esa-sceva/satcom-chunk-collection huggingface dataset:
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 4 Performance Analysis
### Packet Loss Rate Test  
Figure 8(a) shows the network topology of the packet loss rate test, and Table 3 shows the network settings of the packet loss rate test. We test the packet loss rate of the three protocols under routing buffer sizes of 1000 packets and 100,000 packets and count the number of packet losses at R0. The packet loss rate can be expressed as  
\[\mathrm{R}_{\mathrm{drop}}=\frac{\mathrm{P}_{\mathrm{drop}}}{\mathrm{P}_{ \mathrm{all}}} \tag{19}\]  
Figure 10: Interprotocol fairness test results.  
where Rdrop represents the rate of packet loss, Pdrop denotes the number of packets dropped by the router, Pall indicates the total number of packets transmitted through the router, and Table 4 presents the results of packet loss rate testing.  
Based on the test results, neither the BRF nor the CUBIC protocols significantly contribute to active packet loss of the router, whereas the BBR protocol may result in a considerable number of data packet losses and retransmissions when the link buffer is limited [33].  
BRF uses in-network control mode to uniformly allocate the bandwidth of each flow at the bottleneck route. The sum of the bandwidth of each flow is equal to the bandwidth of the network card, so there is no queuing or packet loss. The CUBIC protocol uses a cubic polynomial growth curve, and the Wmax value stores the window value of the last packet loss. The window of the sender grows extremely slowly when approaching Wmax, so the CUBIC protocol causes almost no packet loss.
## 3 Post-Processing Methodologies
### Time-Domain Analysis  
In flash thermography, the excitation conditions typically introduce a strong non-uniform background, which is in many cases detrimental for the defect detectability using post-processing techniques based on the magnitude of temperature. This excitation non-uniformity is clearly seen in the thermal response at an arbitrary time sample (see Figure 3 Left Column). In order to reduce the effect of non-uniform excitation, a standardization step is applied to the raw data. The standardized thermal response \(T_{\text{{stand}}}(i,j,t)\) was calculated in a pixel-wise manner as follows [28]:  
\[T_{\text{{stand}}}(i,j,t)=\frac{T(i,j,t)-\overline{T}(i,j)}{\sigma_{T(i,j)}} \tag{1}\]  
where \(T(i,j,t)\) is the thermal response of pixel \(p^{(i,j)}\), and \(\overline{T}(i,j)\) and \(\sigma_{T(i,j)}\) its respective mean value and standard deviation over time. The effectiveness of this prior standardization is demonstrated in Figure 3 Right Column.  
Figure 2: Graphical representation of the thermal response for a sound and defected pixel.
# Modeling the Directional Clumping Index of Crop and Forest
## 4 Sensitivity Analysis
### Factors Influencing Crop Clumping Index Variation  
#### 4.1.1 Leaf Area Index  
Figure 11 shows that \(\Omega(\theta)\) decreases with increasing LAI in all directions, since heterogeneity is more apparent for larger intra-ridge LAI. Before the zenith angle reaches the range angle, \(\Omega(\theta)\) varies greatly with crop canopy LAI. The view zenith angle of satellite observations is mainly smaller than the range angle, e.g., MODIS view zenith angle is between \(0^{\circ}\) and \(65^{\circ}\). Therefore, \(\Omega(\theta)\) is essential to LAI estimation while using satellite data.  
Figure 10: Comparison between Hemispherical average Clumping index (\(\widetilde{\Omega}\)) and distribution of \(\Omega(\theta)\) when \(5^{\circ}\leq\theta\leq 80^{\circ}\) using box plot. The three boxplots represent black spruce forest (refer to Table 2, Column 1), Larch forest (refer to Table 2, Column 2), and crop (refer to Figure 2 with leaf area index (LAI) = 3), respectively. In each box, the upper/lower black dots represent the max/min value of \(\Omega(\theta)\), respectively. The top/middle/bottom lines of the box correspond to \(20^{\circ}\), \(40^{\circ}\), \(60^{\circ}\) of \(\theta\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Canopy Type** & **Parameters** & \(\widetilde{\Omega}\) & **Relative Variation Magnitude \(A_{v}\) (\%)** \\ \hline Crop & Refer to Figure 2, LAI = 3 & 0.926 & 32.4 \\ Black spruce forest & Refer to Table 2, Column 1 & 0.453 & 102.3 \\ Larch forest & Refer to Table 2, Column 2 & 0.308 & 48.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Angular clumping index variation statistics.
