaluation metric of model accuracy. The prediction accuracy was 94.3% in the human single-nucleotide polymorphism (SNP) panel and 99.7% in the synthetic DNA information storage sequence (SynDNA) panel. Our model substantially reduced data processing time (from 334 min to 4 min of CPU time in the SNP panel) and model parameters (from 300 k to 70 k) compared with the baseline model.  
targeted sequencing; sequencing depth; bidirectional long short-term memory network; attention mechanism +
Footnote †: journal: Applied sciences
## 2 Materials and Methods  
### Study Area and Sample  
We selected a total of 17 traditional settlements within the jurisdiction of Jiexiu City in Jinzhong, Shanxi Province, China (Figure 1). Situated near the Mianshan Mountain range, with varied terrain and an average altitude of about 800 m, the area is traversed by the ancient Tea Road, carrying significant historical and cultural heritage along with abundant traditional settlement resources [25, 26]. Based on the characteristics of settlement distribution, these are categorized into mountainous, hilly, and plain types. Jiexiu City experiences a temperate continental monsoon climate with distinct seasons, an average annual temperature of approximately 10 \({}^{\circ}\)C, and annual precipitation ranging from 450 to 550 mm, most of which occurs in summer. The diurnal temperature variation is significant. This research focuses on 12 traditional settlements from the mountainous and hilly categories: Banyu Village, Liujiashan Village, Xingdi Village, Dajin Village, Hongshan Village, Jiaojia Fortress Village, Nanzhuang Village, Tian Village, Xialihao Village, Xiaojin Village, Zhangbi Village, and Zhangcun. The geographical locations of these settlements are situated at the foot of Mianshan Mountain and exhibit typical features characteristic of mountainous communities. The architecture within these settlements is constructed in harmony with the surrounding terrain and topography, with traditional buildings primarily made of loess, gray bricks, and timber. Walls are often built using rammed earth or masonry, and roofs are typically wooden structures covered with gray tiles or clay. To adapt to the climate, buildings generally feature thick walls and narrow windows. Some buildings are excavated directly into loess cliffs, forming structures known as yaodong (cave dwellings). Both architectural forms are representative of traditional Chinese residential structures and hold significant research value.
## 2 Background of the Literature
### Application in Manufacturing  
Relevant themes that have emerged as a result of flexibility, customisation, optimisation (saving time and costs), and smartisation, and those that require increasing connectivity include: predictive maintenance, which serves the objectives of sustainable manufacturing in the three dimensions of 3E; virtual commissioning [53] and crowdsourced manufacturing organisations [54]; real-time online support for production operators; new opportunities for servitisation; co-design; and co-manufacturing, cloud manufacturing and social manufacturing [55].  
The role of Industry 4.0 maintenance, especially that of predictive maintenance, presents a strategic factor in manufacturing [56]. Techniques, such as forecasting, health and safety management (PHM), and condition-based maintenance (CBM), create a demand for Operators 4.0 with adaptive interfaces that allow suitable characterisation and development of the required maintenance, with the necessary connectivity and the appropriate decision support [19]. The implementation of such techniques requires the use of the Industrial Internet of Things (IIoT) [40], cloud computing [57], big data [58], machine learning, and augmented reality [59; 60]. In this respect, [PERSON] et al. [56] describe the architecture of intelligent and predictive maintenance to support Operators 4.0 by providing guided intelligent decision support.  
Virtual commissioning, through the creation of a simulation model in a virtual environment of a manufacturing plant, allows Operator 4.0 to propose the necessary changes for its subsequent implementation in the real plant. However, today's lack of competencies and associated experiences hinder the full integration of this tool in manufacturing [53].  
Crowdsourced manufacturing organisations share their manufacturing resources based on their demand and capacity. [PERSON] et al. [54] have developed a manufacturing simulation model in collaboration with a resource model and a negotiation algorithm based on cyber-physical systems to evaluate the effectiveness of manufacturing. This enables Operator 4.0 to cover the task of resource manager and requires interfaces capable of providing real-time feedback on shared resources and on any needs that may arise.
## 2 Materials and Methods
### Bayesian Predictive Distribution  
The obtained parameters of the aftershock sequence model during the training time interval play a crucial role in calculating the EVD. The uncertainty of the parameters have a significant impact on the calculation of the corresponding probabilities. [PERSON] et al. [3, 48] incorporated the model uncertainties into the computation of the probabilities for the occurrence of the largest expected earthquakes by applying the Bayesian predictive distribution (BPD) approach, in which the BPD can be defined as:  
\[\Pr_{B}\{m_{ex}\geq m\mid S,\Delta t\}=\int_{\Omega}\int_{\Theta}\Pr_{EV}(m_{ex }\geq m\mid\theta,\omega,\Delta T)p(\theta,\omega\mid S)\,d\theta d\omega, \tag{12}\]  
where \(\Theta\) and \(\Omega\) are the frequency-magnitude distribution and seismicity rate parameter domains, respectively. \(\Pr_{EV}(m_{ex}\geq m\mid\theta,\omega,\Delta T)\) is the EVD and \(p(\theta,\omega\mid S)\) is the posterior distribution function, which quantifies the uncertainties of the model parameters.  
