esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Swmm  
The SWMM is a distributed hydrological model in which complex terrain redistributes rainfall during a rainfall event. Rainfall becoming runoff is net rainfall after initial losses such as interception by vegetation and evaporation. The net rainfall then flows into the drainage network system. If the drainage network system is overloaded, the rainwater that overflows from the system gradually fills the low-lying reservoirs and becomes surface runoff.  
The SWMM uses the Rain Gauge module to calculate and simulate rainfall. Rainfall events in the study area can be imported or simulated using the rainfall intensity formula:  
\[i=\frac{134.5106(1+0.4784\text{log}T_{M})}{(t+32.0692)^{1.1947}} \tag{1}\]  
where  
\(i\)--Rainfall Intensity  
The initial loss of rainwater due to interception by plants and evaporation is calculated using the Horton infiltration model, as follows:  
\[f_{s}=f_{t}+(f_{0}-f_{t})e^{-kt} \tag{2}\]  
where  
\(f_{s}\)--Ground infiltration rate  
\(f_{t}\)--Infiltration stability value  
\(f_{0}\)--Initial infiltration rate (mm/h)  
\(k\)--Infiltration decay rate (h\({}^{-1}\))  
\(t\)--Infiltration duration (h)  
The process of net rainfall flowing through pipes and nodes and finally flowing to the drain represents the hydraulic simulation process in the SWMM. The drainage network model presented in this paper is a tree-like drainage network, and the water flow within the drainage network is in a gravity flow state. Consequently, the dynamic wave model is employed in the three calculation methods of constant flow, moving wave, and dynamic wave. The simulation formula is as follows:  
\[\begin{cases}\frac{\partial Q}{\partial t}+\frac{\partial A}{\partial t}=q\\ \frac{1}{g}+\frac{\partial v}{\partial t}+\frac{v}{g}+\frac{v}{g}+\frac{\partial v }{\partial x}+\frac{\partial H}{\partial x}=S_{0}-S_{f}\end{cases} \tag{3}\]  
where  
\(Q\)--Pipeline flow (m\({}^{3}\)/s)  
\(A\)--Water crossing section area (m\({}^{2}\))  
\(x\)--Pipe length (m)  
\(t\)--Time (s)  
\(q\)--Incoming flow per unit length (m\({}^{3}\)/s)  
\(g\)--Design velocity of the stormwater pipe (m)  
\(v\)--Roughness coefficient of the stormwater pipe (m)  
\(H\)--Diameter of the stormwater pipe (m)  
\(S_{0}\)--Slope of the stormwater pipe  
\(S_{f}\)--Friction term  
The Manning formula calculates surface runoff as follows:  
\[v=\frac{D^{\frac{3}{2}}\times l^{\frac{1}{2}}}{4^{\frac{3}{2}}\times N} \tag{4}\]where  
\(v\)--Design flow rate of storm pipe (m/s)  
\(N\)--Roughness coefficient of rainwater drainage network  
\(D\)--Radius of storm water network (m)  
\(I\)--Hydraulic grade  
The maximum volume of the stormwater pipe \(Q_{p}\) is calculated as follows:  
\[Q_{p}=\frac{\pi D^{2}}{4}\times v \tag{5}\]
## 4 Experimental Evaluation  
Different experiments have been run to characterize and validate the localization and communication systems. The first experiment is developed in an obstacle-free environment with a secondary and a primary node to test the stochastic 433 MHz link. The second experiment is developed with a primary node and a base station node in an obstacle-free environment to analyze the 166 MHz link over the implementation of a specific protocol. Finally, an experiment involving the GPS has been carried out to estimate the time required for the GPS to obtain the current time and position.
# Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments +
## 3 Radio Channel Analysis
### 3D-RL Calibration and Validation
This is because Cases 3, 4 and 5 considered larger angular resolutions than the other cases (one degree and 0.5 degrees of angular resolution) in comparison with 0.2 degrees in the other cases. In Case 1, the angular resolution was high, but the number of reflections was the lowest. It was shown that Case 1 did not have good agreement with the measurements. It was observed that Case 2 and Case 6 (both with angular resolutions of 0.2 degrees but different numbers of reflections considered) both had a good match with real measurements. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\Delta\sigma\) **(rad)** & \(\Delta\theta\) **(rad)** & **Cuboids Size (m)** & **Number of Reflections** & **Number of Rays** \\ \hline Case 1 & \(\pi/900\) & \(\pi/900\) & 0.1 & 5 & 810,000 \\ Case 2 & \(\pi/900\) & \(\pi/900\) & 0.1 & 7 & 810,000 \\ Case 3 & \(\pi/360\) & \(\pi/360\) & 0.1 & 7 & 129,600 \\ Case 4 & \(\pi/180\) & \(\pi/180\) & 0.1 & 7 & 32,400 \\ Case 5 & \(\pi/180\) & \(\pi/180\) & 0.1 & 13 & 32,400 \\ Case 6 & \(\pi/900\) & \(\pi/900\) & 0.1 & 13 & 810,000 \\ \hline \end{tabular}
\end{table} 
Table 1: Different cases for the convergence analysis of the algorithm. Table 2 shows the mean error and standard deviation of the comparison of the different simulation cases with the real measurements. It is also shown the simulation computational time of each one of the considered cases. It can be seen that Case 2 and Case 6 have the lowest mean error, as stated before. In Cases 1, 3, 4 and 5, the mean error increases considerably. With regards to the simulation computational time, it is observed that Case 6 has an increase of 186,861 s more than Case 2, with a slight decrease in the mean error. So, it can be concluded that, for the underground tunnel scenario, the optimal simulation parameters are Case 2, achieving a good trade-off between accuracy results and simulation computational burden.
## 3 Results and Analysis
### Influence Analysis of Various Factors
#### 3.3.3 Cover Thickness  
The relative cover thickness is calculated from the ratio of the cover thickness to the height of the section steel (\(C_{\text{ss}}/d\)). The cover thickness refers to the distance between the section steel and the outer surface of RAC. The relationship is shown in the following equations:  
\[\overline{\tau_{s}} =(1.566C_{\text{ss}}/d-0.318)f_{t} \tag{9}\] \[\overline{\tau_{u}} =(0.983C_{\text{ss}}/d+0.317)f_{t}\] (10) \[\overline{\tau_{r}} =(0.469C_{\text{ss}}/d+0.243)f_{t} \tag{11}\]  
It can be seen from Figure 8c that the average characteristic bond strength obviously increases with the increase of the cover thickness.
## 8 Performance Analysis  
### Complexity Analysis  
In this section, we analyze the performance of the proposed intrusion detection system against selective routing attacks. The performance is studied under the following metrics: communication complexity and message complexity, which are defined as follows:  
Figure 6: Resiliency against upstream-node effect.  
* Communication complexity (CC): The number of one-hop transmission each node needs to perform initially and periodically.
* Message complexity (MC): The size in terms of bits of control information transmitted by each node initially and periodically.  
The complexity results are summarized in Table 1. Initially, each node \(i\) broadcasts \(Witnessed_{i}\) with \(TTL=2\), i.e., the messages are broadcast by \(i\) and its neighbors. This operation leads to a communication complexity of \(O(d_{max}+1)\), where \(d_{max}\) is the maximum node degree of the network. The same complexity is incurred by sending \(RN_{i}\). By adding the broadcast operation of \(Witnessing_{i}\), the total communication cost becomes proportional to \(O(2d_{max}+3)\) per node. Let \(RN\) and \(TC\) be the size of \(RN_{i}\) and TOHC value, respectively. As the size of \(Witnessed_{i}\) and \(Witnessing_{i}\) is \(O(d_{max}\log_{2}N)\), the message complexity in the initialization phase is \(O((d_{max}^{2}+2d_{max})\log_{2}N+(d_{max}+1)RN)\). The proposed IDS does not cause any additional packets during the periodic step, but it piggybacks some control information to the beacon packet. During the time period of \((|Relay_{i}|+2)\) slots, each node \(i\) relays the ratings of its neighbors in \(Relay_{i}\) and its two own ratings. The periodic message complexity is \(O(d_{max}+\log_{2}N)\), as explained in Section 5.2.2. In addition, the TOHC value is appended to each observation.
# Influence of Sea Level Anomaly on Underwater Gravity Gradient Measurements
## 6 Conclusions  
Herein, we processed the daily grid data of sea level observed by global ocean satellites and quantitatively calculated the disturbing gravity gradient caused by sea level anomalies using the right rectangular prism method. The results show the following:  
1. The gravity gradient forward modeling results are related to the integration range, and the selection of the integration range is related to the calculation height (depth). Therefore, to obtain relatively accurate forward modeling results, different integration ranges need to be considered when calculating at different heights (depths).
