esa-sceva/satcom-chunk-collection huggingface dataset:
# Field Test Study on Vertical Bearing Characteristics of Coal Rock
## 3 Results
### Bi-Directional Load Test
#### 3.2.3 Deformation Modulus  
The tip resistance test of TP1 and TP2 is equivalent to the situation of local loading in the elastic semi-infinite space. Based on the Mindlin solutions for stress and displacement, the deformation modulus of coal rock can be calculated [28]:  
\[E_{0}=\omega\frac{pd}{s} \tag{6}\]  
\[\omega=\frac{(1+\
u)}{16(1-\
u)}\left\{\begin{array}{l}\frac{109(3-4\
u)}{64}+ \left(8\
u^{2}-12\
u+5\right)\left\{\sqrt{1+4m^{2}}\left[2-\frac{1}{4(1+4m^{2 })}-\frac{3}{64(1+4m^{2})^{2}}\right]-4m\right\}\\ \\ +(10-16\
u)m^{2}\left\{\frac{1}{m}-\frac{\left[2+\frac{1}{4(1+4m^{2})}+\frac{ 9}{64(1+4m^{2})^{2}}\right]}{\sqrt{1+4m^{2}}}\right\}\\ \\ +2m^{2}\left\{\frac{1}{m}-\frac{\left[1-\frac{1}{4(1+4m^{2})}-\frac{3}{64(1+4m ^{2})^{2}}\right]}{\sqrt{1+4m^{2}}}-\frac{4m^{2}}{(1+4m^{2})^{\frac{3}{2}}} \right\}\end{array}\right\} \tag{7}\]  
\[m=\frac{h}{r} \tag{8}\]  
where \(w\) is the comprehensive coefficient obtained from [PERSON]'s vertical displacement solution; \(m\) is the coefficient; \(h\) is the depth of the loading area; \(r\) is the radius of the loading area.  
Equation (6) can be rewritten as:  
\[p=\frac{E_{0}}{\omega d}s \tag{9}\]  
It can be seen from Formula (9) that if \(p\)-\(s\) is proportional, \(E_{0}\) can be calculated from the slope of the \(p\)-\(s\) curve.  
The design value of the unit tip resistance is 1729 kPa. Within this stress range, the unit tip resistance-displacement curve is close to a straight line. The test points of TP1 and TP2 are uniformly taken as the straight-line segment within the maximum pressure of 1886 kPa. The curve fitting results are shown in Figure 14. It reveals that there is a good linear correlation between the unit tip resistance and the displacement.  
Figure 14: Relationship between unit tip resistance and displacement at liner segment: (**a**) TP1; (**b**) TP2.  
The \(E_{0}\) values of TP1 and TP2 calculated according to Formula (9) and Figure 14 are 183.9 MPa and 228.8 MPa, respectively. The average value is 206.3 MPa, and it is taken as the deformation modulus of the coal rock under the working load.  
The \(E_{0}\) value corresponding to the \(p\) value can be calculated according to Formula (6). The results beyond the design value of unit tip resistance of 1729 kPa are shown in Figure 15. It shows that the deformation modulus decreases with the increase in the pressure.
## 4 Discussion
### NEZ Influence on Soil N and C Pools.  
#### 4.1.1 NEZ Effects on N Pool Size and Isotopic Signature  
The addition of NEZ into the soil (7 CZ treatment) did not lead to significant differences in TN or Fix N-NH\({}_{4}\)\({}^{+}\) content regardless the N input of about 410 kg of N ha\({}^{-1}\) brought by NEZ introduction. On the other hand, this N input has clearly influenced Exch N-NH\({}_{4}\)\({}^{+}\), N-NO\({}_{3}\)\({}^{-}\) and N\({}_{\rm mic}\) pools, increasing their size. Through the application of a simple mass balance equation, it was estimated that the increment in 7 CZ Exch N-NH\({}_{4}\)\({}^{+}\) pool corresponded to the amount of N introduced with NEZ in the first 30 cm of soil.  
