esa-sceva/satcom-chunk-collection huggingface dataset:
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 5 Design and Implementation
### Latency Optimization Strategy  
Note that the packet latency optimization strategy should not reduce the maximum throughput rate of the data plane because, in general, packet loss is less acceptable to the data plane than high packet latency. Therefore, the overhead of our packet latency optimization strategy should not be too high.  
Through the analysis in Section 4, when the packet arrival rate is low, the packet will wait in the Tx queue until the number of CPU polls reaches the counter threshold \(N\). In this case, the performance utilization of the CPU is very low, so we can consider forwarding the packets directly instead of putting them in the Tx queues.  
When the packet arrival rate is close to the maximum throughput rate, according to Formula (6), the packet latency mainly comes from the processing time of subsequent packets. Therefore, we can consider dynamically adjusting the size of \(\theta_{Tx}\) to reduce the packet waiting time in the Tx queues and thus reduce the packet latency.  
According to the architecture in Section 2.1, when the data plane has multiple Rx queues, for any of the Tx queues, the packets inside it may come from any of the Rx queues rather than from a particular Rx queue. It depends on the distribution of traffic. Note that since Tx queues are independent of each other, it is sufficient for us to analyze the case of a single Tx queue. To make our strategy easier to understand, we first deal with the case of a single Rx queue.
## 4 Discussion  
### Quantitative Analysis of Sample Size in Reaching Feasible Model Performance  
The studies compared two algorithms in simulating NEE (Table 5). Predictions of XGBoost for forest biome in this study were generally better than SVR [33]; for XGBoost, the evaluation metrics (R\({}^{2}\)) of DBF, EBF, ENF, and MF were 0.78, 0.58, 0.79, and 0.67 respectively, and for SVR they were 0.78, 0.59, 0.29, and 0.37 respectively; GRA also had a similar result, the R\({}^{2}\) of XGBoost and SVR were 0.55 and 0.37, respectively; but SVR had a better prediction for CRO, with a higher R\({}^{2}\) of 0.60 compared to 0.43 of XGBoost. For the forest biome (DBF, ENF, MF), XGBoost (with R\({}^{2}\) between 0.59-0.81) prediction was similar to the results of the best prediction in adaptive neuro-fuzzy inference system (ANFIS), extreme learning machine (ELM), artificial neural network (ANN), and support vector machine (SVM) model, with R\({}^{2}\) between 0.59 and 0.80 [39]. Note that the XGBoost training samples are the hybrid data from multiple EC sites, while the predictions of [PERSON] et al. [39] are for a single EC site. Similar prediction results were obtained for the bamboo forest sample sites using the RF method (R\({}^{2}\) = 0.68) [50]. [PERSON] et al. [36] used the MTE model to obtain predictions of NEE with R\({}^{2}\) = 0.49 from multiple EC sites (no biome distinction) around the world.  
Some predictions using the gradient boosting regression (GBR) method are better than using XGBoost to predict an EC site of ENF (with R\({}^{2}\) = 0.90 for GBR and R\({}^{2}\) = 0.81 for XGBoost) [37]. This likely arises for two reasons. First, the GBR method used a single site data for training. Due to the differences in climate and ecological environments of different EC sites, the prediction of a single site will be better than the hybrid data of multiple sites. We evaluated the same EC site and time period as [PERSON] et al. [37] did with XGBoost method. The evaluation results showed a slight improvement over biome-level simulation of ENF, which the site belongs to (with R\({}^{2}\) = 0.81 and RMSE = 1.049 g C m\({}^{-2}\)d\({}^{-1}\) for ENF biome and R\({}^{2}\) = 0.824 and RMSE = 0.802 g C m\({}^{-2}\)d\({}^{-1}\) for site-level). Second, the impact of extreme components (maximums and minimums) of the variables on the model was considered when training the GBR model. Therefore, we believe that under the same conditions the prediction of NEE by XGBoost generally meets expectations and is better than most other popular ML methods. It should be noted that all previous studies use multi-source data for model training, such as remote sensing data of vegetation, meteorology data etc., while we only use the meteorology data, which is the least number of data sources and environmental variables. It shows that single source and fewer environmental variables still have a good prediction effect on NEE. Similar results were reported for the prediction of NEE using meteorological data alone comparing the combination of meteorological and remote sensing data for model training [38].