2. Based on the measurement accuracy of the gravity gradiometer 1 E, within 50 m below the mean sea level, sea level anomalies at local positions will significantly affect the underwater gravity gradient measurements, with a maximum contribution exceeding 10 E and the maximum difference between different locations exceeding 20 E. Moreover, the change of the sea level anomalies with time will significantly impact the underwater gravity gradient measurements, with the maximum change value exceeding 20 E, and the impact will accordingly change with the seasonal change of the sea level anomalies. Therefore, underwater carriers need to consider the disturbing gravity gradient caused by sea level anomalies when using gravity gradient for underwater detection and navigation.  
Conceptualization, B.J. and S.B.; software, P.X.; validation, B.J.; formal analysis, P.X.; writing--original draft preparation, P.X.; writing--review and editing, B.L.; visualization, P.X.; project administration, B.J.; funding acquisition, B.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 41874091 and 42174051.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://cds.climate.copernicus.eu/cdsappff/dataset/satellite-sea-level-global?tab=overview](https://cds.climate.copernicus.eu/cdsappff/dataset/satellite-sea-level-global?tab=overview)]([https://cds.climate.copernicus.eu/cdsappff/dataset/satellite-sea-level-global?tab=overview](https://cds.climate.copernicus.eu/cdsappff/dataset/satellite-sea-level-global?tab=overview)) (accessed on 22 October 2021).  
The sea level dataset was provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and the Copernicus Climate Change Service (C3S). The authors are indebted to anonymous reviewers for helping to improve the manuscript.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods  
### Tested Soil and Amendments  
The soil was procured from Chengmenshan Copper Mine (115\({}^{\circ}\)48\({}^{\prime}\)32\({}^{\prime\prime}\) E, 29\({}^{\circ}\)41\({}^{\prime}\)26\({}^{\prime\prime}\) N), situated in the Chaisang District of Jiujiang City, Jiangxi Province, China. Employing a meticulous five-point sampling method, a homogeneous soil sample was extracted from the surface layer (5-30 cm). Employing the quartile method, the soil samples underwent a partition into two segments. One segment was transported to the laboratory, where it underwent air-drying, grinding, and sieving through a 2 mm nylon sieve for subsequent utilization in pot experiments. The second segment found its place of preservation in a \(-80\)\({}^{\circ}\)C freezer to facilitate DNA extraction. The steel slag utilized in this experimental inquiry emanated from a local iron and steel establishment, while vermicompost was procured from Huizhou Nianhe Agricultural Technology Co., Ltd., Huizhou, China. The examination of the surface microphotographs of vermicompost and steel slag was conducted via Scanning Electron Microscopy (SEM, Zeiss EVO18, Tokyo, Japan). X-ray Diffraction (XRD, Malvern Panalytical Empyrean, San Jose, CA, USA) was instrumental in discerning the mineral composition of the steel slag. Fourier Transform Infrared Spectroscopy (FTIR, Thermo Scientific Nicolet iS20, Waltham, MA, USA) served as the analytical tool for probing the surface functional groups of vermicompost. The fundamental physicochemical parameters of the soil and amendments are presented in the following table (Table 1).
# Algorithm Providing Ordered Integer Sequences for Sampling with Replacement Confidence Intervals
## 4 Discussion
### Question 2  
How should one deal with proportions instead of variables?  
The main issue is people's tendency to simplify. For instance, \(\frac{1}{2}\) is often written as \(0.5\); for \(\frac{1}{3}\), we write \(0.(3)\) or \(0.33\ldots\), but it is, in fact, not the same thing when it comes to sampling.  
The preference for real numbers is obvious: people are much more comfortable with them and they quickly indicate the order of magnitude (\(0.0\ldots\) or \(0.1\ldots\) is close to \(0\), \(0.8\ldots\) or \(0.9\ldots\) is close to \(1\), \(0.4\ldots\) or \(0.5\ldots\) is close to \(0.5\)), which allows for instant interpretation. At the same time, however, it lets something slip through the cracks. One can express (with three decimal places, for example) any real sub-unit number, but its exact value cannot be obtained as a proportion of two integers, except under certain conditions and very few situations (one of these being that the sample size is \(1000\); for the sample size \(997\), however, there is no non-trivial situation).  
