esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Inherent Optical Properties (IOPs)
### Atmosphere IOPs
#### 3.4.2 Aerosol IOPs  
If we know the size distribution and the refractive index of the aerosol particles, we may use available aerosol models to generate aerosol IOPs. For example, one may use the aerosol models employed in the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Database Analysis System (SeaDAS), and described by [PERSON] et al. [30]. Alternatively, we may use the OPAC models [31]. For atmospheric correction of ocean color imagery, it is customary to assume a lognormal distribution of aerosol sizes [32]. Based on AERONET data [33; 34], [PERSON] et al. [30] adopted a bimodal lognormal volume size distribution:  
\[v(r)=\frac{dV(r)}{dr}=\frac{1}{r}\frac{dV(\ln r)}{d\ln r}=\sum_{i=1}^{2}\frac{ V_{i}}{\sqrt{2\pi\sigma_{i}}}\frac{1}{r}\exp\Bigg{[}-\left(\frac{\ln r-\ln r_{vi}}{ \sqrt{2}\sigma_{i}}\right)^{2}\Bigg{]} \tag{66}\]  
where the subscript \(i\) represents the mode, \(V_{i}\) is the total volume of particles with mode \(i\), \(r_{vi}\) is the mode radius, also called the volume geometric mean radius, and \(\sigma_{i}\) is the geometric standard deviation. Please note that since the numerator in the exponential of Equation (66), \(\ln(r/r_{vi})\), is dimensionless, so is \(\sigma_{i}\). Since  
\[\int_{0}^{\infty}\frac{dr}{\sqrt{2\pi\sigma}}\frac{1}{r}\exp\Bigg{[}-\left( \frac{\ln r-\ln r_{v}}{\sqrt{2}\sigma}\right)^{2}\Bigg{]}=1\]  
integration over all sizes for both modes, yields:\[\int_{0}^{\infty}v(r)dr=V_{1}+V_{2}=V.\]  
In terms of the number density (concentration), Equation (66) becomes  
\[n(r)=\frac{dN(r)}{dr}=\frac{1}{r}\frac{dN(r)}{d(\ln r)}=\sum_{i=1}^{2}\frac{N_{i }}{\sqrt{2\pi}\sigma_{i}}\frac{1}{r}\exp\Bigg{[}-\left(\frac{\ln r-\ln r_{\rm ni }}{\sqrt{2}\sigma_{i}}\right)^{2}\Bigg{]} \tag{67}\]  
where the number of particles \(N_{i}\) and the mean geometric (or mode) radius \(r_{\rm ni}\) are related to \(V_{i}\) and \(r_{\rm vi}\) as follows  
\[\ln r_{\rm ni}=\ln r_{\rm vi}-3\sigma_{i}^{2} \tag{68}\] \[N_{i}=\frac{V_{i}}{\frac{3}{3}\pi r_{\rm ni}^{3}}\exp(-4.5\sigma _{i}^{2}) \tag{69}\]  
and integration over all sizes for both modes, yields:  
\[\int_{0}^{\infty}n(r)dr=N_{1}+N_{2}=N.\]  
If we use the subscript \(i=f\) to denote the fine mode, and the subscript \(i=c\) to denote the coarse mode, we have \(V=V_{f}+V_{c}\), and the volume fraction of fine mode particles becomes \(f_{v}=V_{f}/V\).
# Heated Metal Mark Attribute Recognition Based on Compressed CNNs Model
## 4 Methodology
### Top Compressed CNNs Models
#### 4.2.2 SqueezeNet  
_SqueezeNet_ was proposed for preserving accuracy with few parameters [51]. A novel building block, _Firemodule_, is used as the core structure in _SqueezeNet_.  
Three main strategies are adopted to construct the _Firemodule_. First, 3 \(\times\) 3 filters are replaced with \(1\times 1\) filters. This can make the number of parameters 9\(\times\) smaller than before. Second, the number of input channels to filters is decreased. The number of parameters in one standard layer can be represented as \(N_{channel}\times N_{filter}\times S_{filter}\), where \(N_{channel}\) is the number of input channels, \(N_{filter}\) is the number of filters and \(S_{filter}\) is the size of filter. _Squeeze_ layer is proposed to reduce \(N_{filter}\) so the total number of parameters can be further decreased. Third, the network is late downsampled. Usually, layers have small activation feature maps if their stride is larger than 1, and larger activation feature maps can lead to higher performance.  