As it is known, NH\({}_{4}\)\({}^{+}\)-enriched zeolites can act as slow-released fertilizer (Stein  
after the zeolitite spreading as N-NO3\({}^{-}\) content was very similar to the CNTR notwithstanding the large N input provided by NEZ spreading (with residual N-NO3\({}^{-}\) from the pig slurry treatment). The agronomic year before the experimental cultivation was low in precipitation and the drought caused low yield and accumulation of high amounts of N from fertilizers in the upper soil horizon, thus no differences were expected between CNTR and ZT. A possible explanation of this behavior is that controlled retention and release capacity of NH4\({}^{+}\) contained in the microsite of the open-ring structure of the mineral. These ions are physically protected and likely less available to nitrifying bacteria (leading consequently to a lesser N-NO3\({}^{-}\) content in the pore-waters) [45; 46; 47; 48]. This hypothesis is plausible but not supported by a direct measurement of gross nitrification rates (e.g., by the mean of \({}^{15}\)N pool dilution technique). In this light, further studies are urgently needed where gross N transformation rates are measured in soil amended with NEZ and NZ to efficiently quantify gross N production and consumption through mineralization, nitrification and denitrification processes.  
In May-2013, Cmic and Nmic increased significantly in all treatments, while N-NO3\({}^{-}\) pool significantly decreased (but not Exch N-NH4\({}^{+}\) pool with the exception of 7 CZ) (Figure 4), suggesting that microbial immobilization prevailed over mineralization [49]. In addition to NO3\({}^{-}\) decrease, 7 CZ treatment showed a reduction of Exch N-NH4\({}^{+}\) pool, where the N surplus caused by the NEZ introduction completely disappeared. A fraction of this mineral N depletion can be attributable to NO3\({}^{-}\) leaching and/or to N2-N2O losses through denitrification process (probably enhanced by waterlogged conditions; [25]). It is important to consider that NEZ introduction affected mainly the Exch N-NH4\({}^{+}\) and not the Fix N-NH4\({}^{+}\) pool and that 7 CZ still had high TN \(\delta^{15}\)N in May-2013, confirming that an important fraction of NEZ-N was still present in the soil system. It is thus possible that a large fraction of the exchangeable N contained into NEZ has been immobilized by the microbial biomass [50; 51].
## 3 Results
### Comparative Experiments
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class No.** & **DeepHyperX** & **ViT** & **SpectalFormer** & **SSFTT** & **MorphFormer** & **HybridSN** & **DBMA** & **DCTransformer** \\ \hline
1 & 88.98 & 83.00 & 82.15 & **82.53** & 81.67 & 80.15 & 81.86 & 82.43 \\
2 & 94.27 & 98.50 & 98.78 & 100.00 & 100.00 & 100.00 & 95.77 & **100.00** \\
3 & 76.43 & 85.74 & 94.65 & 96.24 & 97.82 & 94.85 & **99.20** & 99.00 \\
4 & **99.91** & 99.53 & 91.73 & 99.72 & 96.69 & 96.68 & 98.29 & 95.92 \\
5 & 80.69 & 66.57 & 87.32 & 80.88 & 95.87 & 99.24 & **99.71** & 90.78 \\
6 & 99.34 & 95.93 & 96.59 & **100.00** & 97.63 & **100.00** & **100.00** & 99.72 \\
7 & 95.80 & 72.03 & 90.91 & 92.31 & 95.80 & 93.56 & 95.42 & **95.80** \\
8 & 88.07 & 61.05 & 68.07 & 91.23 & 81.75 & 88.03 & 79.88 & **91.23** \\
9 & 87.41 & 90.21 & 90.30 & 95.90 & 93.28 & 90.65 & 88.66 & 95.06 \\
10 & 70.30 & 83.11 & 79.89 & 95.92 & 97.53 & 88.52 & 89.67 & **98.58** \\
11 & 77.30 & 63.72 & 73.69 & 74.45 & 81.95 & 90.03 & 96.29 & **91.93** \\
12 & 83.38 & 68.56 & 76.02 & 84.51 & 89.71 & **92.02** & 92.12 & 90.75 \\
13 & 79.34 & 89.38 & 51.54 & 80.79 & 93.63 & 82.80 & 88.87 & **94.40** \\
14 & 83.81 & 93.12 & 93.52 & 100.00 & 99.60 & 95.54 & 98.78 & **100.00** \\