## 4 Results
### Spatial Distribution  
Figure 9 shows the estimated ET (Figure 9a) and NEE (Figure 9c) maps of a case region, which are compared with the MODIS 8-day composite products (with a spatial resolution of 1 km). From the perspective of spatial distribution trends, the ET and NEE obtained in this study show good consistency with the MODIS data. From a detailed perspective, this study, based on Landsat data (with a spatial resolution of 30 m), can provide more detailed information. Moreover, high-resolution temporal images offer deeper insights into crop phenological changes. Overall, our ET and NEE estimation model serves as a valuable tool for regional water resource management, crop growth monitoring, as well as ecological and environmental field surveillance.
## 5 Conclusions
Therefore, construction companies will be able to simulate the impact of multiple management practices on CLP prior to their implementation in order to avoid applying management practices that have only subtle impact on CLP for given projects. Furthermore, they will be able to allocate their limited budget and resources to those strategies that most significantly improve CLP while providing a high degree of implementation feasibility, high degree of workers' adaptation, and low degree of implementation risk. Based on the findings of this study, construction practitioners can improve the CLP of their projects significantly in order to reduce variances from the primary plan and keep projects on time and within budget. The first limitation of this study was determining the effect of each improvement strategy on CLP without considering the existence of other strategies. However, improvement strategies are interconnected and synergy among them is expected to exist. To overcome the aforementioned limitation, future research may consider the impact of improvement strategies on each other by using simulation techniques such as fuzzy system dynamics to achieve results that are more precise. The second limitation was related to the limited number of experts that were used to carry out various phases of the developed model. In order to have more generalized and representative results, future studies may utilize a larger sample size in terms of experts. In addition, more criteria, such as impact on a company's key performance indicators, schedule risk, and budget risk, can be considered for ranking CLP improvement strategies. Although adding more criteria increases the computational complexity of fuzzy MCDM methods and increases the time spent on survey questionnaires, the model is expected to deliver more accurate results for prioritizing improvement strategies as more criteria are considered in the strategy selection process. Adding more criteria can also increase the applicability of the proposed decision support model within a broader context, such as the selection of the most effective improvement strategies on multifactor construction productivity, which includes labor, equipment, and material. Conceptualization, M.K., P.N. and A.R.F. ; methodology, M.K. and P.N. ; software, M.K. ; formal analysis, M.K. ; investigation, M.K. ; writing--original draft preparation, M.K. and P.N. ; writing--review and editing, M.K., P.N. and A.R.F. ; supervision, A.R.F. ; project administration, A.R.F. ; funding acquisition, A.R.F. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Industrial Research Chair (IRC) in Strategic Construction Modeling and Delivery (NSERC IRCP) 428226-15), which is held by Dr. [PERSON]. Not applicable. All data, models, and code generated or used during the study appear in the submitted article. The authors declare no conflict of interest.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## 7 Conclusions and Future Work
### Data Structure  
Before pre-processing, the Yelp dataset contained additional attributes, many of which are missing or partial and this makes the dataset semi-structured. Since a relational database schema is fixed, this would increase the number of tables in a relational database for each potential attribute that could have a relationship to any other data entry. In contrast, graph databases are schemaless and thus well-suited to handle such unstructured or semi-structured data.
## Nomenclature  
\(\alpha\), \(\beta\)  
Fitting parameters in the negative exponential gradation equation (NEGE). \(\alpha\) controls the shape of the gradation curve, and \(\beta\) controls the degree of inclination.  
Particle diameter (mm), the independent variable in the gradation equation.  
Maximum particle diameter (mm), a parameter in the gradation equation.  
Percentage of soil mass smaller than a given particle size.  
Porosity, the ratio of the volume of pores to the total volume of soil.  
Permeability coefficient, a key parameter measuring the seepage ability of soil.  
Uniformity coefficient, reflecting the uniformity of particle size distribution.  
