esa-sceva/satcom-chunk-collection huggingface dataset:
# Gait Planning Research for an Electrically Driven Large-Load-Ratio Six-Legged Robot
## 2 Configuration and Walking Gait of the Large-Load-Ratio Six-Legged Robot
### Typical Gait of the Large-Load-Ratio Six-Legged Robot  
The gait of the six-legged robot is a static gait. The number of legs in the support phase should be no less than three at any time. To facilitate the gait analysis, \(s_{u}\) and \(t_{r}\) are respectively regarded as the legs of the support phase and transfer phase. The relations are \(3\leq u\leq 6\) and \(u+r=6\). The gait is called \(u\) foot gait when the number of legs is \(u\) in the support phase. The walking speed, step pitch, total number of legs, gait period, and duty ratio are respectively set as \(v_{R}\), \(s\), \(n\), \(T\), and \(\beta_{R}\). \(T_{\rm s}\) and \(T_{\rm t}\) are defined as the time of the support phase and transfer phase. Generally speaking, the range of the duty ratio is from 0.5 to 1 for the six-legged robot. Then, the mathematical expression can be written about the walking speed of the robot. \[v_{\rm R}=\frac{s}{T_{\rm s}}=\frac{s}{\beta_{\rm R}T}=\frac{sn}{Tu} \tag{4}\]  
Based on Equation (4), it can be concluded that the three foot gait (or tripod gait) has a higher walking speed than the four foot gait (or quadrangular gait, tetrapod gait) and the five foot gait (or pentagon gait) when the walking speed and step pitch are constant. The parts of the gait parameters are listed in Table 1 for the large-load-ratio six-legged robot. The tripod gait is the most common gait and the fastest gait for six-legged robots. The pentagon gait is the most stable gait and slowest gait for six-legged robots. Although the stability and walking speed of the quadrangular gait are between that of the tripod gait and the pentagon gait, the analysis complexity of the quadrangular gait is generally more than that of the other gaits. One period of the quadrangular gait includes three steps: 1/3 gait, 2/3 gait, and 3/3 gait (it can also be regarded as the last 1/3 gait). The stability and articulated torques need to be considered when the large-load-ratio six-legged robot passes a slope. The number of legs in the support phase should be two in the front and rear. Then, 18 kinds of higher-stable swing sequences of the legs, as shown in Table 2, can be obtained when the robot traverses a slope with the quadrangular gait. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Duty Ratio \(\beta_{R}\)**} & \multirow{2}{*}{**Number (\(u\))**} & \multirow{2}{*}{**Gait**} & \multicolumn{2}{c}{**Support Time (\(T_{\rm s}\))**} & **Walking** \\  & & & & **between Legs of Support Phase** & **Speed \(v_{\rm R}\)** \\ \hline \(\beta_{R}=1/2\) & 3 & Tripod Gait & Equality & \(v_{R}=2s/T\) \\ \(\beta_{R}=2/3\) & 4 & Quadrangular Gait & Equality & \(v_{R}=3s/2T\) \\ \(\beta_{R}=5/6\) & 5 & Pentagon Gait & Equality & \(v_{R}=6s/5T\) \\ \(\beta_{R}=1\) & 6 & Stationary State & Equality & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parts of the gait parameters of the large-load-ratio six-legged robot.