To accomplish the above strategies, _Fire_ module was designed, which consists of a squeeze layer and an expand layer. Squeeze layer has only \(1\times 1\) convolution filters (Strategy 1) and expand layer has \(1\times 1\) and \(3\times 3\) convolution filters. Then, three hyperparameters are set: \(s_{1\times 1}\), \(e_{1\times 1}\) and \(e_{3\times 3}\), which represent the numbers of \(1\times 1\) convolution filters in squeeze layer, \(1\times 1\) convolution filters and \(3\times 3\) convolution filters in expand layer, respectively. \(s_{1\times 1}\) is set to be less than (\(e_{1\times 1}+e_{3\times 3}\)), so the squeeze layer can help to limit the number of input channels to expand layer (Strategy 2). The _SqueezeNet_ model is constructed by stacking many Fire modules. The number of filters per _Fire_ module is increased gradually, and a max-pooling with stride 2 is performed with a certain interval (Strategy 3).  
The evaluation demonstrates that the _SqueezeNet_ architecture has 50\(\times\) fewer parameters than original _AlexNet_ and maintains _AlexNet_-level accuracy on ImageNet. Based on _SqueezeNet_, some works implement it on field programmable gate array (FPGA), and the model parameters can be stored entirely within FPGA and there is no need to access off-chip storage.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 3 Details for Model Training and Evaluation
### Evaluation Metrics  
To evaluate the detectability of trained segmentation models, two widely used metrics were introduced: (pixel-wise) F1 score and intersection over union (IoU). The F1 score is the harmonic mean of the precision and recall and is given by Equation (2):  
\[\text{F1}-\text{score}=2*\frac{\text{Precision}\;*\;\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{2}\]  
The precision and recall can be calculated using:  
\[\text{Precision}=\frac{\text{TPs}}{\text{TPs}+\text{FPs}} \tag{3}\]  
\[\text{Recall}=\frac{\text{TPs}}{\text{TPs}+\text{FNs}} \tag{4}\]  
where true positives (TPs) are the overlapping pixels between the prediction and ground truth, false positives (FPs) are the non-overlapping pixels of the prediction, and false negatives (FNs) are the non-overlapping pixels of the ground truth. Precision assesses the model's reliability in classifying positive samples, while recall assesses the model's detectability on positive samples. By calculating the harmonic mean of the precision and recall, the F1 score provides a balanced measure of both reliability and detectability.  
The IoU, also known as the Jaccard coefficient, is a commonly used metric for semantic segmentation tasks and can be described as the area of intersection of prediction with the ground truth, divided by the total area of the union of prediction and ground truth. The IoU can be expressed using TPs, FPs, and FNs, as shown in Equation (5):  
\[\text{IoU}=\frac{\text{TPs}}{\text{TPs}+\text{FPs}+\text{FNs}} \tag{5}\]
# Assessment of MicroPET Image Quality Based on Reconstruction Methods and Post-Filtering
###### Abstract  
The accuracy of positron emission tomography (PET) imaging is hampered by the partial volume effect (PVE), which causes image blurring and sampling. The PVE produces spillover phenomena, making PET analysis difficult. Generally, the PVE values vary based on reconstruction methods and filtering. Thus, selection of the proper reconstruction and filtering method can ensure accurate and high-quality PET images. This study compared the values of factors (recovery coefficient (RC), uniformity, and spillover ratio (SOR)) associated with different reconstruction and post-filtering methods using a mouse image quality phantom (NEMA NU 4), and we present an effective approach for microPET images. The PET images were obtained using a microPET scanner (Inveon, Siemens Medical Solutions, Malvern, PA, USA). PET data were reconstructed and/or post-filtered. For tumors smaller than 3 mm, iterative reconstruction methods provided better image quality. For tumor sizes bigger than 3 mm, reconstruction methods without post-filtering showed better results.  
m ouse image quality phantom; reconstruction; post-filtering; image quality; PET performance
## 3 Data and Methods
### MODIS Products Used in This Study
#### 3.1.1 MOD11C3 LST  
The land surface temperature (LST) is retrieved from MODIS thermal bands data (bands 31 and 32) over the entire land surface of the Earth. The required surface emissivity in bands 31 and 32 are estimated from land cover types [39]. LST is derived from the energy balance at the soil-atmosphere interface and is therefore a crucial parameter for the environmental energy budget [39,40]. The atmospheric effects are corrected using the split-window algorithm, considering that the signal difference in the two TIR bands is caused by the atmospheric differential radiation absorption [41].  