15 & 73.57 & 85.62 & 77.17 & 82.24 & 87.53 & **100.00** & 99.36 & 97.67 \\ \hline OA & 85.35 & 83.17 & 83.78 & 89.80 & 92.74 & 92.27 & 92.20 & **94.40** \\ AA & 85.24 & 82.74 & 84.01 & 90.45 & 92.71 & 92.80 & 93.16 & **94.89** \\ \(\varepsilon\) (100) & 84.09 & 81.74 & 82.39 & 88.93 & 92.11 & 91.43 & 91.53 & **93.92** \\ F1 score & 85.37 & 83.21 & 83.72 & 89.69 & 92.64 & 92.08 & 92.19 & **94.41** \\ precision & 85.86 & 84.49 & 84.89 & 90.25 & 92.97 & 92.44 & 92.76 & **94.83** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance (IN%) on the **UH** HSI dataset. exhibits a remarkable overall accuracy (OA) of 96.73%, an average accuracy (AA) of 97.80%, and a kappa coefficient (\(\kappa\)) of 96.25%. Our proposed approach achieves a better performance by invoking superior attention mechanisms. Moreover, Figure 5 visualizes the classification maps generated for the IP dataset. In Table 7, the Trento dataset's results are demonstrated for the DCTransformer model we proposed, which displays an OA of 97.45%, an AA of 95.07%, and kappa coefficients of 96.58%, respectively, which is approximately 0.6%, 1.8%, and 0.9% greater than the SSFTT. Figure 6 shows the 2D graphical plots depicting the features extracted with t-SNE on the Trento dataset. Through analysis of the t-SNE, the category points produced by our methods are more concentrated, which means our DCTransformer has a better performance in classification. Figure 7 shows the visualization with the confusion matrix for the Trento dataset. Our method has a higher True Positive Rate (TPR) compared with other methods.
## 3 Experiments and Comparison
### Image Correction Effect of Image Size Used in Linear Fitting Model  
The two-step approach for infrared scan images is proposed in this research. First, a portion of the high-resolution scan image is intercepted, and then the intercepted image is corrected using a one-dimensional guided filter model. The next step is to calculate the coefficients of the linear fitting model and then use the correction coefficients to correct the full image frame. This section primarily studies the effect of intercepted image size on the correction. The experimental data consists of 20 frames of real infrared scanning images without nonuniformity to which horizontal stripe was added using Formula (15), where parameter \(g(i)\) follows a Gaussian distribution with a mean of 1 and a variance of 0.02 and parameter \(b(i)\) follows a Gaussian distribution with a mean of 0 and a variance of 0.02. The scan images with various column counts are intercepted, and the PSNR of the images after correction is computed. Table 1 displays the average findings of the full dataset.  
Table 1 demonstrates that when 1500 scan image columns are picked for the calculation of correction coefficients, the image can already reach a good level. PSNR increases somewhat when additional scan image columns are selected to calculate correction coefficients. As the number of image columns increases, the PSNR of the corrected image tends to stabilize and does not grow with the number of image columns. This demonstrates that this approach is practical for the nonuniformity correction of the infrared scan image and can only use a portion of the image to calculate the correction coefficients needed to finish the correction of the entire image frame.
# Research on Dynamic Modeling and Transition Flight Strategy of VTOL UAV
## 5 Flight Strategy Calculation Case
### Transition Window  
The transition flight window can be divided into the transition front window and the transition terminal window. The transition front window is the flight status of hovering at a safe height after vertical take-off. The transition terminal window is the flight state that meets the safe level flight speed. The terminal window usually has three important flight parameters: flight speed, power thrust and aircraft attitude.  