# Extraction of Novel Bioactive Peptides from Fish Protein Hydrolysates by Enzymatic Reactions
## 5 Characterization of Novel Bioactive Peptides
### Purification and Identification of Peptides Using One-Dimensional Separation Systems
#### 5.1.3 Reverse-Phase High-Performance Liquid Chromatography  
The commonly used separation technique that is versatile and usually employed in chemical analysis is called high-performance liquid chromatography (HPLC), and the reverse-phase HPLC is a more favorable separation technique due to its simplicity, versatility, and wide scope, since it can handle diverse compounds in terms of polarity and molecular mass [112]. Studies on the improvement in this separation technique concern the optimization of amino acid resolution in RP-HPLC chromatograms. The RP-HPLC separation technique is usually employed to purify potent peptide fractions from fish hydrolyses. The study by [PERSON] et al. [113] characterized peptide fractions from tilapia (_Oreochromis niloticus_) byproduct hydrolyses and utilized RP-HPLC to purify the peptides with antimicrobial activities from tilapia hydrolyses. [PERSON] et al. [114] isolated peptide fractions from salmon (_Salmo salar_) hydrolyses using RP-HPLC and observed that the isolated peptide fractions differ in hydrophobicity, yet the fractionation that was employed improved the free radical scavenging properties of the salmon peptides. Additionally, the work by [PERSON] et al. [115] characterized peptides with ACE-I properties and utilized a rapid separation technique by applying a magnetic field, and they further identified the peptides using RP-HPLC. The authors further stated that the affinity interaction used in the initial purification of the peptides, followed by RP-HPLC, resulted in an effective purification of ACE-I peptides from food sources. Based on these studies, it can be noted that the use of RP-HPLC as a one-dimensional separation and purification method, or combined with other methods, could be used to purify other bioactive peptides from fish hydrolysates and other sources.
## 2 Mathematical Modeling
### Validation  
The CFD methodology must be verified against the available experimental data before using it for the analysis of the cyclones. In the present study, the results from the numerical simulations are compared to the experimental data of [PERSON] [51] at various axial locations, such as \(Z\) = 0.75D, 2.0D, and 2.5D. In the experiment, [PERSON] [51] considered the Stairmand high-efficiency cyclone with a diameter (\(D\)) equal to 0.29 m. A collection bin with a diameter of \(D\) and a height equal to 2.0D was installed at the bottom opening. An inlet velocity (\(U_{in}\)) equal to 16.1 m/s was used in the experiment that corresponds to the Reynolds number equal to 2.8 \(\times\) 10\({}^{5}\) (here, the Reynolds number is calculated as \(Re\) = \(\rho\cdot U_{in}\cdot D/\mu\), with \(\rho\) as the fluid density; \(U_{in}\) is the superficial inlet velocity at the inlet; \(D\) is the cyclone main body diameter; and \(\mu\) is the fluid viscosity). The two levels of the grid have been tested here to evaluate the effect of cell counts on the results. The first level mesh (referred to as the coarse mesh) comprised 0.885 million hexahedra, whereas the second level mesh (referred to as the fine mesh) consisted of nearly 2.187 million hexahedra. Figure 3 shows the comparison between the two levels of the grid as well as with the reference data. It becomes apparent that at the two levels of the mesh, there is no appreciable change in the mean and root mean square error values at all the axial stations. Hence, the results may be considered grid independent. Furthermore, a very good agreement is observed between the numerically simulated results and the experimental data. The mean profile from the LES fits well with the reference data at each axial location. However, there is a mild overprediction in the mean tangential velocity profile as well as the rmse values of axial velocity (in the core region). To validate the discrete phase simulation methodology, we make use of the experimental data by [PERSON] [36], and the results are presented in the last row of Figure 3. Two levels of the mesh consisting of 1.21 and 2.45 million hexahedra were considered, which indicates that the results are grid-independent and that the simulation results fit well with the experimental data. Since the cyclonic flows are highly anisotropic and turbulent, validation can be accepted with a fair level of accuracy. Hence, the solver settings, as well as the boundary condition applied, are appropriate and can be used in all future simulations with confidence.  