Thus, using the same numbers, for \(x=26\) positive outcomes from \(m=30\) trials, in order to pass along all information, a convention is first needed to express the proportion as is without simplifying it (proportion is then \(\frac{26}{30}\)), and the confidence interval is expressed accordingly with fractions too.  
The BalancedCI at \(95\%\) desired coverage (\(\alpha=5\%\)) for \(\frac{26}{30}\) is \(\lfloor\frac{23}{30},\frac{29}{30}\rfloor\), and its actual coverage is \(94.8\%\).
# Optimization of Xylose Recovery in Oil Palm Empty Fruit Bunches for Xylitol Production
## Materials and Methods
### Sugar Analysis (Xylose)  
Xylose concentration in the hydrolysate was analyzed using HPLC (Alliance HPLC System, Waters, Milford, MA, USA) using a method developed by National Renewable Energy Laboratory (NREL) [32]. The xylose was separated using an HPX-87H Biorad-Aminex column (length of 300 mm, a particle diameter of 9 \(\upmu\)m, and an internal diameter of 7.8 mm), which was maintained at 60 \({}^{\circ}\)C and detected using a 2414 refractive index (RI) detector (detector temperature 40 \({}^{\circ}\)C). The column conditions were set as follows: sample volume 20 \(\upmu\)L, a mobile phase of 0.005 M sulfuric acid, a flow rate of 0.6 mL/min, a detector temperature of 40 \({}^{\circ}\)C, and processing time of 30 min. Xylose as the standard in HPLC analysis was purchased from Sigma-Aldrich (Singapore), Chemicals for citrate buffer were purchased from E-Merck (Singapore).  
Xylose recovery was calculated using Equation (1).  
\[\%\;x\;ylose\;recovery=\;\frac{[x\;ylose\;produced]\;x\;working\;volume\;of\; hydrolysis}{mass\;of\; OPEFB\;used}\;\times\;100. \tag{1}\]
# Tightly-Coupled Data Compression for Efficient Face Alignment
## 6 Discussion  
This paper proposed an adaptive data compression method for the training model of an SDM-based face-alignment algorithm. An efficient method to quantize the data according to the probability density-aware K-means algorithm was proposed in this paper. Furthermore, our quantization method was tightly coupled into the training process so that the accuracy loss was minimized. Experimental results proved that our proposed method was on par with the standard method while only needing less than 1/5 of the original storage space. Our method even achieved comparable results with state-of-the-art deep learning based methods in images with moderate head  
\begin{table}
\begin{tabular}{c c} \hline
**Module of the Algorithm** & **Average Time (ms)** \\ \hline Probability density-aware initialization & 12.3 \\ K-means clustering & 256.7 \\ Data quantization & 9.4 \\ \hline \end{tabular}
\end{table}
Table 6: Extra computational cost for each part of the algorithm in the tightly-coupled training process.  
\begin{table}
\begin{tabular}{c c c} \hline
**Index** & **Average Score of Uncompressed Training Model SDM [6]** & **Average Score of Our Compressed Model Method** \\ \hline
1 & 4.10 & 4.05 \\
2 & 3.80 & 3.70 \\
3 & 4.15 & 4.10 \\
4 & 3.95 & 4.00 \\
5 & 4.40 & 4.45 \\
6 & 3.85 & 3.75 \\ \hline \end{tabular}
\end{table}
Table 5: Comparisons of average scores for the two methods.  
poses, and it consumed an order of magnitude less memory storage space. Our proposed method has the potential for the SDM algorithm to be applied in mobile applications. In our method, all the data were quantized with the same number of bits. However, it can be concluded from Figure 4 that many values were concentrated around 0. It is expected that these values can be represented by fewer numbers of bits or even pruned. In the future, we plan to do research along this direction so that the compression power can be further enhanced.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; and [PERSON] wrote the paper.  
