esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hierarchical Trajectory Clustering Algorithm
### DBSCAN Algorithm with Automated Parameter Tuning
#### 3.2.2 Parameter Settings
Third, the knee region was defined as shown in Figure 6b, where the starting point of the knee region was set at the point where the change in data points rapidly increased, and the end point of the knee region was set where the change in data points rapidly decreased. Fourth, as shown in Figure 6c, the point of maximum curvature was identified and set as the Epsilon value. Generally, as shown in Figure 6d, this appears at the end of the knee region. Several researchers have assumed that the sharp changes in the k-distance graph correspond to changes in density levels. To reflect this, the point of maximum curvature was found and set as the Epsilon value to capture this significant change. Fifth, the MinPts with the highest Silhouette score were selected based on the determined Epsilon. In cases where the Silhouette scores were the same, the smaller MinPts value was chosen. Table 3 shows a comparison between the DBSCAN parameter settings examined in previous studies and the values derived by the method presented in this study using the Silhouette score, a cluster evaluation method. It can be seen that the Silhouette scores of the AF-DBSCAN method and the method presented in this study were the most similar and that the score of the method presented in this study was slightly higher than that of the AF-DBSCAN method. Figure 5: DBSCAN parameter setting process. The series of processes that apply the parameter setting method of DBSCAN proposed in this study are summarized in Algorithm 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Division** & **Silhouette Score** & **Epsilon** & **MinPts** & **No. of Clusters** \\ \hline
**Proposed** & 0.785840 & 0.0102880 & 41 & 6 \\ \hline
**VDBSCAN**[45] & 0.647035 & 0.0021 & 3 & 10 \\ \hline
**AutoEpsDBSCAN**[46] & 0.275513 & 0.00017659 & 4 & 5 \\ \hline
**AF-DBSCAN**[47] & 0.780567 & 0.0058385 & 4 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of DBSCAN parameter setting performance with existing studies. Figure 6: Epsilon value-setting process. (**a**) k-dist graph of similarity measure and dimensionality-reduced data. (**b**) Moving average k-dist graph and knee section of the moving k-dist graph. (**c**) The process of finding the point of maximum curvature in the knee section. (**d**) Point of maximum curvature in the knee section.
## 4 Discussion
### Availability and Limitation of LDDI in Monitoring LDD Processes  
In this study, a new land degradation and development index (LDDI) was proposed to quickly and accurately monitor the LDD processes of terrestrial ecosystems; it wassuccessful in revealing the LDD dynamics in China from 1982 to 2015. Our study proved that the LDDI is suitable for monitoring LDD processes at large spatiotemporal scales. The LDDI demonstrated certain advantages. First, the data used are readily available. Both NDVI and NPP data can be easily acquired from existing remote sensing products, and most are reliable and are generally provided without any cost. Second, compared with other improved NDVI indexes created by introducing transformation parameters, LDDI in the present study can be calculated based on MATLAB software or R by using some relatively concise programs and in a very short time. Third, by integrating NDVI and NPP, LDDI reflects vegetation greenness and productivity in the meantime, and thus accurately reveals the actual vegetation dynamics.  
LDDI, of course, has its limitations in monitoring LDD processes. For example, the index partly improves the saturation in high vegetation cover by considering greenness and productivity together, but it is unable to avoid this problem. In addition, this index is not suitable for certain regions, such as mountainous areas with high altitudes and complex terrain.
# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 4 Materials and Methods
### Computation and Analysis of Engagement Key Indicators
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Key Engagement Indicator** & **Pattern Type** & **Comments** \\ \hline \multirow{7}{*}{Persistence Persistence} & \multirow{7}{*}{Access patterns} & These patterns indicate the persistence of the student on a specific SQL exercise. They differentiate between students adopting a sequential approach, i.e., they address the exercises according to the given order, and those adopting an out-of-order approach, i.e., they reconsider the previously accessed exercises by going back-and-forth between the provided exercises. \\ \hline \multirow{7}{*}{Concentration Concentration} & \multirow{7}{*}{Successful patterns} & These patterns indicate the tendency of a student to focus on a specific exercise until a solution has been found. They differentiate between the students adopting a try-until-successful approach, which entails insisting on the same exercise until a solution has been found, and those adopting a move-to-the-next-exercise approach, which entails jumping to other exercises before solving the current one. \\ \hline \multirow{7}{*}{Confidence Confidence} & \multirow{7}{*}{Assistance requests patterns} & These patterns indicate the level of self-confidence of the students in solving the proposed exercises on their own. They differentiate between the students who usually ask for help during the computer lab session and the students who generally try to solve the main issues on their own. \\ \hline \multirow{7}{*}{Reflection Reflection} & \multirow{7}{*}{Errors patterns} & These patterns indicate the ability of the student to learn from her/his mistakes. They differentiate between students with a strong reflective attitude, who carefully analyze each error in order to minimize the error rate at the next submission, and students with a less reflective attitude, who adopt a trial-and-error approach, thus spending a very limited amount of time in understanding the reasons behind the errors before the next submission. \\ \hline \end{tabular}
\end{table} 
Table 2: Key engagement indicators and associated patterns. Algorithmic Optimization Based on KEI Information  
To efficiently extract the key engagement indicators, we enforce further mining constraints deeply into the candidate sequence generation process (see Algorithm 1). Specifically, similar to [48], we use regular expressions to discard ordered sequences of elements early when they do not meet any of the categories reported in Table 2. This prevents the generation and evaluation of an unnecessary large set of candidate sequences, many of which are potentially not relevant to students' engagement level analysis.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## 2 Preliminary
### Batched Network Coding
Simply speaking, recoding generates recoded packets of a batch by applying RLNC on the packets of the batch at the node. Let \(c_{i}\) and \(v_{i}\) be the coefficient vector and the payload of the \(i\)-th packet in the batch, respectively. A new recoded packet generated by RLNC has a coefficient vector \(\sum_{i}\beta_{i}c_{i}\) and payload \(\sum_{i}\beta_{i}v_{i}\), where \(\beta_{i}\)'s are randomly chosen from the field. The number of recoded packets depends on the recoding scheme [64; 70; 71; 75]. Before generating new recoded packets, the packets of a batch on hand can be considered as part of the recoded packets. This strategy is called systematic recoding [77; 72], which is independent of the decision on the number of recoded packets to be sent. A more refined version is that only the linearly independent packets of a batch are being forwarded. This way, we would not forward too many \"useless\" packets if the previous node generates excessive recoded packets. The destination node runs a BNC decoder, where the decoding method depends on the BNC, e.g., Gaussian elimination, belief propagation, and inactivation [96; 108]. The ranks of the batches at the destination node form sufficient statistics to the performance of the BNC [68; 77]. Besides the standard approach, there are also advanced encoding and decoding schemes such as improved BP decoders [109; 110], systematic encoding [111], partial data recovery [112], protograph-based construction [113], encoding for expanding windows with feedback [114], decoding with coupled batch size and degree distribution [115], decoding in cooperative broadcasting scenarios [116; 117], joint spatial-temporal encoding [118], and joint BP decoder with LDPC precoding [119; 120; 121].
## 4 Experimental Results
### Comparison Methods  
This paper selected the literature [34] that shared the same core concept of \"mapping first, then localization\" as the comparison method. For the target localization scene, in [34], a dense map was constructed offline based on node models. During the online localization, [34] first generated the around-view images based on sensor data. Subsequently, it employed the quantity of the matched local feature pairs as a measurement criterion to achieve node-level localization within the map and to calculate the pose of the vehicle based on the result of the node-level localization. In this paper, a comprehensive evaluation was conducted in various aspects, including the memory requirements for the maps, the nearest map node retrieval, the metric localization, timeliness, and robustness, when compared with [34], to thoroughly assess the performance of the proposed method.  
Simultaneously, to further evaluate the proposed method's performance in terms of localization accuracy, robustness, and the ability to overcome cumulative errors, this paper selected the mature panoramic SLAM method, PAN-SLAM [25], as a comparative benchmark. This feature-based PAN-SLAM method calculated the target's position based on panoramic image sequences, employing steps such as feature detection and matching, frame tracking, and loop closure detection. PAN-SLAM demonstrated outstanding localization performance across various scenarios.