###### Abstract  
An improved method based on the Hybrid Finite Element-Statistical Energy Analysis (FE-SEA) method and quasi-steady state theory is proposed to predict the response of spacecraft structure during the process of pyrotechnics separation. Firstly, the amplitude-frequency value of shock load is obtained by using time-frequency conversion technology. Then, according to the frequency response characteristics of each part of the spacecraft structure, a more accurate hybrid FE-SEA model is established. The piecewise loading method is used to predict the response of the hybrid model. Finally, the time domain response results are reconstructed, and the shock response spectrum (_SRS_) is calculated. Based on the test system of simulating pyroshock, the shock experiment of spacecraft structure is conducted. The high frequency and high velocity character of pyroshock could be effectively simulated, and an accurate shock force function could be obtained through the experiment. This indicates that the numerical results are in line with the ones of the experiment. The _SRS_ obtained from experiments and calculations have similar trends and amplitudes. This conclusion verifies the rationality and sufficient accuracy of the novel method in this paper. The novel method presented in this paper greatly improves the computational efficiency. At the same time, it provides theoretical guidance for shock response prediction of spacecraft structure by steady-state methods.  
shock response prediction; time-frequency domain transformation; hybrid FE-SEA method; shock response spectrum
# Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot
## 3 Manipulator Robot Model  
The following general notation is considered to obtain the dynamic model of a 2-DoF planar manipulator robot with \(i=1,2,\ldots,n\). joints and q generalized coordinates [3; 38]:  
\[\mathbf{\tau} = \mathbf{M}(q)\ddot{\mathbf{q}}\ +\ \mathbf{C}\Big{(}q,\dot{q}\Big{)}\ +\ \mathbf{G}(q)+\mathbf{F}\Big{(}\dot{q}\Big{)} \tag{5}\]  
where:  
\(\mathbf{\tau}\): Generalized forces vector (\(n\times 1\) dimension).  
\(\mathbf{M}\): Inertia matrix (\(n\times n\) dimension).  
\(\mathbf{C}\): Centrifuge and Coriolis force vectors (\(n\times 1\) dimension).  
\(\mathbf{G}\): Gravitational force vector (\(n\times 1\) dimension).  
\(\mathbf{F}\): Friction force vector (\(n\times 1\) dimension).  
\(\ddot{\mathbf{q}}\): Joint acceleration vector (\(n\times 1\) dimension).  
\(q\): Components of the joint position vector.  
\(\ddot{q}\): Components of the joint speed vector.  