Since the ETAS model deviates from a non-homogeneous Poisson process the EVD for the largest magnitudes is not given by Equation (9). [PERSON] et al. [3] suggested to use the stochastic simulations to approximate the extreme value distribution and ultimately the BPD. In this approach, the Metropolis-within-Gibbs algorithm is used to sample from the conditional posterior distribution to generate the chain of the model parameters using the Markov Chain Monte Carlo (MCMC), then the model parameter chain is used to simulate the ETAS model during the forecasting time interval \([T_{e},\,T_{e}+\Delta T]\). At the end, the maximum magnitude is taken from each sequence of events to construct a distribution that approximates the BPD.  
When performing MCMC sampling a certain initial part of the parameter chain is discarded as \"burn-in\". The Gamma distribution was considered for the prior distribution of the model parameters. As burn-in the first 50% of Markov chains were discarded and the second half was utilized for calculation of the BPD.
## 4 Discussion
For example, for \(\Delta\)LAI, the actual \(|\mathrm{r}|\) at \(\Delta=2\) is 0.52 (compared to a theoretical value of 0.13), while the actual \(|\mathrm{r}|\) at \(\Delta=5\) is 0.75 (compared to a theoretical value of 0.45). Furthermore, the retrieval of \(\Delta\)LAI and \(\Delta\)LUE\({}_{\text{Canopy}}\) is also useful as a measure of the timescale of the sensitivity of the MODIS measurements to changes in the canopy structure and crop status. Good responsiveness to time-sensitive processes is important for several applications of crop remote sensing. For example, good responsiveness is important in monitoring phenology/crop growth stage [103; 104; 105], in-season detection of nitrogen [106; 107], water [107], and disease [108] stresses, and measurement of change in canopy structure during important growth stages [109]; these applications have proven useful in crop growth modeling [59], precision agriculture [110], and phenotyping for breeding selection [109], respectively. Our results show that satellite measurements can be used to detect changes in LAI and LUE\({}_{\text{Canopy}}\) faster and with higher accuracy than would be expected if the error in LAI and LUE\({}_{\text{Canopy}}\) retrievals were not autocorrelated in time. As a result, we also show the potential to rapidly detect growth and stress related changes in crop state variables with greater precision than that would be inferred from looking at generic performance validation studies [27; 28]. The analysis used in this study relied on strong crop growth model simulation performance to expand the dataset of ground-truth LAI values to daily resolution. The strong performance of the HM simulations at the Mead, Nebraska sites, seen in [90] and Figures 1 and 2, provides a potential path [67] for future research to expand the development of testing agronomic satellite retrievals to a wide variety of G x E x M factors with farmer-provided agromanagement data. The results in Table 6 show that using HM simulation data from Mead, Nebraska to train LAI retrievals can provide nearly identical performance to using actual ground-truth LAI measurements from Mead, while Table 7 shows there are some relatively significant biases in using modeled LUE\({}_{\text{Canopy}}\) to perform the training. The results for training LAI retrievals on HM simulation data show the potential of using farmer-provided agromanagement data to train, test, and improve retrieval algorithms, although a significantly greater number of sites is needed to understand the generalizability and biases in this approach. Nevertheless, the potential of reducing the uncertainty in the retrieval of crop state variables and the potential to map G x E x M factors shown in this study provides strong support for pursuing this collocated crop growth model simulation approach in agricultural remote sensing and should encourage researchers to increase their collaborative efforts with farmers [68; 111].
# A Wide-Range Four-Phase All-Digital DLL with De-Skew Circuit
## 2 The Proposed Four-Phase All-Digital DLL with De-Skew Circuit Architecture
The controller changes the number of active units of the delay line to provide the delay for skew compensation, thus aligning the edges of the system clock and the \(0^{\circ}\) clock output of the four-phase circuit. Finally, the proposed four-phase DLL with de-skew circuit generates the accurate four-phase outputs based on the system clock. Furthermore, instead of the traditional serial-cascade configuration, the DLL system adopts the proposed parallel-cascade configuration. The former usually works in sequence, and the power current varies in different operating modes, leading to voltage fluctuations in the supply network. Since the unit delay is susceptible to PVT, the voltage fluctuation eventually causes the alignment error of the de-skew circuit to change. In contrast, the latter has almost no power current variation during mode switching and keeps the supply voltage stable during system locking process. Therefore, the parallel-cascade configuration can effectively solve the alignment error variation caused by power supply integrity and ensure the phase-locked accuracy of the DLL system. Figure 2: The specific structure of the four-phase circuit. The all-digital four-phase DLL with a de-skew circuit begins working with a one-time trigger when the interface system is powered up. After it is locked, all control codes in the DLL are fixed to provide stable four-phase outputs to other circuits in the interface. If the interface system detects a phase error greater than the threshold, the system will send a reset signal to restart the DLL system to obtain new, accurate four-phase outputs.