Figure 3: Comparison of simulation results against experimental data at \(Re\) = 2.8 \(\times\) 10\({}^{5}\) for two levels of the grid. From left to right: velocity profiles at axial locations \(Z/D\) = 0.75, 2.0, and 2.5 (\(R=D/2\) is the radius of the cyclone). From top to bottom: first row—mean axial velocity; second row—mean tangential velocity; third row—rmse axial velocity; and fourth row—rmse tangential velocity, respectively. At the bottom: grade efficiency curve.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 6 Performance Evaluation
### Signaling Cost  
A signaling cost calculation needs to be performed for 6L\({}_{\text{MNi}}\) between gateway nodes based on the number of messages sent between them in order to assess the potential for congestion on the network [34]. The messages within the group are not included in the calculation. Table 4 presents the results of the signaling overheads; it can be seen that SL_GAS has a significantly lower signaling overhead than the related work, Refs. [11; 16; 20].
# Performance Analysis of Multihop Underground Magnetic Induction Communication
###### Abstract
Such an assumption poses practical limitations on system design and analysis since any sudden change in the propagation characteristics renders the extraction of the desired signal at the receiver end inefficient. The change of the channel state has been discussed in earlier works such as [29; 30]. Most recent works such as [31; 32; 33] also analyse non-homogeneous channel conditions. To the best of our knowledge, there has been no study on multihop relay-based MI-WUSNs that  
Figure 1: Simple wireless underground communication systems based on (**a**) electromagnetic waves, (**b**) acoustic waves and (**c**) magnetic induction (MI). has investigated independent and non-identical channel states between hops and their impact on the system performance. The important features of this work are as follows:  
* A basic point-to-point underground communication model involving MI was extended to develop a multihop underground MI-based communication system. * A mathematical model was developed for evaluating the performance of the proposed MI communication system under the following assumptions. 1. Statistically independent and identical distribution (IID) of channel states between hops 2. Statistically independent and non-identical distribution (INID) of channel states between hops
* While earlier work in this domain has been based on an asymptotic analysis, we evaluated the performance by using different system parameters and validated its efficacy with extensive numerical analysis. Our evaluations showed that if a communication system is designed for fixed channel state information (CSI) under the IID assumption for the hop state, the corresponding overestimated model can cause performance overhead when the communication system is practically deployed. Accordingly, current solutions should be updated to account for this change in the channel state on all hops, by providing an adaptive and scalable solution instead of a theoretical model based on impractical assumptions. The rest of this paper is organised as follows. Section 2 presents the traditional MI communication system model for point-to-point communication and its extension to multihop MI communication. Section 3 details a mathematical model for analysing single-hop and multihop MI-WUSNs for IID and INID cases. Section 4 presents numerical results of the proposed model, and finally, Section 5 concludes this paper.
###### Abstract  
This paper focuses on downlink packet scheduling for streaming video in Long Term Evolution (LTE). As a hard handover is adopted in LTE and has the period of breaking connection, it may cause a low user-perceived video quality. Therefore, we propose a handover prediction mechanism and a pre-scheduling mechanism to dynamically adjust the data rates of transmissions for providing a high quality of service (QoS) for streaming video before new connection establishment. Advantages of our method in comparison to the exponential/proportional fair (EXP/PF) scheme are shown through simulation experiments.  
