esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Density and Microstructure
The volume percentage of CSA cement was just 1.4 wt.%. The HGMSs were loosely stacked, and the void was scarcely filled with ettringite, but the hydration of the cement phase occurred obviously on the shells of the HGMSs, which helped to connect the HGMSs and created a durable network. However, in contrast to sample 50C, the long crystals in the network were missing. Figure 6b shows very few crystals with a pronounced needle-like structure (see, e.g., the top left corner). Apart from that, the number of broken shells seems to be slightly increasing compared with Figures 4 and 5. This could be attributed to an increased degree of shell breakage during preparation or during three-point bending tests, as shown by the SEM pictures of the fractured surfaces. The three-point bending tests were carried out in a non-dried state according to DIN EN 196-1 [20]. All specimens were covered with a wet towel before testing to prevent them from drying. The results of the three-point bending tests are shown in Figure 7. Sample 100C showed the highest bending strength of 3.74 MPa. With an increased amount of HGMSs, the maximum bending strength decreased by more than 80% to 0.56 MPa for sample 70C, and even to 0.34 MPa for sample 30C (Table 4). The rapid decrease in mechanical strength with the first addition of HGMSs indicates that the primary strength-providing component was the hydrated cement phase. As soon as the volume percentage of HGMSs rose to more than 60% (cf. Table 3, samples 70C to 30C), the flexural strength dropped to about one-tenth of the value of sample 100C. Beyond that point (70C), the volume fraction of HGMSs increased only slightly. Accordingly, the flexural strength only declined to a small extent. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & **100C** & **90C** & **80C** & **70C** & **60C** & **50C** & **40C** & **30C** \\ \hline Flexural strength (MPa) & 3.74 \(\pm\) 0.36 & 2.30 \(\pm\) 0.22 & 1.54 \(\pm\) 0.29 & 0.56 \(\pm\) 0.03 & 0.50 \(\pm\) 0.09 & 0.57 \(\pm\) 0.07 & 0.42 \(\pm\) 0.06 & 0.34 \(\pm\) 0.03 \\ \hline Thermal conductivity (W-m\({}^{-1}\)K\({}^{-7}\)) & 0.833 \(\pm\) 0.024 & 0.186 \(\pm\) 0.003 & 0.073 \(\pm\) 0.002 & 0.072 \(\pm\) 0.007 & 0.055 \(\pm\) 0.001 & 0.048 \(\pm\) 0.001 & 0.046 \(\pm\) 0.001 & 0.043 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Flexural strength and thermal conductivity for all mixtures. Figure 6: SEM images of a fractured surface after three-point bending tests of the hydrated sample 30C, with 70 wt.% HGMSs and 30 wt.% CSA cement. The acceleration voltage was set to 5 kV, while a SE detector was used: (**a**) magnification 200\(\times\); (**b**) magnification 500\(\times\).
# Influence of Loess-Mudstone Strata Structure on Slope Seismic Stability of Loess Plateau in China
###### Abstract  
The widely distributed loess-mudstone strata structure in the Loess Plateau of China is one of the fundamental reasons for the susceptibility and group occurrence of seismic landslides in the area. This study took the loess landslides induced by the Haiyuan earthquake as a case study. Through extensive field investigations, the loess cover thickness, mudstone dip angle, slope height, and slope angle were chosen as the influencing factors. Together with mathematical statistics and geotechnical tests, two models, one of a loess-mudstone slope and the other of a pure loess slope, were constructed. Their seismic stabilities under different slope angles and slope heights were compared to analyze the influence of the underlying mudstone strata on the loess slope seismic stability. Meanwhile, a sensitivity analysis of these strata factors affecting the seismic stability of the loess-mudstone slope was carried out based on the orthogonal test. The results showed that ignoring the underlying mudstone strata would lead to smaller calculation results than the actual situation. The sensitivity influence of the factors on the loess-mudstone slope seismic stability comprised the slope angle, loess cover thickness, slope height, and mudstone dip angle from high to low.  
