esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Calculation Process  
#### 2.3.1 Capillary Water Absorption Coefficient  
Capillary water absorption refers to the process wherein liquid phase water enters an unsaturated porous material due to capillary action. The capillary water absorption coefficient \(S\) (kg/(m\({}^{2}\cdot\)h\({}^{1/2}\))) is an important parameter of the capillary water absorption process and represents the rate of water absorption by the porous material under capillary action. In the early 1980s, [PERSON] et al. [44; 45] carried out numerous experiments on capillary absorption in bricks and rocks, and concluded that the water absorption of the test samples had a linear relationship with the square root of the duration of water absorption. This ratio is the capillary water absorption coefficient \(S\). Subsequently, after numerous experiments, the following formula was proposed [45]:  
\[i=S\sqrt{t}+a \tag{1}\]  
where \(i\) is the cumulative water absorption per unit area, \(S\) is the capillary water absorption coefficient, \(t\) is the duration of water absorption, and \(a\) is the intercept on the \(y\)-axis.
## 6 Interpretation and Results
### Line 5 and Line 6  
The length of the L5 line is 920 m, with 24 measuring points and the distance between the points is 40 m. L5 is running NW-SE with an azimuth angle of 102 degrees. The electric sounding results of the L5 line indicate the presence of a noticeable decrease in resistivity between measuring points 80 and 200 at 100 m depth, suggesting the occurrence of a low resistivity anomaly. There is speculation regarding the presence of a fault or crack in this particular component. Additionally, it is postulated that there is both a contact surface with high resistance and a contact surface with low resistance within this component. Furthermore, it is hypothesized that there is a favorable contact surface for water in this component. The shallow portion of the line is hypothesized to consist primarily of argillaceous slate exhibiting a low resistivity abnormal response. In contrast, the deeper section is characterized by quartzite slate, which exhibits a high resistivity reaction (Figure 13). There is speculation regarding the presence of fissures within the contact interface of the two strata.  
The L6 line is 560 m long, with 28 measuring points and the distance between the points is 20 m apart. L6 is running W-E with an azimuth angle of 110 degrees. The results of the electrical sounding conducted on line L6 reveal a decrease in resistivity between measuring points \(-\)100 and \(-\)200. This observation suggests the presence of a fault or crack in this section, characterized by a contact surface exhibiting both high and low resistance. Furthermore, it is hypothesized that this specific area may possess a favorable water contact surface. There is speculation regarding the composition of the shallow portion of the line, which is believed to consist predominantly of argillaceous slate exhibiting low resistivity abnormal reactions. Conversely, the deep portion of the line is characterized by a high resistivity body primarily composed of quartzite slate reactions  
Figure 12: Line 4 (**a**) shows the resistivity distribution with depth and (**b**) shows hydrogeological profile of the survey line.
# Theoretical Study of the Wear of a Reduced-Diameter Wheel for Freight Wagons, Based on Its Diameter
## 2 Methods and Materials
### Calculation Process  
An algorithm was developed, consisting of input data blocks, calculation blocks, and two output blocks, as illustrated in Figure 3. Each of the referenced equation blocks is linked with the corresponding article title, under which it is described in detail:  
* At the top of the algorithm, the input data (represented by green blocks) was entered to the calculation blocks. The data is arranged in blocks that are added before going down to the main branches. These blocks gather information on the wheelset and bogie geometry, vehicle speed, railway line geometry, wheel geometry, load characteristics, rail geometry and contact materials properties.
* On the left, in the 3 central blocks (in light blue), the kinematic parameters for the wheelsets are obtained through relations dependent on the line geometry after inputting information on the wheelset and bogie geometry, vehicle speed, railway line geometry and wheel geometry. After that, the uncentering of each wheelset is saturated through equations dependent on the line geometry and finally, creepages are obtained through kinematics equations.
* On the right, in the 6 central blocks (in dark blue), the normal force on each wheel is computed by means of dynamics equations after entering data on the vehicle speed, line geometry, load characteristics, and some results coming from the left main branch after the saturation of uncentering. Afterwards, the geometric and normal contact problems are solved by means of [PERSON]'s solution, for which data on the wheel and rail geometries and the contact materials properties is needed. The results of [PERSON]'s solution and the creepages computed in the left main branch allows for the application of [PERSON]'s method. This solution can be applied either with constant or variable friction. At the end of this branch, the flange-rail contact is characterized by equilibrium equations.