Figure 1 shows the scheme of the 2-DoF planar manipulator robot used, which includes the location of the centroids (mass centers) of each link. According to Equation (1), which is based on the Lagrange-Euler formulation, the dynamic model of the manipulator may be expressed through Equations (6) to (17) [3; 38]:  
\[\mathbf{M}=\left[\begin{array}{cc}M_{11}&M_{12}\\ M_{21}&M_{22}\end{array}\right] \tag{6}\]  
\[M_{11}=m_{1}\cdot l_{c1}^{2}+m_{2}\cdot\left[l_{1}^{2}+l_{c2}^{2}+2\cdot l_{1 }\cdot l_{c2}\cdot\cos(\theta_{2})\right]+I_{1}+I_{2} \tag{7}\]  
\[M_{12}=m_{2}\cdot l_{1}\cdot l_{c2}\cdot\cos(\theta_{2})+m_{2}\cdot l_{c2}^{2 }+I_{2} \tag{8}\]  
\[M_{21}=m_{2}\cdot l_{1}\cdot l_{c2}\cdot\cos(\theta_{2})+m_{2}\cdot l_{c2}^{2 }+I_{2} \tag{9}\]  
\[M_{22}=m_{2}\cdot l_{c2}^{2}+I_{2} \tag{10}\]  
\[\mathbf{C}=\left[\begin{array}{c}C_{11}\\ C_{21}\end{array}\right] \tag{11}\]  
\[C_{11}=-m_{2}\cdot l_{1}\cdot l_{c2}\cdot\sin(\theta_{2})\cdot\dot{\theta}_{ 2}^{2}-2\cdot m_{2}\cdot l_{1}\cdot l_{c2}\cdot\sin(\theta_{2})\cdot\dot{ \theta}_{1}\cdot\dot{\theta}_{2} \tag{12}\]  
\[C_{21}=m_{2}\cdot l_{1}\cdot l_{c2}\cdot\sin(\theta_{2})\cdot\dot{\theta}_{1}^ {2} \tag{13}\]  
\[\mathbf{G}=\left[\begin{array}{c}G_{11}\\ G_{21}\end{array}\right] \tag{14}\]  
\[G_{11}=\left\{m_{1}\cdot l_{c1}\cdot\cos(\theta_{1})+m_{2}\cdot\left[l_{1} \cdot\cos(\theta_{1})+l_{c2}\cdot\cos(\theta_{1}+\theta_{2})\right]\right\} \cdot g \tag{15}\]  
\[G_{21}=\left[m_{2}\cdot l_{c2}\cdot\cos(\theta_{1}+\theta_{2})\right]\cdot g \tag{16}\]  
\[\mathbf{F}=\left[\begin{array}{c}F_{11}\\ F_{21}\end{array}\right] \tag{17}\]  
where:Table 1 shows the parameter values considered for each link of the manipulator robot [1,38].
## 4 Discussion
### Which Smoothing Method is the Most Accurate  
The local filtering methods SG and LO have very similar definitions, although LO has a more advanced weighting system than SG. Despite this, the results from LO were not clearly better than those from SG. The explanation may be that LO assigns very high weight to the middle data point in the smoothing window and gives low weights to the other points. This leads to a more pronounced 'local' characteristic for LO, which may be negative in the presence of strong noise. On the other hand, this 'local' characteristic may be an advantage for detecting detailed changes during a growing season. In many cases the local methods were superior for reconstructing ground NDVI. However, the differences in RMSE were small and may be due to random chance.  
Spline smoothing has the advantage of having only one parameter, \(p\), to change the degree of smoothness of a curve. However, the performance of SP was not better than the other methods in any of the three experiments. This could partly be explained by a limited number of tested \(p\) values. \(p\) can be set to any positive natural number, but in this paper \(p\) was only tested for six selected values, which means that the full capability of SP was not utilized. SPCV generated more robust results than SP, but it had poorer performance in generating results in optimized parameters. When applying SPCV to images it optimizes \(p\) for every pixel. However, it can be questioned whether is it reasonable to allow  
Figure 10: Maps of start of the season (SOS), end of the season (EOS), length of the season (LOS) and small seasonal integral (SI) for the Ammar region, generated using the optimized settings of DL.  
different degrees of smoothing for different pixels, irrespective of their class membership. The third experiment showed that SPCV performed better than SG, LO and SP but worse than AG and DL.  
Contrary to the other methods, AG and DL smooth data in a static window (half/full seasons), which is the reason for their robustness. In addition, AG and DL tend to eliminate noise at peaks and valleys in a time-series. Although this elimination limits the ability of AG and DL in detecting detailed variations during periods near the season peak, it helps to automatically and robustly extract temporal phenology variables, e.g., SOS, EOS, and LOS (third experiment). The results of our tests point to the superiority of these function fitting methods for extracting phenological parameters over large areas where calibration of the smoothing parameters is not possible.