loess slope; strata structure; sensitivity analysis; stability analysis Article
# Phase Calibrated Ring Oscillator PUF Design and Application
## 6 Experimental Results  
In this section, we present the measured data from six implemented PCPUFs on two KC705 boards, as shown in Figure 18. The temperature of FPGAs was controlled at 30 \({}^{\circ}\)C according to the on-chip sensor, and the voltage was 1.008 V. Table 1 lists the post-implementation primitive utilization of the traditional RO PUF with the shift register and the optimized PCPUF. We excluded the UART and FIFO module in the design to make a fair evaluation. The overall utilization of our PCPUF was 0.58% of the look-up tables and 0.05% of the flip-flops on the Kintex-7 FPGA. We cannot compare our PC PUF design with a traditional RO PUF architecture (Figure 1), because only 35 RO arrays can be placed on a Kintex-7 FPGA, which means four FPGAs are required to provide enough area for a 128-bit traditional RO PUF implementation. By using a shift register or hash function in our design, we are able to easily generate 128-bit responses in a fraction of a single FPGA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref Name** & **RO PUF** & **PCPUF** & **Functional Category** \\ \hline LUT1 & 514 & 2 & LUT \\ LUT2 & 268 & 857 & LUT \\ LUT3 & 175 & 55 & LUT \\ LUT4 & 52 & 94 & LUT \\ LUT5 & 273 & 30 & LUT \\ LUT6 & 293 & 279 & LUT \\ MUXF7 & 0 & 34 & MuxFx \\ MUXF8 & 0 & 16 & MuxFx \\ FDCE & 20 & 16 & Flop and Latch \\ FDRE & 430 & 195 & Flop and Latch \\ FDSE & 1 & 1 & Flop and Latch \\ CARRY4 & 7 & 9 & CarryLogic \\ IBUF & 4 & 4 & IO \\ OBUF & 2 & 2 & IO \\ BUFG & 1 & 1 & Clock \\ IDELAYE2 & 0 & 2 & IO \\ IDELAYECTRL & 0 & 1 & IO \\ Slice & 647 & 365 & CLB Slice \\ Utilization & 1.26 & 0.71 & \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: FPGA resource usage of the shift-register RO PUF and the optimized PCPUF implementations for a 128-bit response. Traditional RO PUF cannot be implemented on FPGAs for 128 bits. CLB, Configurable Logic Block.
# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
## 2 Preliminaries  
### Graph Neural Networks  
Traditional machine learning and deep learning methods model sequential data in isolation, ignoring the complex associations between student, teacher, and course entities [19]. As the demand for solving complex relational and structural problems grows, some researchers have explored the application of graph data structures in the field of online course recommendations. Graph data structures have proven to be more effective in capturing relationships between entities. Graph Neural Networks (GNNs) have been successfully applied in many fields [20], such as event prediction, scene recognition, and image classification [21; 22]. [PERSON] et al. and [PERSON] et al. [21; 23] proposed the use of recurrent neural networks to process graphs and further improved this approach in subsequent work. In this context, Graph Neural Networks have been introduced into online education. GNNs are a class of deep learning models specifically designed to handle graph-structured data. They can not only effectively capture the relationships between entities but also learn global information about the entire graph [23].  
GNNs technology is a powerful tool for modeling multiple entities and their relationships in course recommendation for online education systems. It employs graph embedding representation learning to organically map learners, teachers, exercises, and knowledge concepts into a unified vector space. By computing vector similarity or predicting edge attributes, GNNs transform online education course recommendation problems into classic graph tasks, thereby providing new enhancements for various intelligent services in ITS [12; 13; 15; 16; 24].  
Although the above studies show that GNNs-based models outperform other methods, GNN models also have inherent shortcomings. They are less capable of modeling complex item relationships and cannot fully mine cross-learner correlations in courses. In a simple graph structure, each edge can only connect two nodes. In contrast, in a hypergraph, each hyperedge can connect multiple course nodes. For example, two courses may appear unrelated in a short-term sequential context, but if the perspective is expanded to the global graph structure, they may exhibit strong correlations.
# Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability
###### Abstract  
Analysis of the stability of slopes in fractured rock masses is not trivial and is fraught with uncertainty and risk. A DFN-DEC model (Discrete Fracture Network-Distinct Element Code) based on the MATLAB platform is developed to evaluate the stability of rock slopes with random fractures. Then, the influences of mean values of geometric characteristics (i.e., the trace length \(T\), the dip \(D\), and the spacing \(S\)) for both the horizontal (denoted with 1) and inclined (denoted with 2) fractures on the mean slope stability are investigated. The results indicate that the proposed DFN-DEC model based on the MATLAB platform is adequate, robust, and can generate more realistic fracture networks. By means of probabilistic analysis (i.e., Monte Carlo simulation), we can obtain a more accurate stability assessment result of fractured rock slopes. In addition, it is found that the \(\mu_{F_{5}}\) (the mean of \(F_{8}\)) of slopes decreases with the increase in \(\mu_{T_{1}}\), \(\mu_{T_{2}}\) (the mean trace length of horizontal and inclined fractures, respectively) and \(\mu_{D_{1}}\) (the mean dip of horizontal fractures), and increases gradually with the increase in \(\mu_{D_{2}}\) (the mean dip of inclined fractures), \(\mu_{S_{1}}\) and \(\mu_{S_{2}}\) (the mean spacing of horizontal and inclined fractures, respectively). Furthermore, the geometric characteristics related to inclined fractures have a much greater influence on \(\mu_{F_{5}}\) than that related to horizontal fractures. This study can be well applied in engineering practice, e.g., preliminary evaluation of the slope stability according to the statistics of fracture geometric characteristics.  