## Data and Methods
### Analysis  
We examined the statistical relationships between the eight VIs and GPP using MODIS data and flux tower data for the 121 FLUXNET sites. First, we evaluated the relationship between the eight MODIS-derived VIs and tower GPP at the monthly scale for each of the 121 EC sites encompassing ten biomes. For each site, each VI was averaged within each month and was then correlated with monthly tower GPP. To evaluate the performance of each VI by biome, the R\({}^{2}\) values for the relationship between each VI and tower GPP were also averaged over all the sites within the biome. Second, the EC sites with six or more years of flux data were chosen to analyze the relationship between the VIs and tower GPP at the annual scale. We identified those sites having at least 6 years of flux data from the 121 FLUXNET sites. A total of 48 sites had at least 6 years of data. Third, we examined the relationships between the VIs and tower GPP at the site and biome levels. Fourth, we analyzed the relationship between VIs and two environmental scalars, f\({}_{\text{Topt}}\) and f\({}_{\text{VPD}}\), representing optimum temperature and vapor pressure deficit (VPD) stresses, respectively, to determine how VIs respond to environmental stresses and how a combination of VIs and environmental factors (f\({}_{\text{Topt}}\), f\({}_{\text{VPD}}\) and PAR) could explain the variance of GPP. The f\({}_{\text{VPD}}\) scalar used in this study is based on the water scalar in the MODIS GPP algorithm. For temperature stress, we used the temperature scalar (f\({}_{\text{Topt}}\)) in the vegetation photosynthesis model (VPM) that includes the minimum, maximum and optimal temperature for photosynthetic activity; we did not use the temperature scalar (f\({}_{\text{Tmin}}\)) in the MODIS GPP algorithm because of the lack of optimal temperature in the scalar. For each site, both f\({}_{\text{Topt}}\) and f\({}_{\text{VPD}}\) were calculated from flux tower meteorological measurements.
## 2 Theoretical Tooth Surface Modeling
### Meshing Contact Modeling  
The mathematic models of the gear and the pinion are established in the gear coordinate system \(\beta_{g}\) and the pinion coordinate system \(\beta_{p}\), respectively. In order to make the position relationship of spiral level gear pair consistent with the theoretical design, transforming the pinion coordinate system \(\beta_{p}\) is into the gear coordinate system \(\beta_{g}\). The gear rotates around \(\mathbf{p}_{2}\) axis and the pinion rotates around \(\mathbf{p}_{1}\) axis, when \(r_{2},\mathbf{n}_{2}\) rotates \(\mathbf{\varphi}_{2}\) around \(\mathbf{p}_{2}\), and \(\mathbf{r}_{1},\mathbf{n}_{1}\) rotates \(\mathbf{\varphi}_{1}\) around \(\mathbf{p}_{1}\), the conjugate contact between M point on the gear tooth surface and M point on the pinion tooth surface is established. The equation \(\mathbf{R}_{2}\) and the normal vector \(\mathbf{N}_{2}\) of tooth surface of the gear at the contact point can be represented as:  
\[\mathbf{R}_{2}=\Big{(}\mathbf{r}_{2}\mathbf{p}_{2}\Big{)}\mathbf{p}_{2}+sin\varphi_{2}\Big{(} \mathbf{p}_{2}\times\mathbf{r}_{2}\Big{)}+cos\varphi_{2}\Big{(}\mathbf{p}_{2}\times\mathbf{r} _{2}\Big{)}\times\mathbf{p}_{2} \tag{36}\]  
\[\mathbf{N}_{2}=\Big{(}\mathbf{n}_{2}\mathbf{\cdot}\mathbf{p}_{2}\Big{)}\mathbf{p}_{2}+sin\varphi_{ 2}\Big{(}\mathbf{p}_{2}\times\mathbf{n}_{2}\Big{)}+cos\varphi_{2}\Big{(}\mathbf{p}_{2} \times\mathbf{n}_{2}\Big{)}\times\mathbf{p}_{2} \tag{37}\]  
The equation \(\mathbf{R}_{1}\) and the normal vector \(\mathbf{N}_{1}\) of the gear at the contact point can be represented as:  
\[\mathbf{R}_{1}=\Big{(}\mathbf{r}_{1}\mathbf{p}_{1}\Big{)}\mathbf{p}_{1}+sin\varphi_{1}\Big{(} \mathbf{p}_{1}\times\mathbf{r}_{1}\Big{)}+cos\varphi_{1}\Big{(}\mathbf{p}_{1}\times\mathbf{r} _{1}\Big{)}\times\mathbf{p}_{1} \tag{38}\]  
\[\mathbf{N}_{1}=\Big{(}\mathbf{n}_{1}\mathbf{p}_{1}\Big{)}\mathbf{p}_{1}+sin\varphi_{1}\Big{(} \mathbf{p}_{1}\times\mathbf{n}_{1}\Big{)}+cos\varphi_{1}\Big{(}\mathbf{p}_{1}\times\mathbf{n} _{1}\Big{)}\times\mathbf{p}_{1} \tag{39}\]  
According to the meshing principle of gears, \(\mathbf{R}_{2},\ \mathbf{R}_{1},\ \mathbf{N}_{2}\), and \(\mathbf{N}_{1}\) should satisfy the equations as follows:  
\[\left\{\begin{array}{c}\mathbf{R}_{2}=\mathbf{R}_{1}-\overline{O_{1}O_{2}}\\ \mathbf{N}_{2}=\mathbf{N}_{1}\end{array}\right. \tag{40}\]  
There are six unknowns \(\theta_{1},\ \theta_{2},\ \Delta q_{1},\ \Delta q_{2},\ \varphi_{1}\), and \(\varphi_{2}\) in the above equations; however, there are only five independent equations. When the pinion rotation angle \(\varphi_{1}\) is given as input according to the  
Figure 4: Theoretical Modeling of the pinion concave and convex surfaces.  