# Reflective Inverse Diffusion
## 4 Results
### Spot Size  
The relationship between SLM dimension \(N\) and the background speckle size led to a corresponding relationship between \(N\) and the final focused spot size. The final spot size is approximated by applying diffraction theory to the light propagating from an individual SLM segment. The relevant dimensions are shown in Figure 11. Given that \(R_{1}\approx f\), the focal length of lens \(L2\), the spot size at the reflector is approximated by,  
\[q_{1}\approx\frac{\lambda R_{1}}{d_{pix}}, \tag{18}\]  
where \(d_{pix}\) is the width of a single square SLM segment. The final spot dimension in the observation plane is approximated by,  
\[q_{2}\approx\frac{\lambda R_{2}}{q_{1}}\approx\frac{R_{2}}{R_{1}}d_{pix}= \frac{R_{2}}{R_{1}}\frac{d}{N}, \tag{19}\]  
where \(d\) is the width of the entire SLM.  
The spot size from the CCD was determined by a one-dimensional autocorrelation on the center row of pixels for the \(N=8\), 16 and 32 stages. The autocorrelation for each of the reflector materials is shown in Figure 12. The vertical lines in the figure mark the predicted size in red with upper and lower bounds of the measured spot in cyan and magenta, respectively. The measured full width from the CCD was consistent with the spot sizes predicted by Equation (19) of 384 \(\upmu\)m, 192 \(\upmu\)m and 96 \(\upmu\)m for \(N=8\), 16 and 32, respectively.  
Figure 11: Experimental distance definitions for spot size calculations.
## 4 Results and Discussion
### Verification  
To verify the reliability of specimen-specific calculated aspect ratio and crack density values, both the total porosity and crack porosity of that specimen were calculated. The weight and volume of each specimen were measured to determine the total porosity created during the weathering process, and crack porosity was calculated using the aspect ratio and crack density values substituted into Equation (3). The calculated porosity at each step was averaged and the results shown in Figure 14. The calculated total porosity and crack porosity were in the ranges 1.36-2.14% and 0.083-0.124%, respectively, and the crack porosity had a relatively small absolute value. The calculated ratio of crack porosity to total porosity is shown in Figure 14b, and had a range of 3.8-7.9% and a mean value of \(\sim\)6.3%. This result matches those of previous studies, which suggest that crack porosity accounts for about 5% of the total porosity [37; 38], showing that the aspect ratios and crack densities derived from the Equations presented in this work are reliable.  
Figure 14: Distribution of porosity: (**a**) comparison between total porosity and crack porosity; (**b**) ratio of crack porosity as a function of total porosity. The 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 denote samples albite 1, albite 2, albite 3, albite 4, quartz 1, quartz 2, quartz 3, biotite, calcite, and chlorite, respectively.
# Review of Smart-Home Security Using the Internet of Things
## 4 Analysis and Discussions of the IoT Setting
### Architectural Structure of IoT Ecosystems
The network layer is responsible for ensuring the smooth operation of applications and the delivery of services provided by the IoT system. This layer also separates the data collected by different sensors from smart devices for different applications. However, the application layer is highly vulnerable to attacks and intruders who may try to exploit vulnerabilities in the architecture [99]. The perception layer consists of various devices and technologies designed to gather input from the surrounding environment. This includes pressure sensors, smoke sensors, vibration sensors, and RFID sensors. Its primary role is to collect and modestly process information, acting as a component of a larger, intelligent system. Often referred to as the sensing layer, it faces several challenges. Among these, the most critical issue is the efficient collection and capture of data [100]. The network layer [101] is accountable for ensuring that the devices in an IoT system are efficiently connected to each other so that they can communicate, exchanging data through the creation of a communication channel that links the terminal nodes to the servers. This level is essential in both telecommunications and Internet networks since it is responsible for transmitting data to and from the intermediate devices within the system. Its main responsibility is to securely send data collected by sensors to remote nodes, acting as a bridge between the local network and the remote node. Wired or wireless communication is used to connect devices and share necessary data, ultimately providing the desired services through applications managed by the topmost layer, the application layer. The final layer of the IoT architecture is the physical layer [102], which encompasses the hardware that comprises an intelligent environment with IoT technology. It consists of all smart devices, such as smart phones, computers, tablets, smart watches, smart TV, smart locks, smart smoke detectors, and others, which have sensors to collect data. Additionally, the required hardware for all smart devices to connect to the IoT network, whether wired or wireless, is also included at this level. Hence, it provides the physical connection of the devices as a service to its immediate superior layer, the perception layer.