fractured rock slope; stability analysis; fracture geometry; DFN-DEC model; Monte Carlo simulation 2022  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]; [PERSON] Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability. _Appl. Sci._**2023**, _13_, 226. [[https://doi.org/10.3390/app13010236](https://doi.org/10.3390/app13010236)]([https://doi.org/10.3390/app13010236](https://doi.org/10.3390/app13010236))
## 3 Methods and Results
### The Force Exertion of the End-Effector  
The end-effector force exertion test platform, shown in Figure 17a, was utilized to evaluate the force exertion capability of the robot, employing a Nano 17 force transducer. A Geomagic Touch haptic device (3D Systems, Littleton, CO, USA), shown in Figure 18a, was used to control the robot, with the pose of the haptic device handle read using Python. The three translation DOFs and the two rotation DOFs of the effector corresponded with those of the haptic device. The motion range of the effector tip was confined within a 30 mm diameter sphere in the simulated workspace after mapping to the effector tip. Forces were recorded as the effector tip moved within the motion range as shown in Figure 17b. The maximum forces measured along each axis are detailed in Table 3. Given the long instrument, the softness of the robot scaffold, and the elasticity of the plastic cables, the force output along the X and Y axes is comparatively lower. According to the measured forces of 0.9 N and 0.58 N for lifting and pulling mucosa in the rectum [29], the force exertion of the end-effector for pulling the mucosa appears to be sufficient. However, the insufficient force for lifting the mucosa may not pose an issue in practice, as, for tissue dissection tasks (such as \"peeling off\" the mucosa from the submuc during ESD), retraction in the Z axis holds greater significance. However, this will require validation in subsequent pre-clinical trials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Circle Diameter (mm)** & **29** & **36** & **43** & **50** \\ \hline Position hysteresis (mm) & 0.6553 & 1.065 & 2.388 & 2.070 \\ Angle hysteresis (\({}^{\circ}\)) & 0.5143 & 0.8359 & 1.874 & 1.624 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hysteresis test results.  
Figure 17: The end-effector’s force exertion test. (**a**) The test platform. (**b**) The force exertions by the end-effector in the X, Y, and Z axes and forces’ magnitude.  
Figure 18: The simulated surgical experiments. (**a**) The experimental platform. (**b,c**) The lifting of a simulated tissue. (**d,e**) The performing of a ‘Sweep’ motion.
## 4 Results and Discussion
### End-to-End Latency for Priority Packets  
In communication network, the end-to-end latency of a packet is measured as the time between its generation and reception at its destination. The formula for calculating the average end-to-end latency is shown below.  
\[d_{ETE}=d_{queu}+d_{trans}+d_{proc}+d_{prop} \tag{10}\]  
There are four different latencies in this equation: the latencies associated with queuing, transmitting, processing, and propagation. In this equation, \(d_{queu}\), \(d_{trans}\), \(d_{proc}\), and \(d_{prop}\) signify the queuing time, the transmission time, the processing time, and the propagation time, respectively.  
Figure 8: Average energy consumption per-bit.  
Figure 11 shows the average end-to-end latencies of EQPD-MAC, MPQ-MAC, PMME-MAC, and RMP-MAC for the highest and lowers priority data packets. According to the results, EQPD-MAC has a 75% reduction in latency for \(P_{4}\) priority packets compared to  
Figure 10: Sink energy consumption in Joule.  
Figure 9: Average energy consumption per sensor node in Joule.  