## 3 Experimental Results and Discussion
### Experimental Result
#### 3.2.4 The Relation between the Debris Axial Distribution and the Induced Voltage  
In the process of the continuous wear of friction pairs, more than one wear particle will be produced. When the spacing between the wear debris is too short, the induced voltage signals generated by the debris cutting the magnetic field will overlap, making it difficult to identify the size of each debris particle. To detect the influence of the spacing on the output voltage, we let two wear debris particles pass through the sensor at different spacing under the conditions that the speed of the stepping motor was set to 2.12 m/s, the z-axis height was set to 1 mm and the exciting current was set to 0.5 A.  
The induced voltages of adjacent debris at different intervals are shown in Figure 14. The amplified background noise in the experiment was around 30 mV. When the spacing was less than 25 mm, the output signal waves overlapped, but when the spacing was 30 mm, the output signal waves were completely separate. As the spacing increases, the peak-to-peak value decreased.  
Figure 14: _Cont._  
Figure 13: Z-axis magnetic field distribution of the inductive sensor.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 5 Wire-Grid Sparse Antennas
### Wire Grid: Application and Example  
#### 5.3.1 Real Commercial Parabolic Antenna  
In order to demonstrate the applicability of OCGA, a dual-polarized parabolic commercial antenna [301] (Figure 5) is used. This antenna is designed for directional links with MIMO mode at the frequency band of 5 GHz. The electrical and mechanical parameters of
## 2 Definition of the Geometry
Without loss of generality, a three-dimensional shell is defined if the following limits are specified  
\[\alpha_{1}\in\left[\alpha_{1}^{0},\alpha_{1}^{1}\right],\quad\alpha_{2}\in \left[\alpha_{2}^{0},\alpha_{2}^{1}\right],\quad\zeta\in\left[-h/2,h/2\right] \tag{4}\]  
It should be noticed that the total thickness of the shell \(h\) is defined as the summation of the thickness of each layer (or lamina) \(h_{k}\), if a laminated composite material is taken into account. For a lamination scheme that consists of \(l\) layers as the one shown in Figure 1, the overall thickness is given by  
\[h=\sum_{k=1}^{l}h_{k} \tag{5}\]  
It is evident that the symbol \(k\) is used to define all the geometric and mechanical properties related to the \(k\)-th layer. The differential geometry provides also the definition of the well-known Lame parameters \(A_{1}(\alpha_{1},\alpha_{2})\) and \(A_{2}(\alpha_{1},\alpha_{2})\), which can be evaluated once the position vector \(\mathbf{r}(\alpha_{1},\alpha_{2})\) is specified as follows  
\[A_{1}=\sqrt{\mathbf{r}_{,1}\cdot\mathbf{r}_{,1}},\quad A_{2}=\sqrt{\mathbf{r }_{2}\cdot\mathbf{r}_{,2}} \tag{6}\]  
where the symbol \"\(\cdot\)\" is introduced to specify the scalar product. Finally, the main radii of curvature of the reference surface \(R_{1}(\alpha_{1},\alpha_{2})\) and \(R_{2}(\alpha_{1},\alpha_{2})\) can be computed using the definition shown below  
\[R_{1}=-\frac{\mathbf{r}_{,1}\cdot\mathbf{r}_{,1}}{\mathbf{r}_{,11}\cdot \mathbf{n}},\quad R_{2}=-\frac{\mathbf{r}_{,2}\cdot\mathbf{r}_{,2}}{\mathbf{r }_{,22}\cdot\mathbf{n}} \tag{7}\]  
in which the notation \(\mathbf{r}_{,ii}=\partial^{2}\mathbf{r}/\partial\alpha_{i}^{2}\), for \(i=1,2\), is employed. Expressions (7) are valid only if the curvilinear coordinates are orthogonal and principal. The three-dimensional effect of a shell structure is taken into account by the parameters \(H_{1}(\alpha_{1},\alpha_{2},\zeta)\) and \(H_{2}(\alpha_{1},\alpha_{2},\zeta)\), which are defined as follows  
\[H_{1}=1+\frac{\zeta}{R_{1}},\quad H_{2}=1+\frac{\zeta}{R_{2}} \tag{8}\]
## 2 Related Work  
### Machine Learning with Student Success Prediction  
Predicting student academic performance has been a prominent research area in educational data mining, with numerous machine learning approaches applied to detect students with high risk and predict academic outcomes. Conventional machine learning models, for instance, Random Forest, Decision Tree (DT), K-Nearest Neighbor (KNN), and Gradient Boosting (GB), have been widely used to process structured student data, such as demographic information, course grades, and engagement metrics.  