The window of the transition front section of the vertical take-off and landing UAV in this scheme is horizontal hovering at a safe height of 30 m. The terminal window can be calculated by the equilibrium equation. \(W=L=\rho/2\cdot V_{s}^{2}\cdot S\cdot C_{L\max}\), \(V_{safe}=1.2V_{s}\), \(M=0\), where \(W\) is the weight of the aircraft; \(L\) is lift; \(\rho\) is the air density; \(V_{s}\) is stall speed; \(S\) is the wing area; \(C_{L\max}\) is the maximum lift coefficient; \(V_{safe}\) is the safe flight speed of the transition terminal window.  
The balance equations of force and moment can be derived from the above model, where, the force equilibrium equation is \(T_{f}\cos\theta+(T_{d}+F_{Xin})\cdot\sin(\theta_{d}+\theta)=G\); Moment equilibrium equation is \(T_{f}\cdot l_{f}=T_{d}\sin\theta_{d}\cdot l_{d}+F_{Xin}\sin\theta_{d}\cdot l _{in}\), where \(l\) is moment arm.  
In order to simplify the calculation, only the longitudinal balance problem is considered. Previous formula can be used to calculate the safe flight speed \(V_{safe}=34.70m/s\) of the transition terminal window, and the lift duct deflection angle \(\theta_{d,safe}=24.12^{\circ}\) and attitude angle \(\theta_{safe}=7.21^{\circ}\) at this time. In other words, the flight speed is greater than the safe flight speed \(V_{safe}\), and the lift duct deflection angle is less than \(\theta_{d,safe}\), and the attitude angle is stable at \(\theta_{safe}\).
## 5 Results
### Stress-Dependent Relative Permeability  
The relative permeability is computed as a function of the water saturation and pore size distribution index (\(\lambda_{p}\)) using the Brooks and Corey method, as below:  
\[Kr_{w}=S_{w}{}^{(3+2/\lambda_{p})} \tag{18}\]  
\[Kr_{o}=(1-S_{w})^{2}\Big{(}1-S_{w}{}^{(1+2/\lambda_{p})}\Big{)} \tag{19}\]  
Figure 11: Capillary pressure variations against stress loading at \(V_{w}=0.1\).  
Figure 10: Capillary pressure variations for different injected water volumes and stress-loading conditions.  
The relative permeabilities are computed using the saturation values obtained from the CFD simulations conducted under different stress loadings during water volume injections. Figure 12 depicts the variations in the relative permeabilities of water (\(Kr_{w}\)) and oil (\(Kr_{o}\)) at the fracture-matrix interface for different water-saturation (\(S_{w}\)) and stress-loading conditions. It can be seen that as water saturation increases, the relative permeability of oil reduces nonlinearly. Water relative permeability has the opposite tendency of oil relative permeability, i.e., water relative permeability increases as water saturation increases. This is because injected water pushes the oil out of the fractured reservoir, reducing its relative permeability, while the water relative permeability increases.
## 3 Network Models and Related Definitions
### Energy Model  
The energy consumed by nodes in hybrid WSNs includes sending and receiving data, processing data, and other energy consumption [39]. While static nodes consume energy mainly from sending and receiving data, mobile nodes consume a large quantity of energy for movement when repairing coverage holes, in addition to their own energy consumption for sending and receiving data. A node's energy consumption by sending \(l\) bit of data while \(d\) meters apart is:  
\[E(l,d)=\left\{\begin{aligned} & IE_{elec}+l\varepsilon_{fs}d^{2}& d<d_{0}\\ & IE_{elec}+l\varepsilon_{amp}d^{4}& d>d_{0}^{ \prime}\end{aligned}\right. \tag{2}\]  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{amp}}}, \tag{3}\]  
\(E_{r}(l,d)\) indicates the node's energy consumption by receiving \(l\) bit of data:  
\[E_{r}(l,d)=l\times E_{elec}, \tag{4}\]  
\(E_{m}\) indicates the node's energy consumption by moving \(d\) metres:  
\[E_{m}=d\times e_{0}, \tag{5}\]  
where \(E_{elec}\) represents the node's energy consumption by sending and receiving a unit bit of data, \(d_{0}\) is the transmission distance threshold, \(\varepsilon_{fs}\) and \(\varepsilon_{amp}\) are the power amplification circuit loss coefficients under the free-space and multiplex fading models, respectively, and \(e_{0}\) is the node's energy consumption when it moves a unit distance.