MPQ-MAC. EQPD-MAC protocol also transmits P\({}_{4}\) priority packets more quickly than RMP-MAC's highest priority level (P\({}_{3}\)) by up to 74%. Compared to the other protocols, MPQ-MAC has the highest latency because the nodes must wait longer for the waiting timer \(T_{w}\) to expire, resulting in the higher latency. It can be seen that there is a noticeable difference in the priority packet latency between the PMME-MAC and EQPD-MAC protocols. PMME-MAC does not consider any technique to minimize the energy consumption and achieves reduced end-to-end latency since the sensor nodes never sleep, which increases the energy consumption of the sensor nodes. However, to prevent any network disruption, the EQPD-MAC uses the TA mechanism to conserve more energy at the cost of a marginal increased latency. Despite this, the EQPD-MAC can still support the highest priority packet to provide an acceptable latency for priority packets, while maintaining the lowest energy consumption.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 3 Composite Kernels for SVM  
In this section, composite kernel feature fusion is proposed to combine different features for PolSAR image classification. SVM and kernels are also briefly described.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Morphological Operations** & **Expression** \\ \hline Erosion & \(\varepsilon(I,b)(s,t)=\min\{I(s+x,t+y)-b(x,y)|(s+x,t+y)\in D_{I};(x,y)\in D_{b}\}\) \\ Dilation & \(\delta(I,b)(s,t)=\min\{I(s-x,t-y)+b(x,y)|(s-x,t-y)\in D_{I};(x,y)\in D_{b}\}\) \\ Opening & \(\gamma_{b_{m},b_{ai}}(I)=\delta_{b_{ai}}(\varepsilon_{b_{m}}(I))\) \\ Closing & \(\delta_{b_{m},b_{ai}}(I)=\varepsilon_{b_{ai}}(\delta_{b_{m}}(I))\) \\ Opening by reconstruction & \(\gamma^{rec}_{b_{ai},bc}(I)=\gamma_{bc,I}(I)\), \(J=\varepsilon_{b_{m}}(I)\) \\ Closing by reconstruction & \(\phi^{rec}_{b_{ai},bc}(I)=\phi_{bc,S}(I)\), \(g=\delta_{b_{ai}}(I)\) \\ \hline \hline \end{tabular} \(\varepsilon\) and \(\delta\) represent erosion and dilation operation, respectively. \(b_{ali}\) and \(b_{ero}\) represent SE of dilation and erosion.  
\(I\) represent the _SPAN_ image.  
\end{table}
Table 2: Summarization of morphological operations.  
Figure 2: Polarimetric-spatial classification of PolSAR images by feature fusion.
## 3 Results
### Pelagic Part of the Ecosystem
#### 3.2.1 Phytoplankton  
The multivariate CAP analysis showed that the phytoplankton community structure was influenced by: warming (F1,31 = 21.08, \(p\) = 0.001), chlorophyll a (F1,31 = 5.60, \(p\) = 0.002), PO4 (F1,31 = 7.88, \(p\) = 0.002), total N (F1,31 = 3.58, \(p\) = 0.024), and pelagic primary production (PPP) (F1,31 = 3.88, \(p\) = 0.016; Figure 4). Warming divided the phytoplankton community into two clusters, where the warm temperature treatments were mainly related to an increase in pelagic primary production as well as the chlorophyll a increase, which was driven by _Dolicosperm_ filaments and heterocysts (Figures 4 and 5a,b,d; Table S1). Increasing PO4 concentrations influenced centric diatom species and _Pyramimonas_ sp., which characterized ambient temperature treatments (Figures 4 and 5c,e; Table S1). _Melosira_ sp. abundances were influenced by increasing total N concentrations (Figure 4).
# A Mechanism for Reliable Mobility Management for Internet of Things Using CoAP
## Appendix A
We can conclude that after several retransmission attempts, the average waiting time will converge to \(T_{r}+T_{w}\) for the first retransmission, \(T_{r}+2T_{w}\) for the second retransmission, and \(T_{r}+3T_{w}\) for the third retransmission. The retransmission attempts are repeated until the retransmission counter reaches \(m\). Because the probability of the occurrence of a first retransmission is \(\left(1-A_{f}\right)A_{f}\), the average retransmission delay for a CoAP signaling message on its first retransmission attempt becomes \(\left(T_{r}+T_{w}\right)\times\left(1-A_{f}\right)A_{f}\), as shown in Table 11. As shown in Table 11, the average retransmission delay for a single CoAP signaling message on its second retransmission attempt is \(\left(T_{r}+T_{w}+2T_{w}\right)\times\left(1-A_{f}\right)A_{f}^{2}\). The average retransmission delay for a CoAP signaling message on its third retransmission attempt is \(\left(T_{r}+4T_{w}+2T_{w}+T_{w}\right)\times\left(1-A_{f}\right)A_{f}^{3}\), and the average retransmission delay for a CoAP signaling message on its fourth retransmission attempt is \(\left(T_{r}+8T_{w}+4T_{w}+2T_{w}+T_{w}\right)\times A_{f}^{4}\). Therefore, the average retransmission delay, \(D\), for transmitting a CoAP signaling message considering the exponential back-off retransmission mechanism can be calculated as follows: \(D=\sum_{k=0}^{m-1}(1-A_{f})A_{f}^{k}\Big{\{}T_{r}+\left(2^{k}-1\right)T_{w} \Big{\}}+A_{f}^{m}(2^{m}-1)T_{w}\).