* At the bottom of the algorithm (purple blocks), the wheel wear is computed through the energy transfer model, and the appearance of RCF is predicted with the fatigue index model.
* Regarding the symbology, the orange symbol with a diagonal cross indicates the addition of values, the orange symbol with a Greek cross represents a disjunction, the gray symbol signifies that only one flow is input, and the yellow symbol denotes a bifurcation:
# An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
## 2 Attention-Based RUL Prediction of Rotating Machinery
### Attention-Based GRU for RUL Prediction
#### 2.2.2 Attention-GRU Model for RUL Prediction
The specific calculations are as follows: \(a_{x,i}\) represents the normalized weight coefficient, and \(A_{x}\) represents the weighted sum (\(A_{x}\) is an important indicator of the relevance between the input feature (\(X_{t}\)) and the k groups of previous states). A larger \(A_{x}\) indicates a higher similarity between \(X_{t}\) and the k sets of previous states, whereas a smaller \(A_{x}\) indicates the opposite. \(S_{a}^{i}\) is the similarity weight between the \(i\)-th hidden state (\(H_{i}\)) and the input (\(X_{t}\)). \(S_{a}^{i}\), \(a_{x,i}\), and \(A_{x}\) can be calculated by Equations (10)-(12), respectively:  
\[S_{a}^{i}=X_{t}^{\mathrm{T}}\cdot H_{i},i=[t-k,t-k+1,\ldots,t-1] \tag{10}\]  
\[a_{x,i}=softmax\Big{(}S_{a}^{i}\Big{)} \tag{11}\]  
\[A_{x}=\sum_{i=t-k}^{t-1}a_{x,i}H_{i} \tag{12}\]  
To improve the stability of prediction and reduce the impact of abnormal previous data, the calculation method for the candidate state (\(\widetilde{H_{t}}\)) in the GRU is improved. The correlation coefficient between the current input feature (\(X_{t}\)) and the next state (\(H_{t-1}\)) is marked as \(a_{x,t-1}\), and the attention factor (\(\delta_{a}\)) is introduced to Equation (7) to obtain the improved calculation equation for the candidate state (\(\widetilde{H_{t}}^{\prime}\)), as shown in Equation (13):  
\[\widetilde{H_{t}}^{\prime}=\tan\hslash\left(X_{t}W_{xh}+\delta_{a}\left(R_{g} ^{\text{ }T}H_{t-1}\right)W_{hh}+b_{h}\right) \tag{13}\]  
Where the attention factor (\(\delta_{a}\)) can be calculated by Equation (14):  
\[\delta_{a}= \begin{cases}1,&\frac{a_{x,t-1}}{A_{x}}\geq 1\\ \frac{a_{x,t-1}}{A_{x}},&0\leq\frac{a_{x,t-1}}{A_{x}}<1\end{cases}  \tag{14}\]  
As Equation (14) shows, when \(a_{x,t-1}\geq A_{x}\), the current input feature shares high similarity with the previous state, and the change trend is normal and requires no adjustment; when \(a_{x,t-1}<A_{x}\), the current input has more similarity to the state of a prior period of time than to the previous state, which means that the previous state may be an anomaly, and it is necessary to reduce its weight in the calculation of candidate states.
# Research on Cavitation Characteristics of Two-Throat Nozzle Submerged Jet
## 2 Materials and Methods  
### Experimental Setup  
Figure 1 shows the schematic diagram of the cavitation jet experimental system. A high-pressure piston pump was used to provide upstream pressure with a maximum pressure of 20 MPa and a flow rate of 10 L/min. A pressure gauge with a range of 0-40 MPa was used to measure the outlet pump pressure. A turbine flowmeter was used to measure the flow rate into the nozzle. A transparent polymethyl methacrylate water tank with 800 mm \(\times\) 800 mm \(\times\) 600 mm was used as the experimental vessel. An overflow port was arranged on the top of one side of the water tank to keep the nozzle submergence depth constant. A sliding table with a screw slider was installed on the top of the water tank. The two-throat nozzle was fixed on the screw slider with three degrees of freedom, and the repeatable positioning accuracy of the screw slider was 0.01-0.02 mm. The distance between the nozzle and the liquid level in the water tank should be greater than 200 mm to ensure that the submerged cavitation jet is not disturbed by the fluctuation of the liquid level. Filtered tap water was used as the working fluid during the experiment. As a large amount of heat will be released due to the collapse of cavitation bubbles during the experiment, the water temperature of the water tank will keep rising. Thus, the water temperature should be controlled at 15-18 \({}^{\circ}\)C during the experiment. The water temperature was measured using a mercury thermometer every 5 min. Once the water temperature exceeds 18 \({}^{\circ}\)C, the experiment should be stopped immediately. The experiment can continue after the water in the water tank is replaced. The structure of the two-throat nozzle investigated in this paper was designed based on the literature [29; 30], as shown in Figure 2. According to [PERSON]'s principle, the hydrostatic pressure decreases, and bubbles are produced due to a reduction in its area when the liquid flows through the first throat of the two-throat nozzle.  
