esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Numerical Investigation  
As described above, the seismic stability was evaluated by performing a finite element analysis for a hardfill dam body (Figure 4) with three types of slopes (1:0.6, 1:0.8, and 1:1) and a height of 50 m, as constructed on a soft rock foundation based on the properties of the hardfill determined through the indoor laboratory tests. In this analysis, the state of the freshwater above the planned flood level was applied by setting the most vulnerable condition for the stability of the dam body. The hardfill was modeled as an elastic body with a unit weight of 24.1 \(\mathrm{N}\mathrm{S}\mathrm{/}\mathrm{m}^{3}\), Young's modulus of 991 MPa, and Poisson's ratio of 0.29. Furthermore, the damping of the material was set in accordance with the first two eigenfrequencies of the dam and with an estimated damping coefficient equal to 5% for defining the Rayleigh's coefficients. The dam foundation ground was assumed according to the ground conditions near Wonjucheon Dam in South Korea, which was determined to be suitable as a candidate site for the hardfill dam construction. The static and dynamic geotechnical parameters for the dam foundation ground for the analysis were selected based on the results from subsurface investigations conducted in the area (Table 5).  
Figure 4: Cross-sections of dams for finite element (FE) analysis. (a) Dam slope gradient of 1:0.6 (vertical-to-horizontal ratio); (b) dam slope gradient of 1:0.8 (vertical-to-horizontal ratio); (c) dam slope gradient of 1:1.0 (vertical-to-horizontal ratio).  
A time history analysis method was applied for the dynamic analysis. Three types of seismic waves were used as the input seismic waves: Ofunato and Hachinohe waves, i.e., representative short-period and long-period seismic waves widely used in dynamic analysis, respectively, and artificial waves generated by reflecting the characteristics of the Wonjucheon dam site (Figure 5). In the analysis domain, the left and right sides were fixed in the horizontal direction, and the lower part was set in the vertical direction. A viscous boundary condition was applied to show a behavior similar to reality when a seismic wave is applied by absorbing the wave reaching the boundary.  
Lastly, from the numerical analysis results, the seismic stability of the hardfill dam body was evaluated by comparing the maximum stress generated inside the hardfill dam body with the \"required hardfill strength\" as applied with a safety factor of 1.5 under the seismic load condition [3].
## 2 Materials and Methods
### Analysis Method  
A precise and reliable analytical method was developed for the simultaneous detection of five herbicides using an Agilent 7890A-7000C GC-MS (Agilent Technologies, Palo Alto, CA, USA) equipped with an HP-5 MS quartz capillary column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness). The analysis was performed in electron impact (EI) ionization mode. The interface and ion source temperatures were set to 280 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. Helium was used as the carrier gas at a constant flow rate of 1 mL/min under non-split injection conditions. The injection volume was 5 \(\upmu\)L, with the inlet temperature maintained at 280 \({}^{\circ}\)C. The oven temperature program was as follows: initial temperature of 60 \({}^{\circ}\)C held for 1 min, ramped to 170 \({}^{\circ}\)C at 40 \({}^{\circ}\)C/min (no hold), then increased to 260 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and held for 3 min.
## 3 Remaining Useful Life Prediction Algorithm Based on Double Bidirectional Long Short-Term Memory  
### The Determination of Failure Thresholds  
The failure threshold for bearings is established using the 3\(\sigma\) criterion, based on the description in the referenced literature [47; 48; 49; 50; 51; 52; 53]: approximately 99.73% of the data pointswill fall within the range defined by the mean (\(\mu(t)\)) plus or minus three times the standard deviation (\(\sigma(t)\)), expressed as (\(\mu(t)-3\sigma(t)\),\(\mu(t)+3\sigma(t)\)); therefore, the \(3\sigma\) criterion is widely applied in failure monitoring of bearings. The method and steps for determining the failure threshold of bearing signals are specifically as follows [48]:  
1. The mean of the sample [48]: \[\mu(t)=\sum_{U}^{t}x_{i}\] (17) where \(U\) represents the length of the sample, and \(t\) represents the time point of the sample.