This research was partially funded by National Natural Science Foundation of China under contract numbers 61501451, 51875380, 51375323 and 61563022, Cooperative Innovation Fund-Prospective of Jiangsu Province under grant B72016044-01, Major Program of Natural Science Foundation of Jiangxi Province, China, under grant 20152 ACB20009, high level talents of \"Six Talent Peaks\" in Jiangsu Province, China, under grant D2 XX-046, Qing Lan Project of Jiangsu Province, China.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation
## 2 Initial Concepts
### Computer Cluster  
Software long run time is often an issue, especially in big data context [48]. To decrease software experiment execution time, one can use faster hardware or optimize underlining algorithms. Some hardware options to decrease execution time include FPGA [49; 50], GPU [51], faster processors [52] or computer clusters [53; 54]. Algorithm optimization examples are found in studies by [PERSON] et al. [55], [PERSON] et al. [56] and [PERSON] et al. [57].  
Among computer clusters, one well-known implementation is Beowulf cluster, made by connecting consumer grade computers on a local network using Ethernet or another suitable connection technology [58]. The term Beowulf cluster was coined by [PERSON] et al. [59], which created the topology on NASA facilities as a low-cost alternative to an expensive commercial vendor built form High-Performance Clusters. Beowulf cluster is widely used by diverse research fields such as Monte Carlo simulations [60], drug design [61], big data analysis [48] and neural networks [62].  
According to [PERSON] et al. [39], archiving parallel performance on chosen hardware architecture depends on factors such as scheduler overhead, data/task granularity, cache fitting and data synchronization. There exist different abstraction levels of parallelism strategies that can be combined [41]. Often, a systematic comparison between parallelism strategies is necessary to verify which one has better performance [39]. A comparison of two parallelism strategies in a Beowulf cluster is shown by [63].  
Data Parallelism strategy, as stated by [PERSON] et al. [40], is when one processing data slice does not have dependency on the next one. Thus, data are divided into several data slices and processing them equally by different processors. The Task Parallelism objective is to spawn tasks across processors to speedup one scalable algorithm. Tasks can be spawned by a central task system or by a distributed task system, both adding processing overhead, with a distributed task system achieving less overhead [39].
## 5 Further Discussion
### The Effect of the Damping Layer's Position  
We set the thickness of the damping layer to a constant value of \(h_{d}=0.0002\,m\) and explore four distinct scenarios regarding the placement of adhesive bonds:  
\[\begin{split} Case\quad 1:\quad x_{d1}&=0.18\,m,\quad x_{d3} =0.26\,m,\\ Case\quad 2:\quad x_{d1}&=0.16\,m,\quad x_{d3} =0.28\,m,\\ Case\quad 3:\quad x_{d1}&=0.14\,m,\quad x_{d3} =0.3\,m,\\ Case\quad 4:\quad x_{d1}&=0.12\,m,\quad x_{d3} =0.32\,m.\end{split} \tag{33}\]  
Figure 16 illustrates the effects of different damping layer configurations on the modal loss factor. As shown in Figure 16, even though Case 4 has the longest adhesive length, the changes in the modal loss factor of the system are not markedly different from those with shorter adhesive lengths. As the mode shape order increases, the impact of varying adhesive lengths becomes less pronounced. This suggests that the length of the adhesive has a minimal effect on the ABH effect in this context and can be considered insignificant.
# Research on Ride Comfort Control of Air Suspension Based on Genetic Algorithm Optimized Fuzzy PID
## 5 Simulation Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Criteria** & **Passive** & **Fuzzy PID** & \begin{tabular}{c} **GA-Fuzzy** \\ **PID** \\ **Optimization** \\ \end{tabular} & \begin{tabular}{c} **Percentage of** \\ **GA-Fuzzy PID** \\ **Optimization** \\ \end{tabular} \\ \hline \begin{tabular}{c} Vehicle Vertical Acceleration \\ (m/s\({}^{2}\)) \\ \end{tabular} & 1.8062 & 1.3908 & 1.2643 & 23\% & 30\% \\ \hline \
\begin{tabular}{c} Suspension Wheel Travel \\ (m) \\ \end{tabular} & 0.0337 & 0.0276 & 0.0249 & 18\% & 26\% \\ \hline
\begin{tabular}{c} Dynamic Tire Load \\ (kN) \\ \end{tabular} & 6.0895 & 5.7241 & 5.5414 & 6\% & 9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Test conditions of B-grade road surface at 50 km/h. the air suspension fuzzy PID control optimized by GA demonstrates more noticeable control effects and superior suspension performance, further improving the smoothness and comfort of the vehicle.