LST is a good indicator of the ground surface energy balance, as it is one of the key parameters in the physics of land-surface processes at regional and global scales [42]. LST is the result of the surface-atmosphere interactions and energy fluxes between the atmosphere and ground surface; therefore, it is widely used in climate, hydrologic, ecological and biogeochemical studies [11,41,43,44,45,46, 41].  
The MODIS LST accuracy is within 1 K in the range from 263 to 322 K under clear-sky conditions [47,48]. MODIS/Terra LST Monthly L3 Version 041 with a resolution of 0.05 degrees (MOD11C3) was used in this study.
## 3 Preliminaries
### Feature Extraction
#### 3.3.3 Term Frequency-Inverse Document Frequency  
The TF-IDF approach considers the term frequency of the word in a document (TF) in addition to the rareness of the word across all documents (IDF) [53]. This gives a more informative representation of the word within its context by excluding dominant words such as 'the', which have high term frequency, but they are not informative and do not reflect the context of the domain. That is, a word with high frequency in a specific document and a rare appearance in all documents is considered of high TF-IDF value than other words that are not frequently seen in the document or not rare across all the documents and not reflecting the domain-specific information. The formula of the TF-IDF of word \(t_{i}\) in a document \(d_{j}\) can be defined as follows [54]:  
\[TF\text{-}IDF_{i,j}=TF_{i,j}\times IDF_{i,j} \tag{9}\]  
The \(TF_{i,j}\) part is calculated by finding the number of occurrences of word \(t_{i}\) in a document \(d_{j}\) (\(n_{i,j}\)) and then dividing it by the total number of occurrences of all the words in document \(d_{j}\) (\(\sum_{k}n_{k,j}\)). This can be expressed by the following equation [54]:  
\[TF_{i,j}=\frac{n_{i,j}}{\sum_{k}n_{k,j}} \tag{10}\]  
In contrast, the \(IDF_{i,j}\) part is calculated as the log of the division of the number of documents (\(|D|\)) by the number of documents where the word \(t_{i}\) appears (\(|d_{j}\in D:t_{j}\in d_{j}|\)). This can be expressed by the following equation [54]:  
\[IDF_{i,j}=log\frac{|D|}{|d_{j}\in D:t_{j}\in d_{j}|} \tag{11}\]
## 3 SSMA Broadcast Protocol
### Discussion on Key Protocol Parameter  
#### 3.4.1 The Lower Bound of BS  
In _SSCBT_, _len_(_BS_) should be optimized to reduce energy consumption. Since every message transmission goes through seven steps given in Section 3.3.1 within _BS_, the lower bound of _len_(_BS_) is given as follows:  
\[len(BS)\geq max(rdelay)+t_{mr}+t_{turnon}+t_{CCA}+t_{ppd}+t_{tx}+t_{rm} \tag{10}\]
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### K-Nearest Neighbour Regression (kNN)  
The K-Nearest Neighbor algorithm is a well-established machine learning technique, frequently employed for classification and regression tasks. Its primary objective is to classify unmarked data points by assigning them to the category of the nearest labeled data point [50]. The algorithm's proficiency lies in its ability to leverage similarity metrics, which measure the distance between data points to identify the most analogous labeled data point. K-Nearest Neighbor Regression is a data-driven regression technique that does not rely on predefined parametric relationships between predictor and predicted variables. Instead, it utilizes information from observed data to make real-time predictions of the  
Figure 1: BERT model showing its two phases: pre-training and fine-tuning [47].  
predicted variable. In regression tasks, K-Nearest Neighbor estimates the response of a test point (\(x_{t}\)) by taking a weighted average of the responses from the \(k\) closest training points (x(1), x(2),..., x(k)) in the vicinity of \(x_{t}\). A kernel function is commonly employed to determine the weight assigned to each neighbor based on its proximity to the test point. Considering a training dataset X = {\(x_{1}\), \(x_{2}\),..., \(x_{M}\)} comprising \(M\) training points, each with \(N\) features, the weighted Euclidean distance can be utilized to measure the closeness between each training point \(x_{i}\) and the test point \(x_{t}\)[51; 52]. Euclidean distance is measured according to Equation (7) [52].  
\[D(x_{t},x_{i})=\sqrt{\sum\limits_{n=1}^{N}w_{n}(x_{t,n}-x_{i,n})^{2}} \tag{7}\]  
where \(N\) represents the number of features, \(x_{t,n}\) refers to the \(n^{\text{th}}\) feature value of the testing point \(x_{t}\), and \(x_{i,n}\) represents the nth feature value of the training point \(x_{i}\). The weight assigned to the nth feature is denoted as \(w_{n}\), and it ranges between 0 and 1.  