2. The standard deviation of the sample [48]: \[\sigma(t)=\sqrt{\frac{1}{U-1}\sum_{U}^{t}(x_{i}-\mu(t)^{2})}\] (18)
3. The failure threshold of a bearing is represented by \(DT\)[48]: \[|DT-\mu(t)|\geq 3\sigma(t)\] (19)
4. Failure Judgment: An early warning mechanism will be triggered when condition \(x(t)\geq\mu(t)+3\sigma(t)\) is met.
# In-Band Medium-Frequency R-Mode Signal Quality Estimation +
## 5 Real Data Analysis
In the future, we plan to extend this study by analyzing longer data sets and try to better characterize the different error sources. Figure 5: Fractional part of the phase in rad for Gross Morhdorf (**a**), Hammerodde (**b**), Helgoland (**c**), and Zeven (**d**). Figure 6: 2D histogram of the computed accuracy (1\(\sigma\)) as function of the estimated \(C/N_{0}\). The predicted accuracy is plotted as dashed red line.
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 3 Fundamentals and Analytical Models  
This section describes the analytical models required for the proposed method of optimizing the cost and performance of the VHTS satellite network. In addition, a new analytical model of the _CINR_ is presented as a function of the number of beams, the \(G/T\) ratio of user stations, and the annual availability.
## 3 Results and Discussion
### Isoflavones Content in Soybeans
[31] extracted soybean seeds using pure acetone, dichloromethane, and methanol at different concentrations. The researchers found that neither acetone nor dichloromethane was a good extractant for isoflavones, while glucosides were better extracted at methanol concentrations of 70% and 100, and malonylglucosides were extracted at a methanol concentration of 50%. The highest extraction efficiency was achieved using a mixture of three solvents in a 33/33/33% (\(r/v/v\)) ratio. The concentrations of isoflavones were 0.318, 0.339, 0.712, 0.158, and 0.8025 mg/g DM for daidzin, genistin, malonyldaidzin, malonylglycitin, and malonylgenistin, respectively. The employment of suitable extractants in optimal proportions was imperative for the consideration of enhancements in the extraction efficiency of isoflavones from soybean seeds. This conclusion aligns with findings from [PERSON] et al. [32], who also demonstrated that varying proportions of ethanol, ethylene, propanediol, and water significantly influenced the extraction of flavonoids from _Euonymous alatus_. [PERSON] et al. [34] investigated the extraction by propanediol, with a view to ascertaining its optimal extraction method and potential benefits. Compared to propylene glycol, propanediol, as a plant glycol, yields better results in extracting active compounds from plants and also improves epidermal penetration when incorporated into formulations like hydrogels. As a sustainable green solvent, it can extract active compounds from plants [35]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{6}{c}{**Type of Solvent**} & \multicolumn{6}{c}{**Isoflavone Content**} \\ \hline
**EtOH** & **Propanediol** & **H\({}_{2}\)O** & **Daidzin** & **Genistin** & **Malonyldaidzin** & **Malonylglycitin** & **Malonylgenistin** \\
**\%** & **\%** & **\%** & **mg/g** & **mg/g** & **mg/g** & **mg/g** & **mg/g** \\ \hline
100 & 0 & 0 & 0.00 \(\pm\) 0.00 & nd & nd & nd & nd \\
0 & 0 & 100 & 0.04 \(\pm\) 0.01 & 0.04 \(\pm\) 0.01 & 0.39 \(\pm\) 0.01 & 0.07 \(\pm\) 0.00 & 0.44 \(\pm\) 0.00 \\
0 & 100 & 0 & 0.10 \(\pm\) 0.00 & 0.10 \(\pm\) 0.00 & 0.18 \(\pm\) 0.01 & 0.04 \(\pm\) 0.00 & 0.23 \(\pm\) 0.00 \\
50
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 3 TSCH Energy Model
### TSCH Time Slots  
A TSCH schedule can contain different types of time slots, i.e., cell types, to indicate that a node should transmit, listen or put its radio to sleep. In IEEE 802.15.4e, seven different types of time slots can be identified:  
* TxDataRxAck: The mode sends a frame during this time slot and receives an ACK when the data have been received successfully.
* TxData: The mode sends a frame during this time slot, but does not expect an ACK (e.g., broadcast or multicast frames such as RPL Destination Oriented Directed Acyclic Graph Information Object (DIO) messages).
* RxDataTxAck: The mode listens and receives a frame in this time slot and replies with an ACK to indicate that it successfully received the frame.
* RxData: The mode listens and receives a frame in this time slot, but no ACK is sent (e.g., broadcast or multi-cast frames).
