esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Structural Analyses for Selected Materials
### Determination of Natural Fundamental Periods with ANN
number of hidden layers, and the number of neurons in these layers. Many learning algorithms, learning functions, and activation functions in network structures can be built with MATLAB programming language. In line with the experimental studies, trainlm (Levenberg-Marquardt) is used as the training function, and learngdm (Gradient Descent Momentum) is used as the adaptation learning function in the created network structure, while logsig (Log-Sigmoid) in the hidden layer and the tansig (Hyperbolic Tangent Sigmoid) activation function in the output layer are preferred. There is one hidden layer in the established network structure and ten neuron cells in this hidden layer. The ANN structure used in the natural fundamental period estimations is shown in Figure 11.  
The network structure presented in Figure 10 was first trained with the obtained data set. The performance of the network in the training process is shown in Figure 12.  
According to the regression graphs presented in Figure 12, it can be seen that the established network structure's target line in training, validation, and test overlaps with the fit line and that the data is concentrated on these lines. It shows that the network has a high estimation rate in the training process. Successful results were obtained as a result  
Figure 11: ANN model for natural fundamental period estimation. Figure 12: Regression graphs of the established ANN model. of experimental analyses with the obtained ANN structure, and the fundamental natural periods presented in Table 6, which the network had yet to see before, were estimated. In the experimental studies, it has been observed that the target line and the fit line are separated from each other in the ANN structures established with different network parameters, and the R values decrease as the estimation data moves away from these lines. This shows that the network structures established with different parameters exhibit low performance. The fundamental natural periods obtained by ANN for 13 different types of materials are shown in Table 11.  
When the estimated values found and the periods obtained as a result of modal analysis (Table 7) are compared, the absolute error values of the created ANN structure for each mode and each type of material are shown in Table 12.  
As a result of the findings, the mean square error (MSE) value of the structure established with ANN was calculated as 0.0003, and the root means square error (RMSE) value was 0.0175. In light of this information, successful results were obtained with ANN in the natural fundamental period estimation. These results show that the proposed ANN network structure successfully estimates the natural basis period. In addition, the proposed ANN network structure and the network parameters that should be used in solving such a problem have also been determined. With the obtained architecture, the natural basis period estimation can be determined quickly and accurately.
## 5 Performance Evaluation
### Results
#### 5.3.1 Execution Time  
The execution time of the studied schemes over 6 scenarios in short simulation runs has been measured and presented in Figure 5. As well-known, increasing the number of physical nodes increases the execution time for all schemes, however, increasing the number of physical links for the same number of nodes might increase or decrease the execution time. However, the execution time significantly depends on the placement algorithm itself, while increasing the physical links may only increase the possibility of finding faster solutions.  
From Figure 5, it is clear that the proposed ILP-P and DcPSM show moderate increase of execution time when the physical nodes number increases, which tells that using the proposed path mapping  
Figure 4: The counters for service requests with: (**a**) specific number of nodes and specific number of edges; (**b**) specific number of virtual edges and specific number of paths.  
Figure 5: Average execution time of 10 simulation runs vs. the number of physical nodes over 6 scenarios with 3 types of service requests and 2 types of physical networks.  
approach can be more suitable for mapping mobile core network functions. In this experiment, the generated nodes for \(P_{5}\), \(P_{10}\), and \(P_{20}\) has been uniformly distributed between 5 and 10, while the other version of service requests for \(P_{5}\), \(P_{10}\), and \(P_{20}\) has been generated based on a fixed number of nodes equals to 10 virtual nodes. The results of all 6 scenarios show that the proposed ILP-P and DcPSM reduce the execution time to 39.58% and 6.42% of the execution times of the existing ILP-A and DSBM, respectively.  
Figure 6 reports the execution time for ILP-P, DSBM, and DcPSM to show the impact of changing the number of nodes, paths, and edges on the execution time. ILP-A was omitted here because its execution time is very high especially when the number of nodes and edges are increased. For example, the average execution time of ILP-A for requests with 10 virtual nodes, 29 links, 79 paths is about 1247 s in BT Europe topology. For this reason, we think that ILP-A is not suitable any more and it is confirmed that the existing DSBM can overcome the ILP-A in all scenarios. Eventually, we can conclude that the proposed ILP-P and DcPSM can significantly improve the performance by reducing the execution time, especially in the scenarios with large number of virtual nodes, edges, and paths. This improvement is due to the employment of the proposed path mapping approach, which can perform well compared to the state-of-the-art approaches.