## 2 Method
### Obstacle Prediction Using Fusion Prediction
#### 2.1.1 Physics-Based Prediction: CVM  
In this work, we employ the constant velocity model (CVM) as our physics-based approach due to its strong performance and simplicity [31].  
The CVM assumes that an obstacle maintains a constant velocity over a short prediction horizon, simplifying the prediction process while providing reasonably accurate forecasts for the near future. Suppose at time step \(i\), we detect a moving target with position coordinates \((x_{i}^{o},y_{i}^{o})\), airspeed \(v_{i}^{o}\), and heading angle \(\varphi_{i}^{o}\). Using these variables, the state of the moving obstacle can be denoted as \(\xi_{i}^{o}=[x_{i}^{o},y_{i}^{o},\varphi_{i}^{o}]^{T}\). The state of the moving obstacle at the next time step \(i+1\) can be expressed as follows:  
\[\xi_{i+1}^{o}=\xi_{i}^{o}+v_{i}^{o}\begin{bmatrix}\cos\varphi_{i}^{o}\\ \sin\varphi_{i}^{o}\\ 0\end{bmatrix}\Delta t+w_{i},\quad w_{i}\sim\mathcal{N}(0,Q_{i}) \tag{3}\]  
where \(w_{i}\) are stochastic processes representing the uncertainty in the model, characterized as Gaussian, zero-mean, white noise, and independent random variables.
## 3 Simulated Annealing Algorithm with Cooperative Threads (SACT)
The final control coefficient, \(C_{f}\), the initial control coefficient (temperature), \(Co\), the control factor \(\alpha\) and the best f(\(S_{Hi,c}\)) (at the beginning this is a very large value) are initialized. 3. If the number of SA (\(iter\)) for SACT has not been completed (which is indicated with Maxiter), then a new SA is begun using the new solution \(S_{Hi}\) obtained with the _effective-address_ procedure. **3.1. ** Begin the annealing \(iter\). **3.2. ** The value of initial control coefficient of the SA is initialized. **3.3. ** The external cycle begins, which carries out the decrease in control coefficient (3.3.2) of the SA according to \(\alpha\). **3.3.1. ** The internal cycle in annealing begins, which executes the Metropolis algorithm until equilibrium is reached, this depends on the size of the Markov chain (\(MC\)) and that for optimization problems is represented by the neighborhood size of a solution of the problem. **3.3.1.1. ** A neighborhood structure is used (explained later on, Section 3.1). This generates a state in annealing (neighbor \(S^{\prime}_{Hi}\)). **3.3.1.2.\(S^{\prime}_{Hi}\)** is accepted as a new configuration if the energy of the system decreases. 3.1.3.If the energy of the system increases, \(S^{\prime}\)_Hi_ is accepted as a new configuration according to the probability of acceptance \(P_{accpt}\) obtained by the function of Boltzmann, and 3.1.4. Comparing this \(P_{accapt}\) with \(\rho\), which is a random number uniformly distributed between (0, 1). 3.1.5.If \(\rho<P_{accpt}\) the generated state is accepted as the current state. 3.1.6.If the new schedule cost f(\(S_{Hi}\)) is better than the best schedule cost f(\(S_{Hi,c}\)) of the thread _Hi_, then \(S_{Hi,c}\) is upgraded. 3.1.7.If the new schedule cost f(\(S_{Hi}\)) is better that the best schedule cost f(\(S_{best}\)) which has been obtained from all the threads of SACT, then \(S_{best}\) is upgraded. 3.3.2. The control coefficient is decreased. 3.4. Every time that the thread _Hi_ in execution finishes an SA, an _effective-address_ is carried out between the best solution \(S_{Hi,c}\) obtained from the SA and the best existing solution \(S_{best}\) in the algorithm SACT. The _effective-address_ mechanism is explained later (Section 3.2). The final solution of the algorithm SACT is \(S_{best}\), which is obtained by the cooperation of the \(n\) threads generated with SACT.
## 3 Results and Discussion
### Multivariate Statistical Analysis  
The abovementioned analysis results indicate that there are significant differences in the volatile components and aroma quality of Rucheng Baimao black tea in different seasons. To further explore the seasonal variations of the volatile components in Rucheng Baimao black tea, this study conducted a multivariate statistical analysis using the relative contents of 140 volatile components.