* RxIdle: The mode listens, but does not receive a frame in this time slot.
* Sleep: The mode does not transmit or receive during this time slot.
* TxDataRxNoAck: The mode sends a frame and expects an ACK, but no ACK is received. This could be caused by a collision of the data frame.  
The proposed model divides each time slot into different states. Figure 3 illustrates this and presents a general overview of the activity of a transmitter and receiver during a TxDataRxAck time slot and RxDataTxAck time slot, respectively. Some of the states seen in Figure 3 consist of two parts: one part where the CPU is active and one part where the CPU is sleeping. These two parts are considered as separate states in our model. The state of the radio in our model only changes at moments when the CPU state changes. This is a simplification as in the real world, the radio state changes slightly before or after this moment, typically while the CPU is active.  
Figure 3: States in TxDataRxAck (transmitter) and RxDataTxAck (receiver) time slots, together with the CPU and radio activity in the TxDataRxAck time slot.  
The remainder of this section explains the TxDataRxAck time slot in full detail. The other slots are modeled similarly, and we limit the discussion to highlighting the differences with the TxDataRxAck slot.
driver assistance, and automatic parking), and those procuring safety (i.e., security distance and collision avoidance) [5; 6].  
In a purely ad hoc vehicular environment, vehicles can communicate with each other through vehicle-to-vehicle (V2V) communication [7]. In fact, vehicles are equipped with wireless communication and processing capabilities that lead to the construction of a mobile network as they move along roads [8].
## 2 Study Area and Datasets
### Historical Land Reclamation  
XXNA on Dadeng Island was reclaimed from the sea to meet the needs of urban development. Remote sensing methods provide us with an efficient way for land change detection [30; 31]. To investigate the historical land reclamation on Dadeng Island, Landsat-8 satellite images and Google Earth historical images from 2013 to 2019 were utilized to assist our analysis. The results of the temporal and spatial evolution of land reclamation are shown in Figure 3, 16 km\({}^{2}\) of land, which accounts for 36% of all, has been reclaimed from the sea from 2013 to 2019. When the reclamation project is complete, reclaimed land will reach 29 km\({}^{2}\) (accounts for 65% of all). As shown in Figure 3a, the whole reclamation work is mainly in the southeast of Dadeng Island, the northwest and southwest also have wide reclaimed area. Additionally, according to previous studies [7; 8], the reclaimed land at XXNA is mainly composed of sand-mixed silt (Q4\({}^{\text{m}}\)), silt and silt mixed with sand (Q4\({}^{\text{m}}\)), silt clay and clay (Q3\({}^{\text{al}}\)), silty sand (Q3\({}^{\text{al}}\)), and residual clay (Q\({}^{\text{el}}\)) (The Q4\({}^{\text{m}}\), Q3\({}^{\text{al}}\), and Q\({}^{\text{el}}\) indicate the quaternary marine sediments, the quaternary alluvium sediments, and the quaternary eluvial sediment, respectively). When there is a heap load on the surface, it is prone to obvious subsidence or non-uniform settlement on those land types.
## 3 Numerical Validation
### Numerical Results
#### 3.3.2 Identification of the Damage Location  
Once the damaged edge is identified, the damage location can be further identified in the second stage. Figure 9 shows the difference in the BMC \(D^{r}_{j,1}\) for the first mode. It can be seen that there are many local peaks in \(D^{r}_{j,1}\) due to the effect of noise, but significant increases in \(D^{r}_{j,1}\) indicate damage. The average and standard deviation of \(D^{r}_{j,1}\) of edges b and d were calculated according to Equations (5) and (6). Then, the damage location can be identified by Equation (7). Table 5 lists the identified damage location. By comparing Tables 2 and 5, it can be found that these locations closely match the actual damage positions. The identified damaged range is a little less than the actual one. The reason is that the  
Figure 8: ACBMC and BMAC of model-B (**a**) Edge a, (**b**) Edge b, (**c**) Edge c, (**d**) Edge d.  
Figure 7: ACBMC and BMAC of model-A (**a**) Edge a, (**b**) Edge b, (**c**) Edge c, (**d**) Edge d.  
abnormal difference in the BMC would be found by Equation (7). \(D_{j,1}^{r}\) is the most obvious at the central range of damage location, which is easy to identify.  