Curvature coefficient, reflecting the shape of the gradation curve (e.g., smoothness or abruptness of particle size transitions).  
Back-Propagation Neural Network, a multi-layer feedforward neural network based on gradient descent, optimizing weights and biases via back-propagation.  
Gaussian Process Regression, a Bayesian-based regression model suitable for continuous output prediction, modeling the covariance of data to infer predictions and uncertainties.  
Random Forest, an ensemble learning algorithm constructing multiple decision trees from random subsets of data/features, aggregating results via averaging for regression.  
Coefficient of determination, measuring how well the model explains the variance of the target variable (closer to 1 indicates better fit).  
Root Mean Square Error, quantifying the average magnitude of prediction errors (dimensional consistency with the target variable).  
Mean Absolute Error, averaging absolute differences between predictions and true values (robust to outliers).  
\(\mathrm{U_{95}}\)  
95% Uncertainty Interval Width, the width of the 95% confidence interval for predictions, indicating the stability of model outputs (smaller values mean more reliable predictions).  
Computational Fluid Dynamics-Discrete Element Method, a coupled simulation technique modeling fluid-particle interactions in seepage flow, combining CFD for fluid dynamics and DEM for particle mechanics.  
Particle Swarm Optimization, an algorithm mimicking swarm behavior to optimize hyperparameters. Key variables in PSO  
Gray Relational Analysis, a statistical method evaluating correlations between factors by measuring the similarity of their curve shapes.
## 4 Performance Analysis of an FSO RIS-ACs Link over F-S \(\mathcal{F}\) TI fading Channels  
The first-order (F-O) performance metrics of the single-input single-output (SISO) FSO RIS-ACs link over F-S \(\mathcal{F}\) TI fading channels based on end-to-end average SNR for IM-DD with the OOK technique are provided in the following Section.
## 3 Methodology
### Variational Analysis Method (VAM)  
VAM is a data assimilation technique based on Bayesian theory. It uses observation data obtained from SAR as the observation field and ERA5 wind vectors as the background field. This method retrieves the optimal ocean surface wind speed and wind direction by constructing a cost function for variational optimization. In this process, the errors in both the observation and background field terms are assumed to follow a Gaussian normal distribution [10]. The cost function \(J\) can be expressed as follows  
\[J=J_{\text{O}}+J_{B} \tag{7}\]  
where \(J_{B}\) represents the background field term, and \(J_{\text{O}}\) represents the observation term, which is obtained from SAR data. Based on the characteristics of the observation field term, the cost function can be further decomposed into three categories,  
\[J_{NRCS}=J_{O\_NRCS}+J_{B} \tag{8}\]  
\[J_{DCA}=J_{O\_DCA}+J_{B} \tag{9}\]  
\[J_{NRCS+DCA}=J_{O\_NRCS}+J_{O\_DCA}+J_{B} \tag{10}\]where \(J_{O\_NRCS}\) represents the cost function of the observation field term constructed from the SAR NRCS, and \(J_{O\_DCA}\) represents the cost function of the observation field term constructed from the DCA information obtained through SAR.  
Traditional assimilation methods decompose the wind vector \(V\) into \(u\) and \(v\) components for observation assimilation. However, the relative wind direction is not a state variable and cannot directly affect the analysis results. Additionally, the impact of wind direction observation errors on \(u\) and \(v\) is often ignored. These issues can lead to significant errors in the assimilation of the \(u\) and \(v\) components, especially when the wind observation values differ greatly from the background field (such as during typhoons) [27]. Given that the errors in wind speed and direction in SAR observations are independent [28], the VAM treats these two variables as independent quantities, and its background field term can be described as follows  
\[J_{B}(V,\varphi)=\left(\frac{V-V_{B}}{\Delta V_{B}}\right)^{2}+\left(\frac{ \varphi-\varphi_{B}}{\Delta\varphi_{B}}\right)^{2} \tag{11}\]  
where \(V\) is wind speed and \(\varphi\) is relative wind direction. \(V_{B}\) and \(\varphi_{B}\) represent the background wind speed and the relative wind direction, respectively. \(\Delta V_{B}\) and \(\Delta\varphi_{B}\) are the standard deviations of the background wind speed and wind direction, with values of 1.7 m/s [14] and 20\({}^{\circ}\)[28], respectively.