step size, the equations can be solved. The pinion rotation angle at each moment corresponds to a meshing point, and all meshing points from entry to exit form a contact trace. The following Figure 5 shows the contact point position of the two teeth surfaces (after the gear rotating angle \(\varphi_{2}\) and the pinion rotating \(\varphi_{1}\)).
## 5 Other Topics
### Active Debris Removal and Life Extension  
While the bulk of the paper deals with the autonomous re-entry of a spacecraft and focuses on future objects launched into space, it is important to remember that there is a high chance that there will be the need to retrieve large objects already in space, particularly part of spent upper stages. To do that is necessary to perform an active debris removal (ADR) mission [36, 37, 53].  
Active debris removal is by far more complex than the simple deorbiting of a satellite because the chasing spacecraft has to reach the target, approach it, grab it and to bring it back to the edge of the atmosphere or on a disposal orbit. The analysis of all these problematics is out of the scope of this paper and the reader is referred to the corresponding appropriate literature. What is worth mentioning here is that the propulsion/deorbiting systems should have several analogies with the one described previously, but with more demanding delta-v and functional requirements as the chasing spacecraft has to move first toward the target, doing proximity operations and deorbit it afterwards, sometimes capturing and deorbiting more than one object in the same mission. The technical advantage of an ADR platform is that the total lifetime of this system can be much shorter than that of a typical satellite, simplifying some engineering aspects. This is particularly true in case the target is a medium/large platform with a long lifetime.  
ADR platforms could also highly benefit by strong synergies from future development in the so called in-orbit servicing market and in all the applications that require some sort of space tug. Another interesting related application is life extension, where the satellite lifetime is extended by the arrival of some kind of servicing platform.  
It is also possible that future regulations will force ADR for satellites that have failed to deorbit. If the servicing market will achieve great success, it is even possible that the original satellites will not be equipped with a deorbiting device and then simply captured and disposed after death, booking the service (maybe in advance) from a commercial provider.
# A Comprehensive Review of the Application Characteristics of Biodiesel Blends in Diesel Engines
## 3 Engine Performance
### Brake Thermal Efficiency  
BSFC is not an ideal choice when comparing different fuels with different heating values. Brake thermal efficiency (BTE) is a more suitable parameter under the same operating conditions. BTE refers to the efficiency at which diesel fuel combustion is converted into effective work output. That is, the ratio of power output to energy provided by fuel. BTE and BSEC are inversely related to each other. Therefore, the trends of the two are often opposite. The formula for BTE is as follows [51]:  
\[\text{BTE}=\frac{2\pi NT_{e}}{q\dot{m}_{f}}\times 100 \tag{3}\]  
Here, \(N\) is the operating speed, \(T_{e}\) is the brake torque, \(q\) is the calorific value, and \(\dot{m_{f}}\) is the fuel consumption flow rate.  