## 2 Material and Methods  
### Design Selection and Philosophy  
#### 2.1.1 Analysis of User Needs  
The process of designing the R-barricade began with an in-depth analysis of user requirements to ensure that the design solution could fully meet the functional requirements of highway construction areas. An interactive hardware design based on the fundamentals of human vision can enhance warning and visibility for the barricade system to ensure the safety of highway construction roads, which not only provides effective warnings in various environmental conditions but also maintains high visibility at night or in bad weather. Based on the fundamentals of human vision, the following key design elements were considered:  
* Color: Human vision has different sensitivities to different colors [30]. Red, yellow, and orange have higher brightness, which can attract attention and convey warning messages. Therefore, these colors are adopted as the main visual cue colors in the design of barricades;
* Contrast: High contrast makes targets more visible and improves visibility [31]. In barricade design, contrast is increased by combining dark and light-colored elements, for example, using black and yellow stripes on a barrier wall;* Brightness: Brightness can affect human visibility and distance perception of objects [32]. In barricade design, the intervention of interactive components can improve the ability to prevent physical danger to workers and address the mobility of barricades, as well as improve visibility at night or in low-light conditions;
* Direction: Human vision is very sensitive to direction and shape. In roadblock design, by using arrows or other shapes to indicate the direction of a vehicle, drivers can be given a clearer picture of the road, thus reducing the risk of accidental collisions.  
During the user requirements analysis phase, we identified the core requirements for a barricade system in a motorway construction area, including high visibility at night and in adverse weather conditions, clear warning messages, and interactivity with operators.
# Real-Time Prediction of Fuel Consumption Based on Digital Map API
## 3 Methodology
### The Model of VSP-Fuel Consumption in Different Speed Intervals  
It was proven that VSP has a strong correlation with fuel consumption. Figure 5 presents a scatter diagram of VSP-fuel consumption rate and it can be seen that there is an obvious change trend when VSP equals 0. Thus, the unified regression model of all VSP data cannot well reflect the relationship between VSP and fuel consumption. In this paper, the data in different speed intervals are divided into  
Figure 3: The Vehicle Specific Power distribution at different speed intervals.  
two parts according to VSP (greater than zero or less than zero), and the VSP and fuel consumption rate are analyzed by regression analysis. In addition, to reduce the scatter of fuel consumption rate in each VSP bin, the average fuel consumption rate corresponding to each VSP bin is calculated. Because the data processing methods in these two conditions (VSP > 0 and VSP < 0) are the same, only the regression analysis results when VSP > 0 are shown in Figure 6. Further, the correlation coefficients are above 0.9.  
Figure 4: The Vehicle Specific Power distribution in the speed interval under 20 km/h.  
Figure 5: The VSP-fuel consumption scatter plot.  
For Figure 6, linear regression was a good method to solve the consumption model. The fuel consumption model is described concretely from Equation (7):  
\[\text{FC}=\text{a}*\text{vsp}+\text{b} \tag{7}\]  
FC is the fuel consumption rate in different VSP regions, and a and b are the regression coefficients. It is known that the fuel consumption rates of different vehicle types are significantly different, even under the same traffic conditions and operating mode. Many factors, such as the vehicle weight, engine displacement, and model year, affect the level of fuel consumption. Considering the limitations of the OBD data dimension in this paper, engine displacement is applied to differentiate the vehicle types. Then the OBD data are divided according to the interval of engine displacement in Table 5.  
If the vehicle has turbocharged engines, it would be put in the higher interval. For example, the vehicle with a 2.0-L turbocharged engine will belong to interval 3.  
Figure 6: The regression results after the cluster analysis of the VSP bin.  
\begin{table}
\begin{tabular}{c c} \hline
**Interval** & **Engine Displacement** \\ \hline
1 & (0,1.6) \\
2 & [1.6,2.5) \\
3 & [2.5,4) \\
4 & \(\geq\)4 \\ \hline \end{tabular}
\end{table}
Table 5: The intervals of engine displacement.  
After data partitioning, the coefficient a and b of Equation (7) can be obtained through the calculation of different engine displacement intervals and different speed intervals, as shown in Table 6.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 4 Discussion
### Quantisation Noise  
Truncation or rounding of timestamps has several implications. Firstly, even if the processing times of the system followed, say, a Burr distribution, then rounding or truncation is applied, the result will no longer follow this distribution. As we have a large number of data points, and a significant number of data points lie around the resolution of 1 ms, this can easily be identified by our statistical tests. Secondly, this might have knock-on effects on the temporal ordering of the events.  