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 3 Thermal Bridge Detection Method Using Neural Network
### Thermal Anomaly Area Clustering
#### 3.1.3 Thermal Anomaly Clustering  
This study applied Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [23], to segment physically separated clusters. DBSCAN is a density-based clustering method that has been successfully applied in many real-world applications [24]. In particular, it is designed to find clusters in noisy data [25].  
The segmentation process of abnormal area pixels by applying DBSCAN is as follows. First, the pixels in the abnormal areas are converted into 2D coordinate values. The pixels converted into 2D coordinate values have x and y coordinates, respectively. After selecting a random starting point among the points converted into 2D coordinates, the neighboring points located within a certain distance (\(\varepsilon\)) are identified from this starting point. If the number of points within \(\varepsilon\) is equal to or greater than the minimum number of points (MinPts), the corresponding point and neighboring point are considered as one cluster. Then, the process is repeated for all the neighbors. If the number of neighboring points is less than MinPts, the corresponding point is considered as noise. After repeating this for all the neighbors, the procedure above is repeated for all the remaining points. Figure 4 below shows the clustering process of pixels segmented as abnormal areas by applying DBSCAN.  
Figure 3: Thermal anomaly cluster classification: (**a**) criterion to classify abnormal cluster and (**b**) segmentation result.
# Source Rock Prediction Using Well Log and Seismic Data:
## 3 Methods  
A total of 10 wells in the study area were selected for the study, with a sampling interval of 5-10 m per well. A total of 955 samples were collected, including 190 each from wells R and X; 115 each from wells P and Q; 75 each from wells L, Z, and S; and 40 each from wells M, N, and Y. Dark mudstone and argillaceous sandstone were the mainly materials selected. Total organic carbon, rock pyrolysis, vitrinite reflectance, sporopollen color index, saturated hydrocarbon chromatography-mass spectrometry, and other tests were completed. The total organic carbon was measured using a CS444-type organic carbon analyzer (Hammond Manufacturing Company Limited, Guelph, ON, Canada). The specific method involved taking a power sample of about 0.1 g and placing it in a crucible, then leaving it to stand for 12 h with 5% hydrochloric acid to remove the carbonate. The sample was then washed repeatedly with high-purity water to a neutral state and then placed into the instrument for measurement. A YY3000A comprehensive evaluation instrument was used for the rock pyrolysis. The specific method involved first pulverizing the sample to a 100 mesh, and then putting it into the instrument for measurement. The reflectivity of vitrinite was measured using a UMSP-50 microspectrophotometer. The color index of the sporopoly was DIALUX-22 EB. For the GC-MS analysis, an HP5890 GC/5970B MSD chromatograph and an HP6890 chromatograph were used for determination. The chromatographic column was HP-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.5 \(\mu\)m), the carrier gas was helium (constant current mode, flow rate 1 mL/min), and the temperature was set to the initial temperature of 50 \({}^{\circ}\)C for 2 min. The temperature was raised to 310 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min and then maintained for 15 min, in accordance with the national standard GB/T 18606-2017 [24]. The geochemical characteristics and hydrocarbon generation potential of the Albian-stage source rocks were analyzed according to the experimental results. All analytical experiments were performed in the Geochemistry Laboratory of Yangtze University.
## 3 Correlation Analysis of Echo Broadening and Apparent Angle  
The main scale feature parameters currently used by torpedoes to recognize targets are the two mentioned above; echo broadening and apparent angle. The acoustic decoys can be simulated, making it difficult to recognize the torpedoes. Since the echo broadening and apparent angle characterize the target scale characteristics in the time and angle domain, they are both characterizing the same target characteristics; therefore, there must be a logical correlation between them. In this paper, we propose a scale recognition algorithm, based on the correlation between target echo broadening and apparent angle, which converts target echo broadening and apparent angle into apparent scale, respectively, and recognizes the target according to the difference between the two apparent scales. This scale recognition method further enhances the recognition of a logical relationship between scale features, based on the traditional single-feature parameter recognition method. Hence, the torpedoes may still recognize the acoustic decoys due to their scale features' inability to match, even though they simulate the submarine echo broadening and apparent angle on a large scale. This method improves the ability of the torpedo scale to recognize targets.  