As the area inside the nozzle increases, the pressure rises, and the bubble ruptures to form several bubble nuclei moving downstream with the flow. Similarly, several bubble nuclei are produced in the second throat of the two-throat nozzle. Theoretically, the presence of bubble nuclei is a prerequisite for cavitation. Increasing the number of bubble nuclei can enhance the cavitation performance of the nozzle submerged jet. The structural parameters of the two-throat nozzle are shown in Table 1.
# Effective Use of Flax Biomass in Biorefining Processes
## 2 Materials and Methods
### Analytical and Testing Methods  
The chemical composition of the main cultivated flax biomass components before and after alkaline pretreatment was determined according to standard methods. The cellulose content was determined by the Seifert method using a mixture of acetylacetone and dioxane [33]. The lignin content was determined by the Tappi (Technical Association of the Pulp and Paper Industry) method (T-222 om-06) using concentrated sulfuric acid [34]. The theoretical hemicellulose content was arithmetically calculated as the difference between holocellulose (TAPPI--T9 wd-75) and cellulose [35]. All chemical composition results were the average of three measurements and were calculated on the basis of the dry weight of the raw material.  
In addition, for flax biomass before and after pretreatment, using a scanning electron microscope (SEM, S-3400N, Hitachi, Tokyo, Japan) under high vacuum conditions, the physical morphology was determined. Biomass samples were covered with gold dust.  
The ethanol concentration from flax biomass was determined by HPLC on Elite LaChrom liquid chromatograph from VWR-Hitachi using an RI L-2490 detector, Rezex ROA 300 \(\times\) 7.80 mm column from Phenomenex, at a flow rate of 0.6 mL-min\({}^{-1}\), at 40 \({}^{\circ}\)C. The samples were loaded onto the column at 10 \(\mu\)L. The quantitative identification was performed by the external standard method using the peak area (measurement and computer integration using the Ez-Chrom Elite program).  
Mechanical properties tests of the composites were carried out at room temperature with a universal testing machine Inspekt Table 50 (Hegewald & Peschke MPT, Nossen, Germany) according to ISO 527 and ISO 178, respectively [36; 37; 31]. In both tests, the crosshead speed was set to 5 mm-min\({}^{-1}\). Tensile tests in accordance with ISO 527-1 and 2 were carried out using an MFA clip-on extensometer (MF Mess- & Feinverktechnik, Velbert, Germany) with a nominal length of 20 mm [36; 31].
## 2 Results and Discussion
### Volatile Compounds
#### 2.2.1 Hydrocarbons  
The concentration of the volatile compounds distributed in the different chemical series pointed out that the amount of hydrocarbons was higher in SM and SF and lower in BF and ST. Among the hydrocarbons, limonene and octane were the most abundant in the four locations. Hydrocarbons were at higher concentrations in SF than in the muscles with the exception of limonene and \(\alpha\)-pinene. According to diverse authors [19, 20], the presence of limonene in the hams has been associated with the pig feeding and it contributes with \"lemon\" sensory notes to ham flavor. Taking into account the odor thresholds, octane and limonene are, among the quantified hydrocarbons, the major contributors to ham odors although they do not contribute in same manner to all the hams. Thus, while the octane concentrations are higher than its odor threshold (OT) in samples from all breeds--Table 2 shows high \(p\)-value when comparing non Iberian _vs._ Iberian hams, the concentration of limonene has been quantified in amounts higher than its OT in all the Iberian hams, whichever the analyzed muscle, the highest values corresponding to SM (Table 3). Limonene, in concentrations higher than its OT, were also determined in all the muscles of Gascon and Basque hams, which agrees with [PERSON] _et al._[19], and in two samples from PDO Teruel. This compound always contributed to lemon odor in hams with a maturation time higher than 12 months (Iberian hams maturation time was \(\geq 18\) months). Limonene was not perceived, on the contrary, with hams with short processing times, such as some American hams [21]. The concentration of \(\alpha\)-pinene is higher than its OT in most of the samples, which means this compound contributes to ham aroma with the pine sensory note regardless the pig breed. The high concentration of hydrocarbons in Iberian hams was also observed for the total sum of the concentrations of hydrocarbons.