We were not initially aware that this quantisation noise would be a significant feature of this dataset. In particular, our technique of partitioning the data by head/tail helped us identify where this modelling challenge was significant in the data. As it is not uncommon for data to be rounded in log files to a resolution, such as 1 ms, we highlight the issue of performing a simple fit of a parametric distribution to such data. Similar challenges are likely to face teams with modelling truncated/rounded data.  
On investigating other application log files, we found that many applications either already display 6+ decimal places in timestamps or are moving to do so. We would recommend that DevOps teams consider enabling these higher resolution timestamps on their systems and track typical time intervals that must be measured. For example, suppose our timestamp was displayed up to the nano-second. In that case, our dataset from today might not suffer from quantisation noise; however, if typical job times shorten in the future and approach 1 ns, then finer timestamps would be required to fully track the performance of the system.
## 2 Theoretical Background
### Probability of Failure  
Failure probability, often denoted as \(P_{f}\), is a critical concept in engineering, risk assessment, and reliability analysis that represents the likelihood of a system, component, or structure failing to meet its specified performance or safety criteria under given conditions. The failure probability \(P_{f}\) can be written as:  
\[P_{f}=\mathbb{P}(g(\mathbf{X})<0) \tag{1}\]  
The failure probability \(P_{f}\), when the vector of random variables \(\mathbf{X}\) is described by a joint probability density function (PDF) \(f_{\mathbf{X}}(\mathbf{x})\), is calculated as:  
\[P_{f}=\int_{D_{f}=\{\mathbf{x}:\,g(\mathbf{x})<0\}}f_{\mathbf{X}}(\mathbf{x}) d\mathbf{x} \tag{2}\]  
In Equation (2), which calculates the failure probability \(P_{f}\), the integration domain is determined by the region where the limit-state function \(g(\mathbf{X})\) is greater than or equal to zero, as indicated by the inequality \(g(\mathbf{X})\geq 0\). However, this region is typically implicitly defined by the limit-state function itself.  
In the general case, the computational complexity associated with solving Equation (2) can be significant, particularly when it involves the execution of a computational model with respect to the vector of random variables \(\mathbf{X}\). For a comprehensive review on conventional techniques in structural reliability and their practical applications, the reader is directed to the work by [PERSON] and [PERSON] (2015) and [PERSON] and [PERSON] (2018).
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement
#### 2.1.6 LSTM in Network Measurement  
An enhanced recurrent neural network called LSTM can address the RNN's inability to handle long-distance reliance. LSTM uses a gated technique to address the issue of exploding and disappearing gradients. Since their debut, LSTMs have drawn a lot of attention for their adaptability and efficiency across a wide range of applications [48; 49]. LSTM contains more parameters that can better regulate which memories are kept and which memories are destroyed at a certain time step compared to RNN, which only maintains a single hidden state. Unit state, hidden state, input gate, forget gate, and output gate are the five essential components of an LSTM. A one-step prediction with a specific reference value for time-series prediction can also be made by LSTM in addition to learning a lengthy series.  
[PERSON] et al. [23] suggested using a machine learning-based automated decision system to substitute professional users in bandwidth estimation. It is separated into the categorization system and the LSTM system. For the LSTM system, a composite dataset made up of several jobs was produced. While the second is trained simply using a bandwidth sequence, the first has many features. The performance of the LSTM system with just bandwidth was shown to be marginally superior to the performance of the LSTM system with numerous features. It outperforms the other models and is widely utilized in numerous sequence problems, including forecasting natural gas load. Parallel processing, however, also has several drawbacks. Even longer sequences still struggle with gradient loss. The LSTM neural network's intricacy meant that training took longer than it did for CNN.
# Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
## 4 Construction of Multi-Chain Traceability Model for Pepper Products
### Smart Contract Design under Multi-Chain Traceability Modeling
#### 4.4.2 Mapping Smart Contract  
The mapping relationship is established through the mapping contract and written into the main chain. If the peppercorn product has not yet established a mapping relationship, a new mapping relationship is created, and the mapping relationship is updated in the case of an existing mapping relationship. The specific process is that each link node is uploaded by calling up the smart contract, and the smart contract first uploads all the data of the link to the corresponding side chain, and then establishes the mapping relationship between the traceability code and the side chain transaction hash address, and if there is already the data of the traceability code in the mapping relationship, the corresponding transaction hash address is updated. The mapping contract pseudo-code is shown in Algorithm 2.  
