esa-sceva/satcom-chunk-collection huggingface dataset:
# Economic Loss of Pine Wood Nematode Disease in Mainland China from 1998 to 2017
## 2 Materials and Methods  
### Study Site and Data  
Pine wood nematode was firstly introduced to Nanjing, China in 1982, and rapidly spread to surrounding provinces. Now pine wood nematode disease has occurred in most areas of southern China and some north regions such as Shaanxi province and Liaoning province. With the global climate change, pine wood nematode disease continues to spread northward. This study was conducted in mainland China with wide area coverage--the forest ecosystems between regions has obvious spatial heterogeneity, and the geographical conditions and seasonal climate are very different [32].  
The data of this study included the province-level pine wood nematode disease area from 1998 to 2017 in mainland China. The data were from the National Forestry and Grassland Bureau's National Center for Forest Pests and Diseases Monitoring Network, the national, provincial, prefecture (city) and county-level reporting agencies, and the statistical processing system of the Forest Pest Information Center of the National Forestry and Grassland Bureau. The annual diseased area of pine wood nematode disease came from forestry surveys.  
The pine wood nematode disease economic loss evaluation included direct economic loss and indirect economic loss. The direct economic loss evaluation using the yield and prices of rice, wheat, and corn planted in the country from 1998 to 2017, which are from China Statistical Yearbook and BRIC Agricultural Data Terminal. The indirect economic loss evaluation combines converted data, including the forestry output value index and the Engel coefficient. These data are at national level. In order to achieve provincial-level evaluation, we converted the national data using rainfall and GPP data.  
National rainfall data from 1998 to 2017 were obtained from China Meteorological Data Service Center-China Meteorological Data Network ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))); the GPP (gross primary productivity) data from 1998 to 2017 came from the National Science and Technology Infrastructure-National Earth System Science Data Center ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/))).
## 4 Conclusions and Discussion  
This study investigated the chemical composition of PM\({}_{2.5}\) and the light absorption characteristics of BC and BrC using filter analysis and a seven-wavelength aethalometer, located about 800 m away from a four-lane road. The measurements were conducted from 13 to 23 February 2022. The BC light absorption coefficient due to PM\({}_{2.5}\) was higher at 370 nm than at 880 nm during the study period, indicating the presence of substances that absorb light at ultraviolet wavelengths in addition to BC. These results include contributions from primary emissions and combustion sources such as BB to the light absorption properties of winter PM\({}_{2.5}\) at the study site, indicating a dual influence. Furthermore, BrC\({}_{370}\) exhibited a strong correlation with levoglucan and mannosan, while showing a weak correlation with K\({}^{+}\), indicating that levoglucan and mannosan are more suitable as BB tracers than K\({}^{+}\). Furthermore, the influence of QDTT-OP on the chemical components of PM\({}_{2.5}\) generated during the study period was investigated. The oxidative potential showed relatively high correlations with OC, EC, levoglucosan, BC\({}_{370}\), and BrC\({}_{370}\), indicating a close association with primary combustion sources (vehicular exhaust and biomass combustion) during the winter measurements of PM\({}_{2.5}\) oxidative potential. Evaluating the oxidative potential of PM\({}_{2.5}\) based on differences in the emission sources and chemical components can provide insights into the health risks associated with exposure to atmospheric PM\({}_{2.5}\). Moreover, it will serve as a crucial factor in establishing policies for controlling atmospheric PM\({}_{2.5}\). In addition, to thoroughly analyze the relationship between the chemical components of PM\({}_{2.5}\) and their oxidative potential, investigations should be conducted from a long-term perspective, encompassing various emission sources.  
[PERSON]-H.Y. contributed to this work via experimental measurements, data analysis, and manuscript preparation. [PERSON], [PERSON]-H.O., [PERSON], [PERSON] and [PERSON]-H.K. contributed to the research sample analyses. [PERSON]-S.B. contributed to the experimental planning and data analysis. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Research Funds of Mokpo National University in 2023.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data policy of the Mokpo National University, Republic of Korea.  
The authors declare no conflict of interest.
## 4 Result
### Analysis of the Influence of Noise  
In this paper, in order to analyse the applicability of the improved algorithm in the case of noise interference in different weather, different levels of Gaussian noise are added to the images to simulate rain, snow, or foggy weather. The peak signal-to-noise ratio  
Figure 11: Comparison of the algorithm before and after improvement under different light intensities.  
Figure 10: Comparison of models [EMAIL_ADDRESS]: (**a**) YOLOv5s; (**b**) Ours-YOLOv5s.  