Based on the above results, it can be found that the precise identified damage location is related to the number of measurement points on each edge of the glass panel. Usually, the damage location can be identified at different numbers of measurement points. However, there are differences in the identified location. As the number of measurement points increases, the identified location would be more accurate. In typical experiments, more than five measurement points are set on each edge. Therefore, further research is needed to determine the optimal number of measurement points.
## 4 Discussion  
As a new type of crop protection machinery, some details of the CPUAS application need to be clarified. The ESW, DDU, and DPR are the most important indicators for evaluating qualities of the spraying by CPUAS, and the operation parameters FH and FS, which can be controlled manually, affected ESW, DDU, and DPR. In this article, the results showed that the ESW of CE20 was not consistent and changed as the FS and FH changed, which were consistent with the existing findings [16; 17]. The FS had a highly significant impact (\(p\) = 0.0033 \(<\) 0.01) on the ESW, while the FH had no significant impact (\(p\) = 0.136 \(>\) 0.05) on it, and the ESW value was negatively correlated as the FS varies, which is consistent with the conclusions of [PERSON] et al. [17]. Through comparison from the treatments, it could be seen that the spraying DDU of CE20 was very good with _CVs_ among 23.3% to 34.4%. The two-way ANOVA results showed that the FH (\(p\) = 0.019 \(<\) 0.05) and the interaction between FS and FH (\(p\) = 0.032 \(<\) 0.05) both had significant impacts on the DDU, and the DDU could be improved by appropriately reducing FH and FS. This conclusion was slightly different with some existing conclusions. According to [PERSON] et al. [16], both the FS and FH had highly significant impacts on the DDU, and the interaction between FS and FH had significant impacts on the DDU. The reason may be the different type of CPUAS and the spraying volume, which needed further research. For the DPR, although results showed that it had no obvious correlation with the changes of the FS or FH, the slower the FS, the lower the FH, and the larger the spraying volume, the more droplets deposited in  
Figure 8: Wheat aphids control effect.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Disease** & **Period** & **Date** & **Control Effect** \\ \hline Powdery mildew & Heading stage & 7 d & 77.17 \(\pm\) 1.15\% \\ Head blight & Blooming stage & 14 d & 82.83 \(\pm\) 2.98\% \\ \hline \end{tabular}
\end{table}
Table 8: Control effects of powdery mildew and head blight.  
the lower layer of the crop canopy according to [PERSON] et al. [21] and [PERSON] et al. [22], so a larger spraying volume was suggested if permitted. Based on the test results, optimized parameters of T5 and T1 were selected for aerial spraying operations to control wheat aphids, powdery mildew, and head blight. The control effects could satisfy the actual application. The existing research [23] showed that better control of wheat diseases and insect pests was achieved when using a coarse droplet size and higher spray volume. The control effect of powdery mildew was lower than that of head blight, although the height and canopy density of the wheat during the Heading stage were both lower than those during the Blooming stage (see Table 1); the reason may be related to disease characteristics. The powdery mildew mainly occurs in the middle and lower layers of the wheat plants, while the head blight occurs mainly in the upper layers (ears), so the pesticide droplets would be more likely to contact the leaves infected with head blight.
## 6 Data Analysis
### Analysis of BYOD Security Policy Techniques Based on Privacy and Security Requirements
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref** & **Technology Employed** & **Performance Analysis** & **Advantage** & **Limitation** \\ \hline
[69] & Risk access scheme, & Experiment analyses and & Reduces costs while & It is weaker because \\
[69] & NGFW technique based & Case study (Tokyo & Increasing security. & package content should \\  & deep-packet inspection & University). & & be verified instead of only \\
[70] & Forensics investigation method. & Experiment analyses, real dataset (Android log). Simulation performed in three stages (computer, simulation, and experimental) & Works well to detect and analyse BYOD malware. & Only deal with log files without applications. \\
[71] & Forensic investigation model. & & Three stages (computer, simulation, and experimental) & tracked BYOD user’s traffic. & Forensic investigation \\  & Clustering algorithms & Simulation, public & traffic. & requires an end-to-end \\