```
Input: TraceabilityCode, TxID, Update Various Segments Breed Procees Transport Storage Sales Output: return Mapping 1: message = Stub.GetState(TraceabilityCode) //Checking for TraceabilityCode records in the sidechain 2: Index := &Index[] //If you don't have one, create a new mapping relationship 3: func UpdateIndex (args []string)//If yes, update the mapping relationship 4: if updateItem == \"Plant\" //Update transaction hash addresses for each link 5: Mapping.PlantTxId = TXID 6:!else if updateItem == \"Process\"{ 7: Mapping.ProcessTxId = TXID 8:!else if updateItem == \"Transport\"{ 9: Mapping.TransportTxId = TXID 10:else if updateItem == \"Storage\"{ 11: Mapping.StorageTxId = TXID 12: } else if updateItem == \" Sales\"{ 13: Mapping. SalesTxId = TXID 14: } 15: stub.PutState(traceabilityCode, Mapping)//Write the mapping relationship to the main chain 16: return Mapping
```  
**Algorithm 2** Mapping Smart contract pseudo code.
###### Abstract  
Oriented object detection in remote sensing images is a particularly challenging task, especially when it involves detecting tiny, densely arranged, or occluded objects. Moreover, such remote sensing images are often susceptible to noise, which significantly increases the difficulty of the task. To address these challenges, we introduce the Wavelet-Domain Adaptive Receptive Field Network (WDARFNet), a novel architecture that combines Convolutional Neural Networks (CNNs) with Discrete Wavelet Transform (DWT) to enhance feature extraction and noise robustness. WDARFNet employs DWT to decompose feature maps into four distinct frequency components. Through ablation experiments, we demonstrate that selectively combining specific high-frequency and low-frequency features enhances the network's representational capacity. Discarding diagonal high-frequency features, which contain significant noise, further enhances the model's noise robustness. In addition, to capture long-range contextual information and adapt to varying object sizes and occlusions, WDARFNet incorporates a selective kernel mechanism. This strategy dynamically adjusts the receptive field based on the varying shapes of objects, ensuring optimal feature extraction for diverse objects. The streamlined and efficient WDARFNet achieves state-of-the-art performance on three challenging remote sensing object detection benchmarks: DOTA-v1.0, DIOR-R, and HRSC2016.  
oriented object detection; Discrete Wavelet Transform; noise robustness; selective kernel mechanism; adaptive receptive field Article  
1
## 3 Results and Discussion
### Figures of Merit
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{
\begin{tabular}{c} **Emission Line** \\ **(nm)** \\ \end{tabular} } & \multirow{2}{*}{**Calibration Curve**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**LOD**} & \multicolumn{2}{c}{**LOQ**} & \multicolumn{2}{c}{**Upper Limit of**} \\  & & & & **(mg kg\({}^{-1}\))** & **(mg kg\({}^{-1}\))** & **Calibration Curve** \\ \hline Ag & 328.068 & y = 202.7\(\chi\)\(-\) 401.36 & 0.9999 & 0.18 & 0.61 & 1000 \\ Al & 308.215 & y = 46.26\(\chi\)\(-\) 89.904 & 0.9999 & 2.34 & 7.81 & 1000 \\ B & 249.772 & y = 31.24\(\chi\)\(-\) 192.07 & 0.9999 & 2.95 & 9.85 & 1000 \\ Ba & 230.425 & y = 11.80\(\chi\)\(+\) 8.3761 & 0.9999 & 0.63 & 2.09 & 1000 \\ Bi & 223.061 & y = 1.40\(\chi\)\(-\) 15.393 & 0.9999 & 3.41 & 11.38 & 1000 \\ Ca & 317.933 & y = 34.88\(\chi\)\(+\) 678.32 & 0.9989 & 4.51 & 15.02 & 1000 \\ Cd & 226.502 & y = 6.61\(\chi\)\(+\) 11.911 & 0.9999 & 1.57 & 5.24 & 1000 \\ Co & 238.892 & y = 10.84\(\chi\)\(-\) 32.128 & 0.9999 & 0.67 & 2.22 & 1000 \\ Cr & 357.869 & y = 380.5\(\chi\)\(-\) 2575.6 & 0.9999 & 0.43 & 1.45 & 1000 \\ Cu & 324.752 & y = 600.0x\(\chi\)\(-\) 1284.5 & 0.9999 & 0.15 & 0.52 & 1000 \\ Fe & 238.204 & y = 18.81\(\chi\)\(+\) 46.632 & 0.9999 & 2.92 & 9.75 & 1000 \\ Mg & 280.271 & y = 516.3\(\chi\)\(+\) 3009.4 & 0.9999 & 0.08 & 0.27 & 1000 \\ Mn & 257.610 & y = 233.7\(\chi\)\(+\) 1176 & 0.9999 & 0.09 & 0.29 & 1000 \\ Ni & 232.003 & y=2.76\(\chi\)\(-\) 13.599 & 0.9999 & 0.85 & 2.85 & 1000 \\ Pb & 217.000 & y = 0.43\(\chi\)\(-\) 4.0975 & 0.9999 & 5.95 & 19.83 & 1000 \\ Tl & 276.787 & y = 2.08\(\chi\)\(-\) 20.183 & 0.9998 & 4.98 & 16.60 & 1000 \\ Zn & 213.857 & y = 6.86\(\chi\)\(+\) 23.944 & 0.9999 & 1.55 & 5.18 & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Linearity, LODs and LOQs for the examined analytes.