(PSNR) is utilised to evaluate the quality of the image after adding noise. PSNR is defined based on mean square error (MSE). The definition of MSE is  
\[\text{MSE}=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[I(i,j)-K(i,j)]^{2} \tag{15}\]  
where \(m\times n\) represents the size of the image, and \(I(i,j)\) and \(K(i,j)\) represent the pixels of the original image and the pixels of the noisy image, respectively.  
Then, PSNR can be defined as  
\[\text{PSNR}=20\log_{10}\left(\frac{MAX_{I}}{\sqrt{MSE}}\right) \tag{16}\]  
where \(MAX_{I}\) is the maximum pixel value of the image.  
Through experiments, under the influence of noise, the original YOLOv5s algorithm correctly recognises lane line type in the range greater than 15.2 dB, while the range of recognition failure is less than 11.8 dB. The rest of the ranges are false positives or false negatives. While the improved algorithm correctly recognises the lane line type in the range greater than 13.3 dB, the range of recognition failure is less than 9.8 dB. The rest of the ranges are false positives or false negatives. As shown in Figure 12, where the \(Z\)-axis indicates the detection results, 1 indicates accurate recognition, 0 indicates false positive or false negative, and \(-1\) indicates recognition failure.  
In summary, compared to the original algorithm, the method proposed in this paper is able to accurately recognise lane line type in more severe weather.
## 4 Experiments
### Identifying the Sources of Market Efficiency  
Given that our evidence suggests that markets are more or less efficient, we may proceed to use the technology we have to uncover whether the lack of predictability comes from one of two possible sources; (i) random movements in the stock index, or (ii) nonstationarity in the distribution of stock index returns. In this section, we describe a non-parametric approach to ascribing the lack of predictability to each of these sources.  
We set the lookback period to \(L=30\) days and the forecast period to \(F=10,\,30,\,1000\) days. The size of the training data (rows of data) will be \(N=5000\). We also fix the number of epochs in the fitting exercise to be 50 as before. We compute OA from the following three experiments.  
1. _In-sample_: Using 5000 observations, starting from the first day of the sample, we train the neural net, and then test it on three sets: (i) a randomly chosen set of 10 observations from the training sample; (ii) a randomly chosen set of 30 observations from the training sample; (iii) a randomly chosen set of 1000 observations from the training sample; and (iv) the entire training sample is treated as the test sample. We then roll forward 20 days and repeat this experiment. This will give us 423 such experiments, each with four accuracy values (one from each test set) for \"OA\" and for \"FPA\".
2. _Stationary out-of-sample_: Here we try to maintain stationarity in the sample by bifurcating the sample into separate training and test groups but from the same sample period using a block of consecutive 5000 observations. (i) Randomly select 4990 observations for training and keep the remaining 10 for testing; (ii) randomly select 4970 observations for training and keep the remaining 30 for testing; (iii) randomly select 4000 observations for training and keep the remaining 1000 for testing. as in the previous case, roll forward 20 days and repeat this experiment.
3. _Nonstationary out-of-sample_: Starting from the first observation, pick a block of consecutive 5000 observations, and train the deep learning model. Then, (i) test the model on the next 10 observations; (ii) test the model on the next 30 observations; (iii) test the model on the next 1000 observations. Roll forward 20 days and repeat this experiment.  
Each of these experiments gives an \"OA\" that is denoted \(A_{i}\) where \(i=1,2,3\) for each of the classes above. By comparing these cases, we can determine how much prediction error is supported  
Figure 8: Forecast period average accuracy, where the look back period is \(L=30\) days and the forecast period is \(F=10\), 30 days. Forecast period average accuracy is 64% for \(F=10\) days and 77% for \(F=30\) days, both well over 50% as can be seen from the number of points in Figure 8 that lie above the 0.5 level on the y-axis. The overall accuracy is slightly over 50%, i.e., 53.9% for \(F=10\) days and 52.7% for \(F=30\) days.  
by randomness versus lack of stationarity. In the following section, we quantify these accuracy values to determine the sources of lack of predictability.
## 3 Model Development
### Establishment of the Risk Assessment Index System
#### 3.1.1 Risk Identification and Analysis  
The risk assessment usually begins from the risk identification and analysis of the project. In order to build a reasonable assessment index system for construction safety risk of renovation project, through consulting literature, visiting relevant experts and practitioners of owners, design units, construction units, supervision units, engineering quality inspection units, schools, and other units, the relevant risk factors were identified and analyzed. According to the actual situation of the renovation project and the common safety risks [33], the factors affecting the construction safety risks are divided into five categories: human risk factors, technical risk factors, material and equipment risk factors, management risk factors, environmental risk factors.  
Human risk factors: people are the direct participants in the construction process. The physical and psychological conditions, professional skills and safety awareness of the operators play a decisive role in construction safety risks. Therefore, the dominant role of human beings and the improvement of human activity ability can ensure the renovation project construction safety of existing building.  