LTE (Long Term Evolution); handover; quality of service (QoS); scheduling; streaming video +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Application  
The model was applied to evaluate the feasibility of using the ETSC as water heating system in a food and beverage industry. Table 3 shows a typical hot water demand in the food and beverage industry [23]. It is appeared that 2419 kg of 60 \({}^{\circ}\)C hot water is required, which is equivalent to 45 m\({}^{2}\) of standard solar collecting area capacity. Therefore, 3 parallel sets of a 100-tube ETSC system operated 8 h daily would be required. This system can generate 2482 L of hot water per day at 60 \({}^{\circ}\)C. The thermal energy produced by the ETSC system is around 27 MWh/year, which equal to USD 3000 per year of electricity cost. The capital investment of ETSC is USD 16,000 [24]; therefore, the breakeven of the system is approximately 5.06 years. On the other hand, a reduction by 20% of the number of tubes in the same production rate can be gained by using the modified tubes. The capital investment of ETSC will be saved by USD 3000. The breakeven of this system is approximately 4.09 years. However, the monthly average solar intensity should be concerned because it would vary according to climate condition. The system should be designed to support the lower average solar intensity periods or co-generated with main heat generator in industry.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### Quality Assessment Methods for SAR-Based Snow Cover Products  
In the present SAR-based mainstream algorithms, there is no straightforward method to directly generate reliability indices to allow self-validation as in other SAR-based cryogenic studies such as the signal-to-ratio value of pixel-offsets for glacier velocity tracking [75]. Thus, several studies focused on quality indicators for assessing the reliability of a snow cover products. [PERSON] et al. [151] and [PERSON] et al. [33] utilized a confidence flag denoting the probability for a correct classification; [PERSON] et al. [134] introduced the probability of error (POE) to evaluate each pixel's classification reliability. Practically, validating SAR-based snow cover products using external, independent data is preferable. Overall, around half of the reviewed studies utilized snow cover products derived from spaceborne optical sensors as ground truth, with Landsat and MODIS accounting for most of the employed sensors (Figure 10). AVNIR-2 is used for validating PALSAR-based studies, as both sensors are mounted on the same satellite platform. Sentinel-2 is usually used for comparisons with Sentinel-1 results; Gaofen-1 (GF-1) is often utilized for validating studies in mainland China. Airborne and ground-based data account for a much smaller portion of validation approaches owing to the relatively high costs and  
Figure 9: Overview of targeted snow types and corresponding algorithms. (**a**) Targeted snow types and employed algorithms for (**b**) wet (**c**) dry (**d**) total snow cover extent (SCE).  
limited spatial coverage. The fact that \(\sim\)40% of the reviewed studies did not include any validation at all indicates that many of the published SAR-based snow detection techniques are in an early stage of their development.  
In addition to conventional confusion matrix-based accuracy evaluations, [PERSON] et al. (1999); [PERSON] et al. (2009) proposed a quantitative analysis approach based on the RMSE to check the improvement made by each refining algorithm; [PERSON] et al. (2016) compared the resultant SCE with an elevation map to check whether the distribution of snow is reasonable.
# TrendFlow: A Machine Learning Framework for Research Trend Analysis
## 5 Results
### Quantitative Evaluation Results
#### 5.1.2 Dimensionality Reduction Evaluation  
The dimensionality reduction evaluation results are shown in Table 2. The best score is made bold for each metric. DBSCAN still does not have any results due to its failure in detecting the correct number of clusters.  
In Table 2, we present the evaluation results of the same clustering algorithms used in the TrendFlow framework, but with an additional dimensionality reduction step using principal component analysis (PCA) applied beforehand. The three metrics, adjusted rand index (ARI), silhouette score, and Calinski-Harabasz index (CHI), are once again employed to assess the performance of the clustering algorithms.  
An interesting observation is that the application of PCA has a minimal impact on the adjusted rand index. In fact, for GMM, there is a slight decrease in the ARI score from 0.7202 to 0.7065. The other algorithms show little to no change in their ARI scores, indicating that the addition of PCA has a limited effect on the cluster assignments' similarity to ground truth labeling. On the other hand, the silhouette score has improved across all clustering algorithms when PCA is applied. GMM achieves the highest silhouette score of 0.09064, followed closely by K-means at 0.09061 and spectral clustering at 0.0900. Agglomerative clustering also sees an increase in its silhouette score to 0.0841.  
Similarly, the Calinski-Harabasz index demonstrates an improvement for all clustering algorithms after applying PCA, where the K-means still obtains the highest CHI score of 18.410.  
In summary, the addition of PCA as a dimensionality reduction technique has minimal impact on the adjusted rand index, with a slight decrease observed for GMM. However, the other two evaluation metrics, the silhouette score and Calinski-Harabasz index, show consistent improvements across all clustering algorithms when PCA is applied. This suggests that incorporating PCA may enhance the overall clustering quality in terms of intra-cluster similarity and inter-cluster dissimilarity.