Next, kernel regression is applied and the estimation of response \(x_{t}\) is calculated according to Equation (8).  
\[\hat{f}(x_{t})=\frac{\sum\limits_{i=1}^{k}\varnothing(x_{t},x_{i})f\left(x_{(i )}\right)}{\sum\limits_{i=1}^{k}\varnothing(x_{t},x_{i})} \tag{8}\]  
where \(k\) represents the number of nearest neighbors employed for regression, \(\phi(x_{t}\), \(x_{(i)})\) represents a kernel function centered at the \(i^{\text{th}}\) training point \(x_{(i)}\), and \(f(x_{(i)})\) represents the known response of \(x_{(i)}\).
# Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy
## 2 Soft X-ray Contact Microscopy
### Desk-Top Laboratory SXCM System  
Almost all research on SXCM to date has been carried out using laboratory systems that are not adapted to use by biologists in their laboratory practice. In many articles, the authors emphasize the potential of this simple imaging technique and its usefulness in studying the structures of cells; however, so far, no attempt has been made to commercialize the SXCM microscopy system. The conceptual design of the laboratory microscope was presented only in [56]. Here, we present the compact laboratory system for SXCM microscopy that was developed by our team [42; 44], which we believe can meet these expectations.  
The schematic of the system is presented in Figure 2. The biological sample is placed on the resist in the form of a 500-nanometer-thick PMMA film spun on a silicon wafer. The plate is attached to a holder mounted inside a helium-filled chamber. The design of the holder and its chamber allows the sample to be positioned at a distance of 15 mm from the source of soft X-rays. The source is a laser plasma resulting from the interaction of laser pulses with a double-stream gas puff target. Laser pulses with a duration of 4 ns and energy up to 0.8 J are generated with a repetition frequency of 10 Hz in a commercially available Nd: YAG laser system (EXPLA). The target is formed by pulsed injection of a small amount of argon gas under high pressure into an annular stream of helium surrounding the argon gas stream using a specially designed electromagnetic valve system. The use of the argon gas puff target allows the production of soft X-ray radiation with high efficiency, comparable to a solid target, without the harmful effect of the target debris. The valve producing the gas puff target is mounted inside the vacuum source chamber in such a way that the target is formed at the focus of the laser beam produced by the aspherical focusing lens. The holder chamber is equipped with a 100-nanometer-thick Si3N4 membrane, which separates the chamber's helium atmosphere from the vacuum inside the source chamber. This allows the sample to be placed near the source without venting the vacuum chamber. Additionally, a 200-nanometer-thick Ti membrane is used to eliminate visible light from the plasma. The source produces soft X-ray radiation in the \"water window\" range, with a maximum wavelength of about 3 nm amd energy fluence of about 5 \(\upmu\)J/cm\({}^{2}\)/pulse, corresponding to the photon fluence of about 10\({}^{3}\) photons/\(\upmu\)m\({}^{2}\)/pulse. This fluence makesit possible to obtain the SXCM image of a sample as a result of 100-200 laser pulses (10-20 s exposition time). The whole system can be mounted on an optical table with dimensions of approximately 1 \(\times\) 1 m\({}^{2}\). Detailed information on the presented system and the results of preliminary studies on SXCM can be found in the previous publications by our group [9; 42].
## 4 Simulations and Results Analysis
### Security Level
See in this figure that, when the network size increases, the average length of the routes to the base station also increases. This figure allows us to see that the accuracy of the security level has been tested in networks with a minimum average route length of three hops and a maximum of seven hops. Figure 10: Average number hops to the base station, for different network sizes. In addition, Figure 11 presents the maximum number of hops (one hop being the minimum) traveled by the response messages in each simulated network. Notice that, in the network with \(200\) nodes, the farthest nodes are \(10\) hops away from the base station, and that in the network with \(1000\) nodes, the farthest nodes are up to \(29\) hops away from the base station. Finally, Figure 12 presents the average accuracy of SDSE estimates in differently sized networks. We can see that the accuracy remains extremely high for all network sizes, always next to 100%. Therefore, we conclude that the security level estimated by SDSE presents high accuracy for both messages that travel only one hop to the base station and messages that travel up to about 30 hops. This shows that the security level provided by SDSE is valid for both small and large networks.