Technical risk factors: the construction technology of renovation project is very different from the general construction technology, including wall opening, floor slab opening, external elevators, component reinforcement, partial demolition, etc. These will have great security risks and must be strictly controlled.  
Material and equipment risk factors: the impact of materials and equipments on construction safety are mainly reflected in: whether the materials are qualified, whether the temporary support and safety protection are reasonable, whether the equipment installation and operation control are safe, whether the materials transportation and stacking in the operation area are reasonable, whether the mechanical equipment is periodically inspected, etc.  
Management risk factors: there are many factors involved in safety management, including the establishment and improvement of safety production responsibility system, supervision of the use of safety protection measures, identification of on-site hazard sources, safety technology disclosure, the use of safety protection supplies, regular safety inspections, safety hazard investigation and emergency measures, etc. Therefore, scientific safety management is an important link to ensure the construction safety of existing building renovation project.  
Environmental risk factors: the construction site environment will also have a certain degree of impact on the construction safety of renovation project, including climate conditions, civilized construction, safety and protection, construction waste cleaning and environmental impact.
## 3 Experimental Results and Discussion
### Dynamic Response and Residual Impact Capacity
Figure 12: (**a**) Signal-processed acceleration at the west accelerometer for Beams A2–D2 during the first impact test; (**b**) acceleration at the east accelerometer for A2–D2 during the first impact test; (**c**) integrated velocity at the west accelerometer for A2–D2; (**d**) integrated velocity at the east accelerometer for A2–D2; (**e**) integrated displacement at the west accelerometer for A2–D2; and (**f**) integrated displacement at the east accelerometer for A2–D2. After the first impact test, CWT was applied to the acceleration signals in Figure 14 post-retardation. The signals show oscillation around a constant acceleration level, which resulted in low-frequency energy content from the CWT within a range of about 0-100 Hz. Since the accelerometers cannot measure constant acceleration levels, it was concluded that the low-frequency energy contents are fictitious and due to measurement error. Figure 13: (**a**) Acceleration at the west accelerometer for Beams B2–D2 during the second impact test; (**b**) acceleration for B2 and D2 during the fifth impact test; (**c**) integrated velocity for B2–D2; (**d**) integrated velocity for B2 and D2; (**e**) integrated displacement for B2–D2; and (**f**) integrated displacement for B2 and D2. Beams A1 and A2 in Figure 14a,b showed high energy concentration for frequencies in the range of 300-400 and 250-300 Hz, respectively. These frequency ranges are significantly lower than the fundamental analytical frequencies determined (see Table 5), indicating that the frequency range is not well defined. Figure 14: Acceleration and time–frequency scale-space from CWT of the signal measured at the west accelerometer for Beams A1, A2, C1, C2, D1, and D2. severe damage to the beams. In addition to this frequency range, Beams A1 and A2 have significant energy concentrations in the range of 750-900 and 700-800 Hz, respectively. In Figure 14d, similar frequency ranges with significant energy content showed for Beam C2, as for Beams A1 and A2, indicating severe damage to C2. Beam C1 instead showed higher frequency ranges with high energy density in (c), indicating less damage to this beam. Beam D1 in Figure 14e also presents high energy density in a first and second frequency range, but Beam D2 in (f) mainly showed energy density in one range. The difference between Beams D1 and D2 is that Beam D1 showed severe shear plug damage (Figure 8), while Beam D2 showed mainly flexural damage. This indicates that several frequency ranges with high energy content occur in conjunction with severe shear damage. At the same time, flexure-dominated responses instead show high energy content localized in one frequency range.
## 3 INS Measurement--Outline of Some Results
### Experimental Determination of Scatterer's Mass  
Deviations from the impulse approximation (IA) are often observed in experiments; see, e.g., [10; 26]. In the frame of conventional theory, such deviations can be understood as follows.  
Energy conservation (1) for a two-body collision holds in the IA, the latter being exactly valid at very large momentum transfers and, equivalently, for scattering off quasi-free particles. For finite energy and momentum transfers of most actual experiments, however, the IA is not completely fulfilled, and hence so-called final-state effects (FSE) become observable [10; 26]. FSE are due to interactions of the struck particle with its environment, which affect both initial and final states of it. In this subsection we shortly explain the conventional FSE.  
When a particle with mass \(M\) is not completely free but bound (even weakly) to other adjacent particles (in short: environment), the incoming neutron effectively scatters from a particle having effectively a higher mass than \(M\). This effect is because the adjacent particles are exerting forces on the scattering particle, the free motion of which can only be hindered by these forces. This can only lead to and increase--and never a decrease--of its effective mass \(M_{eff}\)  
\[M_{eff}\geq M\equiv M_{free}. \tag{4}\]  
This reasoning is well understood; see references in [10; 26].  