## 5 Simulation Results and Discussion
### Simulation Environment  
Here, we provide the simulation environment to evaluate the performance of the proposed scheme. We consider an uplink network environment with seven cellular cells, each with the eNB placed at the center. The CUs and DPs are considered to be uniformly distributed in each cell with Rayleigh distributed small-scale fading. The network assumes log-normal shadowing effect with standard deviation of 8 dB and also assumes a path-loss exponent of 4. The main simulation parameters are listed in Table 1, and other simulation parameters were selected based on the 3 GPP LTE regulation [31]. We evaluate the performance using Monte Carlo simulation with a total of 10,000 iterations. We compare the performance of the proposed method in terms of the system capacity, spectrum efficiency, and transmit power.
# Risk Propagation Mechanism and Prediction Model for the Highway Merging Area
## 4 Analysis of the Accident Risk Propagation Mechanism in the Highway Merging Area
### Experimental Results
#### 4.3.2 The Risk Propagation of Rear-End Accidents in the Downstream Area  
The situation in the first impact area is the same as that of side collision accidents occurring in the merging area. The risk of this area continuously exists after the accident happens. However, the risk levels of the second and third impact areas are different. When traffic flow is low, risk propagation only involves the second impact area. Then, it gradually spreads to the third impact area as the traffic volume increases. In this situation, although the number of lanes is reduced, the lane proportion occupied by accidents is lower in this scenario. Therefore, with the increase in the traffic volume, the second and third impact areas are affected more quickly. The risk exposure moment of the second impact area is relatively small (around 200 s; see Figure 8). However, the risk beginning moment of the third impact area is more obvious from 4000 pcu/h to 4350 pcu/h. It decreases from 900 s to 400 s. Although the traffic flow increases afterwards, the risk beginning moment does not decrease by much (see Figure 8). As shown in Figure 9, in all scenarios, the risk propagation speed from the first to the second impact area is greater than that from the second to the third impact area. The difference is greatest at 4000 pcu/h when the third impact area begins to be affected, and the speed of propagation decreases with the increasing traffic flow.  
Figure 6: The risk exposure moment of the second- and third-level impact areas (side collision).  
Figure 7: The speed of risk propagation in the second- and third-level impact areas (side collision).
## 5 Conclusions  
A communication performance analysis method has been proposed, based on a numerical simulation of flow fields for widely used airborne laser communication systems. Using this method, the communication performance variation in laser links with different emission angles was analyzed under different motion speeds and flight altitudes. Under subsonic conditions, the system's communication performance steadily declines as flight speed increases. For every 0.1 Ma increase in flight speed, the increase in BER is around \(0.87\times 10^{-4}\) when the SNR is 10 dB. The system's communication performance improves with an increasing flight altitude, but it also becomes more susceptible to variations in flight speed. For every 5 km increase in altitude, the BER drops to half of the initial rate when the SNR reaches 10 dB. The wavefront distortion of laser beams emitted in different directions varies. In contrast to existing models of airborne laser communication systems, our proposed model is based on a specialized flow field simulation and incorporates a  
Figure 8: Variation curve of the average BER as the SNR varies under different flight altitudes.  
Figure 9: Comparison of the communication performance of different wavelength communication beams under the same moving conditions.  
full aerodynamic flow field structure, including boundary layer effects and other complex flows. To improve accuracy, the model can modify the simulation parameters based on various motion states. The proposed model serves as a theoretical foundation for the testing and implementation of laser communication links on aerial platforms.  
Conceptualization, X.Z. and S.G.; methodology, X.Z.; software, X.Z.; validation, Z.L. and H.W.; formal analysis, X.Z.; investigation, L.M. and K.D.; resources, X.Z.; data curation, Q.J.; writing--original draft preparation, X.Z.; writing--review and editing, S.G.; visualization, S.G.; supervision, Z.L. and S.G.; project administration, S.G.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (U2141231, 62375239) and the Zhejiang Provincial Natural Science Foundation (LDT23F04015F05).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.