## 3 Materials and Methods
### Phenolic Compounds Analysis
#### 3.4.3 LC/DAD/ESI-MS\({}^{\text{n}}\) Analysis  
For the analysis, 20 mg of bee pollen extract was dissolved in 2 mL of 80% ethanol/water. The final solution was filtered through a 0.22 \(\upmu\)m membrane and kept in the freezer at \(-\)20 \({}^{\circ}\)C, until time for analysis. A Dionex UltiMate 3000 ultra-pressure liquid chromatography instrument, connected to a diode array and attached to a mass detector, was used for LC/DAD/ESI-MS\({}^{\text{n}}\) analyses (Thermo Fisher Scientific, San Jose, CA, USA). LC was conducted in a Macherey-Nagel Nucleosil C18 column (250 mm \(\times\) 4 mm id; particles diameter of 5 mm, end-capped) and its temperature was kept constant at 30 \({}^{\circ}\)C. The conditions for the liquid chromatography process were based on a previous work [12], the flow rate was 1 mL/min and the injection volume was 10 \(\upmu\)L. The final spectra data were accumulated in the wavelength interval of 190-600 nm. The LTQ XL linear ion trap mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) equipped with an ESI source was set in the negative ion mode. The ESI conditions were in accordance with those described in previously reported studies [12]. Mass spectra were acquired via full range acquisition covering 100-1000 \(m/z\). For the fragmentation study, a data-dependent scan was performed by deploying a collision-induced dissociation (CID). The normalized collision energy of the CID cell was set at 35 (arbitrary units). Data acquisition was conducted using the Xcalibur(r) data system (Thermo Scientific, San Jose, CA, USA).  
The elucidation of the phenolic compounds was achieved by comparing their chromatographic behaviour, UV spectra, and MS information, with those of reference compounds. When standards were not available, the structural information was confirmed via a combination of UV data and MS fragmentation patterns that were previously reported in the literature. Quantification was achieved using calibration curves for \(p\)-coumaric acid (0.00925-0.4 mg/mL; y = 2.06 \(\times\) 10\({}^{7}\)x - 3.5 \(\times\) 10\({}^{5}\); R\({}^{2}\) = 0.973), kaempfer (0.037-1.6 mg/mL; y = 4.27 \(\times\) 10\({}^{6}\)x + 1.98 \(\times\) 10\({}^{5}\); R\({}^{2}\) = 0.983), chrysin (0.0185-0.8 mg/mL; y = 1.20 \(\times\) 10\({}^{7}\)x - 5.83 \(\times\) 10\({}^{4}\); R\({}^{2}\) = 0.999), quercetin (0.037-1.6 mg/mL; y = 3.9 \(\times\) 10\({}^{6}\)x + 4.65 \(\times\) 10\({}^{5}\); R\({}^{2}\) = 0.937), and naringenin (0.0185-0.8 mg/mL; y = 7.85 \(\times\) 10\({}^{6}\)x - 3.04 \(\times\) 10\({}^{5}\); R\({}^{2}\) = 0.978). When the standard was not available, the compound quantification was expressed as the equivalent of the structurally closest compound. The results were expressed in mg/g of fresh pollen.