[72] & Clustering algorithms & malware dataset, branchy model. & High precision and lower traffic. & long training time. \\  & & Experimental analysis, tested by comparing infected, unaffected android, public malware & The method examines the whole issue to see if BYOD is legal. & Less efficient. \\  & Risk access control model based on a risk estimation algorithm (fuzzy model) & Prototype implementation and Smart contracts dataset. & Making access decisions based on anomalies. & The method requires further development. \\
[75] & Andrologger tool. & Experimental analyses, Real dataset. & Automatically sending & The method only works \\  & OPPRIM-based risk policy model & simulations (AnyLogic), Mathematical Analysis. & Adaptability, cost reduction. & Simulating risk will result \\  & Simulations (Anylogic), & & adaptability, cost reduction. & in conservative \\  & Neural network-based algorithm to detect HTTP botnets. & Thebin dataset, PRISM model checker and Mathematical analysis (Correlation) & High accuracy. & Complexity. \\
[78] & IDS model-based ML algorithm. & Prototype implementation, NSL KDD dataset. & Able to detect DoS, probing and torrent traffic. & The dataset quantity is large. \\  & Behaviour-based abnormality detection model, Pattern Analysis (data mining). & Mathematical analysis. & Analyzing user behaviour to detect abnormal behaviour. & High false alarm rate. \\  & Simulation (NS2), attacks dataset (SYN attack, ICMP flood, Dos attack). & Self-adaptive network can defend against internal threats and reduce attack reaction time. & Should be extended and improved to detect sophisticated APTs. \\  & Malware identification scheme based supervised classification algorithms/ML. & Prototype implemented-android applications dataset. & High accuracy. & The decision limit may be overtrained. \\  & MUSES framework based fuzzy Logic, ML. & Prototype implementation. & Enhanced security and provided intrusion detection systems. & Difficult interpretation. \\ \hline \end{tabular}
\end{table} 
Table 9: Summary of the attack detection techniques.
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 3 Results and Discussion
### Orbit Determination Strategies and Analysis of Accuracy
#### 3.2.4 DORIS Validation  
The DORIS orbit check is a method that uses DORIS phase data to validate orbit accuracy through the rate of change in distance. At the time of validation, the elevation cut-off angle was set to 10\({}^{\circ}\), and the DORIS phase data at 10 s intervals were used. Since the time system of the DORIS data was International Atomic Time (TAI) and the RD orbits of HY-2D used GPS time, the first step was to unify the time system. The ionospheric delay effects were removed using a linear combination to obtain the Ionosphere-Free carrier-phase observations \(LC_{i}\), and the mean distance change rate \(d\Delta\varphi(t_{i})\) was calculated. The mean distance variation rate calculated from the orbits of HY-2D and corrected by various models was \(d\rho\). The difference between \(d\Delta\varphi(t_{i})\) and \(d\rho\) was calculated to obtain the residuals of DORIS validation [42].  
Before using DORIS data to validate, the RD orbit was processed to eliminate the influence of DORIS antenna phase center correction. In order to improve the accuracy of  
Figure 3: The amount of NP data and the RMS value of the SLR station.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DOY** & **Min (m)** & **Max (m)** & **Mean (m)** & **RMS (m)** \\ \hline
262 & \(-\)0.0968 & 0.0972 & 0.0068 & 0.0485 \\
263 & \(-\)0.1061 & 0.0980 & 0.0100 & 0.0495 \\
264 & \(-\)0.1091 & 0.0930 & 0.0098 & 0.0492 \\
265 & \(-\)0.1097 & 0.1010 & \(-\)0.0124 & 0.0501 \\
266 & \(-\)0.1059 & 0.0988 & 0.0091 & 0.0450 \\
267 & \(-\)0.1121 & 0.0989 & 0.0032 & 0.0505 \\
268 & \(-\)0.1076 & 0.0972 & 0.0005 & 0.0576 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Seven day SLR range validation residuals statistics.  
the validation result, the solid-earth tide, ocean tide and polar tide models were used for tidal correction. The zenith delay was calculated using the Saastamoinen model [43], and then the tropospheric delay was corrected by mapping the signal propagation path using the Niell Mapping Function (NMF) [44]. The results of the DORIS validation for the 7 days of orbits are shown in Table 6.  
The RMS values of the residuals for each station fluctuated between 0.005 and 0.009 m/s. The large errors for the YEMB station and MSPB station were due to a large number of data between 10\({}^{\circ}\) and 15\({}^{\circ}\) elevation angles; in order to retain more data, the elevation angle of the DORIS validation was set to 10\({}^{\circ}\), because more data at low-elevation angles led to poor inspection results at these stations. Summarizing the statistical results of the 7 days, the RMS residuals value was 0.0085 m/s.