## 3 Parameter Analysis and Optimization Design
### Parameter Analysis  
#### 3.1.1 Impact of the Intermediate Column's Position and Its Bending Line Stiffness  
The intermediate column's position \(\beta\) affects CLNVD's \(f_{d}\) and \(f_{E}\). Figure 7 reveals the curves of \(\gamma\), \(\eta_{d}\), \(f_{d}\), and \(f_{E}\) with \(\beta\). As is described in Figure 7a, when the intermediate column moves from the ends of the span to the middle of the span, \(\gamma\) increases, while \(\eta_{d}\) increases at first and decreases subsequently. However, the increase in \(\gamma\) is even greater. Therefore, when \(\beta=0.5\), \(f_{d}\) and \(f_{E}\) reach its maximum value. SAP2000 (Version 23.1.0) is used for numerical simulation, and seven seismic waves from Section 4.2 are selected. The analysis results of \(f_{d}\) in this section are taken as the average of the seven seismic wave calculation results. The results indicate that the theoretical calculation results of \(f_{d}\) are highly consistent with the numerical simulation results. In Figure 7b, \(i_{mc1}\) refers to the intermediate column's bending stiffness with the cross-section of 1600 mm \(\times\) 250 mm. As is displayed in Figure 7b, \(f_{d}\) and \(f_{E}\) rapidly increase at first, and then slowly decrease with the increase in \(i_{mc}\). A larger cross-section of the intermediate column is not conducive for CLNVD to obtain the peak value of \(f_{d}\) and \(f_{E}\). In order to ensure that CLNVD has good energy consumption capacity, the CLVD is required to lie at the middle span under the condition that the building space allows this.
# A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
## 1 Introduction
However, previous studies neglected some issues in using hotspot analysis. First, these studies mainly focused on the crashes essentially as a phenomenon related to roads or intersections, and could not directly identify CHIs. Second, often the Euclidean distance was used to calculate the weights, neglecting road network restrictions, which may affect the accuracy of the result of hotspot analysis. To solve these issues, first, we take intersections instead of crashes as the research object, which can directly identify CHIs. Second, the inverse network distance-band spatial weights matrix of intersections (INDSWMI) and the k-nearest distance-band spatial weights matrix between crash and intersection (KDSWMCI) are developed and applied to hotspot analysis to improve the accuracy of the results of CHI identification. The main tasks of this study include (a) creating the INDSWMI with respect to road network restraints; (b) building the KDSWMCI based on the k-nearest neighbors and distance-bands by obtaining the adjacency crashes and the number of crashes at each intersection; (c) testing the Intersection Prediction Accuracy Index (IPAI) of the proposed approach and identifying the CHIs to help safety researchers develop safety strategies to reduce crashes. The rest of the paper is organized as follows. Section 2 presents the methodology developed in this paper. Section 3 describes the original data, including the road network and traffic crashes of Spencer city, Iowa. Section 4 illustrates and discusses the results by applying the proposed methodology to Spencer city, Iowa. Finally, Section 5 concludes the paper and recommends future work.
## 5 Conclusions and Future Research  
To fully utilize the traffic flow data, we propose a method of VMD to decompose the data into different frequency components. We then construct encoders and decoders using the auto-correlation mechanism for each frequency component. The encoder and decoder are used to extract features from the corresponding frequency component, and a correction module is introduced to improve the residual structure of the encoder and decoder. Through comparative experimental demonstration, our model achieves the best prediction results for prediction lengths ranging from 96 to 720. In the ablation experiments, we verify the key role played by VMD and the correction module in improving the overall performance of the model. As the prediction length increases, the improvement in prediction effect due to VMD decomposition becomes more pronounced. Additionally, the correction module enhances the robustness of the model. Our model is able to fully extract the internal features of the data to make a more comprehensive prediction. Our model has high prediction accuracy and stable prediction performance, especially for long-term prediction. It enriches the research on long-term traffic flow prediction.  
Traffic flow prediction is a complex task that is influenced by various factors beyond the past traffic flow data. Factors such as weather conditions, holidays, and other contextual variables can significantly impact traffic flow changes. Furthermore, it has been observed that there exist hidden connections between traffic flows in different locations. Therefore, investigating the influence of these external factors on traffic flow prediction, including their hidden features, holds great potential for improving the accuracy of future predictions.  
Conceptualization, K.G.; methodology, K.G.; software, K.G.; validation, K.G.; formal analysis, G.L. and K.G.; investigation, K.G. and X.Y.; resources, X.Y.; data curation, X.Y. and K.G.; writing--original draft preparation, K.G.; writing--review and editing, K.G.; visualization, K.G. and S.T.; supervision, X.Y.; project administration, K.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the R&D Program of Beijing Municipal Education Commission (KM202111417003).  
Not applicable.  
Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.  
The authors declare no conflict of interest.