## 4 Experiments and Results
Also, it can be seen that the proposed model had a much higher probability of having a low absolute error compared to other comparative models. Specifically, the absolute error of the proposed model was 15.46% less than 1 dBm, and almost half of it was less than 3 dBm. Finally, this model could guarantee that the absolute error of more than 90% was less than 10 dBm. The result shows that the meta-learning based model achieved lower, or similar, performance compared to the model using only 3D CNN. Figure 8: Absolute Error Cumulative Density Function. Figure 7: Absolute Error Comparison—LoS and NLoS Combined. In addition, When the absolute error of less than 10 dBm was seen, the cumulative absolute error of multi-way local attentive learning with single model and multi model was lower than that of the other three models. However, in the case of using the multi model, the error larger than 10 dBm was higher than that of other models. It can be seen that in the combined process of multi-way local attentive learning using multi models, the learning of each model rather interfered with one another's learning. However, in the case of using the single model, the absolute error tended to be lower than that of other models. The application of meta learning and multi model did not have much effect on the regression model learning for 5G mmWave path loss in urban areas, while the proposed multi-way local attentive learning with single model could be used. Tables 8-10 present the partition-wise performance of path loss prediction in terms of the MAEs and their variances of different models. It can be clearly noticed the multi-way local attentive learning--single model performed the best in a most consistent way. This means that the proposed model gave a more robust performance than the other models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Relative Angle** & **Single 3D CNN** & **Multiple 3D CNN** & **3D CNN with** & **Aster Propagation** \\
**Section** & **Model** & **Model** & **Meta-Learning** & **Model** \\ \hline
1 & 2.68 & 2.48 & 6.73 & 5.61 & 2.88 \\
2 & 3.18 & 4.39 & 5.41 & 5.37 & 7.2 \\
3 & 8.65 & 9.29 & 9.09 & 9.95 & 10.63
## 4 Results and Analysis
### Analysis of ARIMA Model Construction and Prediction Results  
#### 4.3.1 ADF Test  
For the data on the number of forest fires in Yunnan Province from 1991 to 2021, the Augmented Dickey-Fuller (ADF) unit root test was employed to verify their stationarity. Given the absence of a distinct trend in the original data, the ADF test, including only the intercept term, was conducted. Detailed results of this test are presented in Table 5.  
In the ADF test, the \(p\) value was 0.5336, which is greater than 0.05. This suggests that at the 95% significance level, the null hypothesis is accepted, pointing to non-stationarity in the data. Given the visible non-stationarity in the forest fire data, a first-order differencing was applied, with the results detailed in Table 6.  
Table 6 presents the results of the Augmented Dickey-Fuller (ADF) test for the first-differenced data. As indicated in the table, the \(p\)-value is 0.000, which is less than 0.05.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(p\)-Value & t-Statistic & \begin{tabular}{c} **Test Key Values at 10\%** \\ **Level of Significance** \\ \end{tabular} & \
\begin{tabular}{c} **Test Key Values at 5\%** \\ **Level of Significance** \\ \end{tabular} &
\begin{tabular}{c} **Test Key Values at 1\%** \\ **Level of Significance** \\ \end{tabular} \\ \hline
0.000 & \(-\)5.889008 & \(-\)2.627420 & \(-\)2.976263 & \(-\)3.699871 \\ \hline \hline \end{tabular}
\end{table}
Table 6: ADF unit root test (first-order difference).  