This inequality can also be derived from the aforementioned energy conservation relation, if one includes an additional term \(E_{int}>0\) caused by the atom-environment interactions (or forces):  
\[\bar{E}=\frac{\hbar^{2}\bar{K}^{2}}{2M}+E_{int}, \tag{5}\]  
\(\bar{E}\) and \(\bar{K}\) refer to the center of a measured peak. Hence, to the recoiling particle, there will be a reduced amount of energy, \(\bar{E}-E_{int}\), available as its kinetic energy after the collision. Obviously, \(\bar{E}\) and \(\bar{K}\) are determined from the neutron's kinematics, in contrast to \(E_{int}\), which is a quantity of the sample's material (i.e., of the scatterer). Now one can try to fulfill this equation using a new pair of energy and momentum transfer variables \((E_{IA},K_{IA})\), and applying the conventional theory in the IA. This yields  
\[E_{IA}=\frac{\hbar^{2}\bar{K}_{IA}^{2}}{2M_{eff}}. \tag{6}\]  
Obviously, if one wants to describe the collision as a two-body process, and fulfill the equation \(E_{IA}=\bar{E}\), the last two equations yield  
\[M_{eff}>M \tag{7}\]  
Additionally, this effect has been demonstrated by [PERSON], who showed its existence with the aid of an exact calculation of neutron scattering from a harmonic oscillator; see Figure 2 of reference [10].  
For more examples, references and associated discussions of \"effective mass\" and FSE, the interested reader may consult [10; 18; 26].
## 2 Quantum Key Distribution Network  
### QKDN Based on Fully Trusted Relays  
A QKD link is a logical connection between two QKD nodes. In the component of a QKD optical network, the nodes of both sides of communication include a QKD module and a key server. Two QKD nodes are connected through optical links in optical network facilities. The links use optical elements and optical fiber transmission keys to reduce interference. In the QKDN, the channel between two nodes includes the classical channel and quantum channel. The classical channel is used for synchronization and data exchange between the QKD modules. Quantum channels are used to transmit encoded photons.  
The network model based on QKD realizes the secure communication between end-to-end networking nodes by means of hop-by-hop manner or a key repeater, since the point-to-point QKD distance is limited by depolarization errors, dark counts and other factors [29]. The premise is that all nodes of the network are trusted. The QKD optical network can be divided into four layers: application layer, control layer, QKD layer and data layer [30]. In the network, the application layer is responsible for generating data service requests with security requirements. As shown in Figure 1, the control layer controls the functions of each layer through the internal controller, and it can manage the resources of the QKD layer and data layer. When the service request arrives, the controller allocates wavelength resources for the key in the QKD and data layer by notifying the corresponding node. The QKD layer provides key for data services.
# A Novel Physics-Guided Neural Network for Predicting Fatigue Life of Materials
## 2 Materials and Methods
### Models Involved in Comparative Verification  
In order to verify the performance of the proposed method, the traditional artificial neural network (ANN) and support vector regression (SVR) methods are introduced to compare the same training samples. The SVR method is briefly introduced here.  
Support vector regression (SVR) is a variant of the support vector machine (SVM) used in regression tasks. The principle is to generate a hyperplane from its kernel function, so that the hyperplane is close enough to all samples to achieve the purpose of regression. Radial basis function (RBF) is widely used in SVR, and C and gamma are two hyperparameters that affect its performance, where C is the penalty coefficient, which affects the degree of fitting and generalization of the model to the sample. Gamma represents the influence range of a single sample on the whole set of samples. In this study, the values of C and gamma were determined by orthogonal experiments, where C was set to 35 and gamma was set to 0.16.  
The SVR model was established based on the Scikit-learn machine learning website [25], and the proposed PGNN and ANN are based on the Pytorch deep learning framework [26]. The SVR method has a certain prediction result for the same training sample after training when the kernel function is constant, and the neural network may be uncertain even for the same training set after training due to the random initialization of the internal neurons. Therefore, this study uses the method of repeating training 5 times to obtain the best value for the proposed PGNN and ANN. The structure of the PGNN is Figure 1, and the ANN participating in the comparison test is a fully connected neural network with 4 layers. The inputs of ANN and SVR are load S, stress concentration factor Kt and stress ratio R, and the output is logarithmic fatigue life. The structure of ANN is shown in Figure 2.  
The batch size, number of Epochs, learning rate and activation function affect the convergence ability of the neural network. The same hyperparameters are set for both the PGNN and ANN, as shown in Table 1.