[PERSON] et al. [9] utilized data mining approaches such as dividing into clusters to forecast students' final grades and used decision trees to monitor students' learning progress, achieving a maximum accuracy of 83.63%. [PERSON] [10] employed classical machine learning algorithms to predict students' performance in Turkish language courses, achieving classification accuracy between 70% and 75%. [PERSON] et al. [11] introduced a student grade forecasting system driven by AutoML, combining different machine learning algorithms with the SMOTE technique and feature selection strategies. They found that Random Forest achieved an F1 score of 99.5%. [PERSON] et al. [12] applied a range of machine learning approaches like RF, NB, DT, and MLP to two datasets for student classification. [PERSON] et al. [13] developed a machine learning platform for predicting learners' academic achievement, utilizing classifiers like Support Vector Machines, AdaBoost, Logistic Regression, and Random Forest. They found that SVM and Random Forest achieved accuracies of 92%, while Logistic Regression and AdaBoost achieved accuracies of 91%. [PERSON] et al. [14], utilizing the OULAD dataset, reported an accuracy rate of 90% using the random forest algorithm.  
These models are particularly effective when working with numeric data, where the relationships between features can be directly modeled. However, while these approaches are valuable, they often struggle to capture more complex temporal or sequential patterns in behavioral data, such as those generated from online learning environments. Moreover, these approaches typically depend on feature extraction, which can limit their ability to generalize to new datasets or uncover hidden relationships within the data.
In this work, we present the results obtained by analyzing displacement time series from both CSK and S1 for investigating the ground stability of hilly villages located in the Southern Italian Apennines (Basilicata region). In the area of interest, landslides are endemic. Many events occurred in the recent past, causing damage to houses, commercial buildings, and infrastructures, and several have been investigated through various remote sensing techniques [14; 15; 16; 17; 18]. We focus here on two test sites in the province of Matera, one on the outskirts of the town of Montescaglioso, where a landslide occurred in 2013, and one near Pomarico, affected by an event in 2019. SAR datasets acquired by CSK and S1 from both ascending and descending orbits were processed by using the SPINUA MTInSAR algorithm [19], in order to exploit the potential of these two satellite missions to investigate ground displacements related to the slope instabilities in pre- and post-failure stages. Monitoring of the displacement rates is the main tool to detect the type and causes of a slope failure [20] and to plan further restoration measures, if possible, of the involved areas. Mean velocity maps and displacement time series were analyzed, looking, in particular, for non-linear trends that are possibly related to relevant ground instabilities and, due to the high spatial resolution, useful in terms of early warning. This analysis was also supported by automated procedures recently developed for identifying targets with peculiar, nonlinear motions. Specifically, this article illustrates an example of slope pre-failure monitoring on the Pomarico landslide, an example of slope post-failure monitoring on the Montescaglioso landslide, and a few examples of structures in both locations (such as buildings and roads) affected by instability related to different causes (soil swelling, for example).