Figure 8: (**a**) Residual frequency histogram for number of fires; (**b**) Normal Q–Q (Quantile–Quantile); plot; (**c**) Residual frequency histogram of fire area; (**d**) Normal Q–Q (Quantile–Quantile) plot. **Note:** The red line represents the theoretical distribution line of expectation and the blue dots are the actual observed results.  
This signifies that the data on forest fire occurrences, after the first-order differencing, are stationary and have passed the significance test. Utilizing the Q-statistic test [56] and employing EViews 8.0 software, the autocorrelation and partial autocorrelation plots were obtained, as shown in Figure 9.
## 3 Materials and Methods
### LBS Missing Path Completion
#### 3.5.2 Evaluation of the Effectiveness of Missing Path Completion for LBS Data  
To validate the accuracy of the travel mode completion results, this study randomly selected 2000 Type B LBS data records containing actual travel mode information for testing the travel mode completion process. Among them, 1000 records were for private vehicle travel, while the remaining 1000 were for public transportation. For each OD pair, this study generated 20 candidate paths, including both public transportation and private vehicle travel modes. The closest travel path selection and its corresponding travel mode were obtained using the method described in Section 4.2 and compared with the original recorded travel mode.  
To evaluate the performance of the path completion method, this study designed the following validation strategy. Since Type B data inherently lack path information and cannot serve for validation, we intentionally introduce artificial missing paths within partial of Type A data. Specifically  
the link coincidence rate (LCR) and path length coincidence rate (PLCR), given as follows, as the performance metric of the completion method.  
\\[LCR_{\\mathcal{P}_{L}}=\\frac{\\left|P_{S}^{\\prime}\\cap P_{L}\\right|}{\\left|P_{L} \\right|} \\tag{13}\\]  
\\[PLCR_{\\mathcal{P}_{L}}=\\frac{\\sum_{\\mathbf{s}\\in(P_{S}^{\\prime}\\cap P_{L})}l_{s }}{\\sum_{\\mathbf{k}\\in P_{L}}l_{k}} \\tag{14}\\]  
where \\(LCR_{\\mathcal{P}_{L}}\\) is LCR of the completion result, \\(PLCR_{\\mathcal{P}_{L}}\\) is the PLCR of the completion result, \\(P_{S}^{\\prime}\\cap P_{L}\\) is the overlapping links between the path \\(P_{S}^{\\prime}\\) obtained from the simulation model and the path \\(P_{L}\\) obtained from LBS data. \\(s\\) is the link common to both \\(P_{S}^{\\prime}\\) and \\(P_{L}\\), and \\(k\\) is link in \\(P_{L}\\).  
To demonstrate the performance of our DDSM, we compare the accuracy between our model and two baseline methods:  
(a) Dual-driven simulation model (DDSM);  
(b) Shortest path algorithm (SP), in which the shortest travel distance among the generated multiple paths is selected;  
(c) Time-dependent shortest path algorithm (TDSP), in which the travel time for each link is calculated by dividing the link length by its speed limit, and then the path with the shortest travel time from among the multiple generated paths is selected.
# ICN-Based Enhanced Content Delivery for CDN
###### Abstract  
With the rapid growth of internet traffic, the traditional host-to-host TCP/IP architecture is subject to many service limitations faced with content-oriented applications. Various novel network architectures have been proposed to solve these limitations, among which Information-Centric Networking (ICN) is one of the most prominent. ICN features the decoupling of content (service) from the physical devices storing (providing) it through location-independent naming, and offers inherent enhancement to network performance, such as multicast and in-network caching. ICN in-network caching has been extensively studied, and we believe that it may also be the main incentive for ISPs to deploy ICN. A CDN (content delivery network) is a typical content-oriented network paradigm that aims to provide the fast delivery of content. In this paper, we leverage the advantages of the in-network caching of ICN to enhance the content delivery efficiency of CDN by integrating ICN as a service. First, we present our design of a content delivery network enhanced with ICN, called IECDN. Additionally, we formulate a mathematical model to optimize the performance of our proposed design and conduct a series of evaluations. The results indicate that our proposed design provides significant performance gains while reducing bandwidth consumption and shows better resilience to traffic surge.  
CDN; ICN; in-network caching; multi-commodity flow problem Article ICN-Based Enhanced Content Delivery for CDN  
[PERSON]; [PERSON] ICN-Based Enhanced Content Delivery for CDN