According to relevant studies, researchers found that the BTE of biodiesel and its blends with diesel are slightly lower or similar to conventional diesel [31; 33; 41; 52; 53; 54; 55]. [PERSON] et al. tested the BTE of diesel and diesel-biodiesel blends with B20, B30, B40, B100 under different loads and found that the biodiesel blends showed lower BTE during combustion [52]. The maximum BTE of diesel oil measured by [PERSON] et al. under a full load condition was 30.03%, while the BTE of diesel is the lowest [31]. However, some researchers reached the opposite conclusion [27; 32]. [PERSON] et al. found that fuel containing _citrullus colocynthis_ L. biodiesel has a higher BTE than diesel. They also found that B10 has the highest BTE at lower loads and B30 at higher loads [32]. The BTE of diesel engines is often affected by ignition delay, reaction temperature, and fuel characteristics [55]. The viscosity and density of biodiesel is high relatively, so the blends of biodiesel in the fuel will deteriorate the atomization, evaporation and air-fuel mixing effects of the fuel, resulting in uneven combustion [29; 54]. By definition, the lower heating value of biodiesel and generally higher BSFC do not improve BTE [29]. Fortunately, the problem of poor atomization will be greatly alleviated when the cylinder temperature is high, such as under high load [52]. In addition, a higher cetane number and higher oxygen content promotes combustion, while good lubricity reduces friction losses, and these improve BTE [34]. The comprehensive mechanism of positive and negative factors determines the BTE performance of biodiesel under various operating conditions. Figure 3 below shows the typical BTE trend of fish oil biodiesel blends.  
Figure 2: Variation of brake specific energy consumption (BSEC) with different engine loads for different test fuels [30].
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 4 Experiment  
We conducted comprehensive experiments to verify the effectiveness of the proposed algorithm. The data from Shenzhen Metro Line No. 7 in China constitute the training set and the validation set while the data from Beijing Yanfang Metro Line in China form the test set. The number of samples of Shenzhen Metro Line is 644,272 and the time series split method in Scikit-learn was employed to split the training set and the validation set. Different from the classical cross-validation techniques such as k-fold and shuffle split, time series split can prevent the data leakage problem. The core idea of this method is to return first \(k\) folds as the training set and the (\(k+1\))th fold as the validation set. One may refer to [PERSON] [53] for more information. The number of samples of Yanfang Metro Line is 71,638. The sample interval of VOBC is 200 ms. For example, the time length of the samples of Yanfang Metro Line is \(\frac{71,638\times 0.2}{60\times 60}\approx 4\) h. The proposed algorithm was trained using the Keras framework. The training process was accelerated by the CUDA acceleration technology. The standard LSTM network structure with one hidden layer was adopted unless otherwise stated. The mini-batch gradient descent method and the BPTT algorithm were used during the parameter update process. For the Adam optimization algorithm, the hyperparameters \(\beta_{1}\), \(\beta_{2}\) and \(\varepsilon\) were set to 0.9, 0.999 and \(10^{-8}\), respectively. Besides, the early stopping mechanism was used to limit the overfitting problem. In Sections 4.1 and 4.2, the experimental results of the proposed train dynamic model and the \(n\) steps speed prediction are provided, respectively.
## 4 Cryptographic Unit System
### Featurered Blocks
#### 4.3.2 Cipher Block  
This block of the encryption system uses the XOR function to generate an output message block where each bit depends on more than one bit from the input. This function is simple to implement and is not of high complexity. Its property where  
\[a\oplus b\oplus b=a \tag{3}\]  
is the key to decrypt the ciphered text after going through this block. Figure 7 explains in detail this operation.  
Figure 6: Encryption/Decryption System: (**a**) Encryption Mode; (**b**) Decryption Mode.
# Enhancing Impact Localization from Fluid-Pipe Coupled Vibration under Noisy Environment
## 2 Materials and Methods
### Water Transmission Main and Experimental Setup  
The experimental pipeline used is a part of a water transmission main in service in South Korea. The main does not have any branches between the monitoring section. The  
Figure 5: An impact signal with a burst noise (**a**) and its CWT (**b**).  
pipe is made of steel and is covered by coating layers both inside and outside the pipe, of which the diameter is 1200 mm and the thickness is 10 mm. The pipeline is buried underground, and its layout is shown in Figure 6a. There are six points accessible without digging and they were used for measuring the wave speed in the piping system, which is discussed further in Section 3.1.  
The propagated waves were measured by accelerometers (393B12, PCB) and recorded by a National Instruments data acquisition module (NI-9234). The sampling frequency was set to 51.2 kHz to observe the frequency response of the pipeline. The measured data were synchronized, using GPS modules. The schematic diagram for the experimental setup is given in Figure 6b. The impacts were induced using an impact hammer (086D20, PCB). The part of the water transmission main that was used for experimentation is delineated in Figure 6c. Three of the accessible places (3, 4, and 6) were used to mark the time of arrival (different for each sensor) to localize the impact source, of which the length was 564 m.