# An ICN-Based IPFS High-Availability Architecture
## 3 Design Overview
### IBIHA Overview
#### 3.1.1 Resolution Mechanism  
As shown in Figure 3, since the network is composed of different functional nodes, IBIHA uses two methods of resolution. The first is the resolution method of DHT and Bitswap, which is supported by both IPFS nodes and augmented nodes, and this mechanism does not focus on the underlying network. Secondly, the other resolution mechanism is ENRS, which can only be supported by the enhanced nodes. In ICN, each routing node is partitioned by latency, and each region has ENRS to manage the registration and cancellation information of all nodes in that latency domain. These ENRS are guaranteeing the resolution delay of the nodes in the resolution domain, and usually guarantee that the resolution delay is at a very small delay constraint.  
When an IPFS node receives a request signaling, the IPFS node that has the data sends the requested block of data to the requester, regardless of whether the requester goes through DHT or Bitswap. If not, Bitswap chooses whether to forward the data based on the TTL, while the DHT mechanism sends to the requester some nodes in its own K-bucket that are logically closest to the requested data. However, if the enhanced node does not own the data, it first checks whether the requested data exist in the time resolution domain to which it belongs, and if it does, it returns the information of the node storing the data or directly pulls the data back to complete the delivery. Otherwise, it routes the data through the application layer resolution mechanism of IPFS. the resolution in IBIHA is done by using the ENRS of ICN to enhance the IPFS node for the neighboring nodes in the time resolution domain contact, which improves the resolution success rate of the node. Meanwhile, the DHT and Bitswap mechanisms can help to realize the cross-zone search of ENRS, and in data acquisition, although ENRS may record the address information of multiple data sources, it is able to design the copy selection policy in the protocol to select the appropriate data source [39].
## 6 Applications
### Dam-Breaking Testing Case  
A 2D dam-breaking problem is simulated for assessment of the efficiency of the proposed boundary treatments. The spacing of fluid particles is set to be 0.01 m, the overall height of the water column is 0.6 m, and its width is 1.2 m. The size of the solid container is 3.22 m long. The initial state of the water tank is shown in Figure 9. The pressure on a point marked red which has a 0.16 m distance above the bottom of the tank on the right-hand-side wall is recorded.  
Figure 8: Sketch of boundary treatment 4: uniformly distributed boundary particles.  
Figure 7: Sketch of boundary treatment 2: ghost particles.  
Figure 9: Initial state of water column with red marked point showing the pressure recording place.  
The results obtained from the simulation are compared with experimental values obtained in [25]. Fluid motions at 0.1 s and 0.2 s obtained from five different boundary treatments are provided and compared in Figure 10.  
It is evident in Figure 10 that boundary treatment 4 (uniformly distributed boundary particles only) fails to prevent the penetration of inner particles. Furthermore, a gap between the water front and the solid boundary is observed in the results obtained with boundary treatments 2 and 3 using ghost particles.  
Figure 11 shows the subsequent results of water motions at 0.3 s. It can be seen that the gap between the fluid front and the solid is still obvious at 0.3 s with all the boundary treatments using ghost particles.  
The subsequent processes of water motions at 0.18 s and 1.4 s are provided in Figures 12 and 13. It can be seen that fluid particle penetration occurs in boundary treatment 2, which indicates that the application of ghost particles only is not sufficient in this  
Figure 11: Dam-breaking water motion at 0.3 s.  
Figure 10: Dam-breaking water motion at 0.1 s (**left**) and 0.2 s (**right**) with boundary treatment 1, 2, 3, 4, and 5 from top to bottom.  
case. The results obtained from all the boundary treatments are very similar. Time histories of the pressure values at the test point, as marked in Figure 9, are compared in Figure 14, for different boundary treatments as well as the experimental results in [25].  
Figure 12: Dam-breaking water motion at 0.8 s.  
Figure 13: Dam-breaking water motion at 1.4 s.  
As shown in Figure 14, the value and the happening time of the first pressure peak recorded in the experiment can be well reflected in numerical simulations. The values obtained from numerical methods are lower than the experimental data. This could be contributed to the three-dimensional effect in the experiment. However, most of the numerical methods cannot predict the second peak correctly. This might be caused by the ignorance of air bubbles entrapped inside the fluid after its impacting with the wall [15].