The apparent tensor is the azimuthal range of the echoes received by the torpedo from different parts of the target combined with the target distance; the scale at which the torpedo detects the target, which is the apparent scale, can be obtained as follows:  
\[L=2R\cdot\sin\frac{\alpha}{2} \tag{8}\]  
Among them, \(L\) is the apparent target scale, \(\alpha\) is the apparent angle, and \(R\) is the target distance. Figure 7 shows the geometric relationship between apparent scale and apparent tensor correlation.  
The echo broadening is the time delay difference between the echoes received by the torpedo from different parts of the target, combined with the target attitude angle. The apparent target scale can be obtained as follows:  
\[L=\frac{D\cdot\tan\theta\cdot\mathrm{c}}{2} \tag{9}\]  
Among them, \(D\) is the target echo broadening, \(\theta\) is the target side angle obtained by estimating the position of the target in the previous cycle and the current cycle, and the underwater sound speed is c, usually 1500 m/s. Figure 8 shows the geometric relationship between the apparent scale and the echo broadening correlation.  
Figure 7: The association between apparent scale and apparent angle.
## 4 Simulation Results and Discussion
### Reactive-Species Concentrations
During the same interval, the concentration of HNO\({}_{3}\) increases by more than 0.2 ppb. It is seen that a major portion (roughly about 2/3) of the NO emissions up to 70 min gets processed to HNO\({}_{3}\). This estimate does not take into account the effects of dry deposition of NO and NO\({}_{2}\). For times greater than about 60 min NO, NO\({}_{2}\) and RH concentrations in all layers increase steadily while O\({}_{3}\), OH and HO\({}_{2}\) concentrations continue the decreasing trends that started after their respective early maxima. All of these changes can be understood as being driven by the emissions of NO and RH. A comparison of NO and NO\({}_{2}\) concentration differences over the 60-200 min. interval shows that the stoichiometric changes for NO\({}_{2}\) are much larger than for NO, e.g., in the middle layer where the NO concentration increases by about 0.04 ppb while the NO\({}_{2}\) concentration increases by about 0.22 ppb. Also during this period, the concentration of HNO\({}_{3}\) increases by 0.4 ppb. By 200 min, the proportion of total nitrogen species that is processed to HNO\({}_{3}\) is about 0.5 and the proportion of NO\({}_{2}\) is about  
Figure 4: Temporal behaviors of the highest concentrations of reactive species for 200 min at the surface layer (SL) [z \(\leq\) 100 m], in the middle layer (ML) [100 \(<\) z \(<\) 900 m], and at the top layer (TL) [z \(\geq\) 900 m] of 64 uniform vertical grids in \(Z_{i}\) = 1000 m. The simulation is designed with emission fluxes for reactive species and no emission fluxes for the other reactive species: (**a**) NO, (**b**) O\({}_{3}\), (**c**) NO\({}_{2}\), (**d**) RH, (**e**) HO\({}_{2}\), (**f**) OH, (**g**) HNO\({}_{3}\), and (**h**) H\({}_{2}\)O\({}_{2}\) at the surface layer, with flux exchanges between CBL and FA for all reactive species, and with no deposition velocity for the end-product species (**g**) _HNO\({}_{3}\)_ and (**h**) H\({}_{2}\)O\({}_{2}\). 0.4 (neglecting dry deposition of NO and NO\({}_{2}\)). Evidently, the continuing emissions of NO and RH overwhelm the ability of the oxidants to maintain their control of the growth of NO\({}_{\mathrm{x}}\). Another related result shown in Figure 4 is that by 200 min the concentration of NO in the top layer slightly exceeds that in the surface layer. This is due to both the rapid transport of NO from the surface and the fact that the photolysis rate of NO\({}_{2}\) exceeds that of the reaction of NO with O\({}_{3}\) by about 8 %. Later this study will compare the TKM results to those for other model systems to illustrate the differences caused by turbulence effects.