## 2 Downlink Massive MIMO-OFDM System Model  
This paper considers the \(N_{t}\times N_{u}\) downlink MU massive MIMO-OFDM system in Figure 1. The number of transmit antennas at the BS is \(N_{t}\) and the number of total users where each user has single antenna is \(N_{u}(N_{t}>N_{u})\). At the transmitter, the modulated symbols are pre-coded by using one of the various pre-coding schemes mentioned in [10], and the OFDM symbols which include the CP go through wireless frequency selective channels.  
The channel matrix \(\mathbf{H}\) between all transmit antennas and total users is as follows,  
\[\mathbf{H}=\left[\begin{array}{cccc}\mathbf{h}_{1}&\mathbf{h}_{2}&\cdots& \mathbf{h}_{N_{u}}\end{array}\right]^{T}, \tag{1}\]  
where the \(n\)-th entry in \(\mathbf{h}_{m}=\left[\begin{array}{cccc}h_{m1}&h_{m2}&\cdots&h_{mN_{t}}\end{array} \right]^{T}\) is independent and identically distributed (i.i.d.) complex baseband Rayleigh fading coefficient with zero mean and unit variance from the \(n\)-th transmit antenna to the \(m\)-th user. An index of subcarrier is ignored for simple notation and generality is not lost. At the \(m\)-th user, the received symbol \(y_{m}\) after the fast Fourier transform (FFT) is as follows,  
\[y_{m}=\sqrt{P}\mathbf{h}_{m}^{T}\frac{\mathbf{g}_{m}}{\|\mathbf{G}\|_{F}}x_{m }+\sum_{n=1,n\
eq m}^{N_{u}}\sqrt{P}\mathbf{h}_{m}^{T}\frac{\mathbf{g}_{n}}{\| \mathbf{G}\|_{F}}x_{n}+z_{m}, \tag{2}\]  
Figure 1: The downlink multi-user (MU) massive MIMO-OFDM system model. S/P: serial to Parallel; ZF: zero-forcing; GS: Gauss–Seidel; NS: Neumann series; IFFT: inverse fast Fourier transform; CP: cyclic prefix.  
where \(P\) is downlink transmit power, \(\mathbf{G}\) is \(N_{t}\times N_{u}\) pre-coding matrix, \(\mathbf{g}_{n}\) is the \(n\)-th column vector of \(\mathbf{G}\), \(x_{m}\) is the \(m\)-th complex transmit symbol with zero mean and unit variance, and \(z_{m}\) is the \(m\)-th complex additive white Gaussian noise (AWGN) with zero mean and unit variance. Finally, \(\|\cdot\|_{F}\) is the Frobenius norm operator.
# Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
## 5 Discussion
A number of simulations with different number of training rounds are conducted for comparison. The average values during the whole learning process are calculated. As shown in Tables 3 and 4, the reward and access delay of SA-FRL relay selection with different number of training rounds are compared, respectively. It can be seen that with the number of training rounds increasing, the reward is higher. After some rounds of training, the long-term reward is close to an extremum value. The reward is approximately the extremum value at about 1200, where the reward of SA-RL is converged to a stable value as shown in Figure 9. It indicates that the amount of per-training data do not have to be as large as test data, the computational burden is not heavy. Furthermore, even with limited number of training rounds, e.g., 400, the pre-training still can provide obvious improvement compared to RL without pre-training. Although the number of experimental data is limited, it still can be observed from Tables 5 and 6, that the value is accessing to extremum value with more training rounds. Even with small number of training, the reward still can be improved. So we can conclude that the pre-training is effective for reinforcement learning and necessary before practical system implementation. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Training** **Rounds**} & **Normalized Access** & **Improvement (\%)** & **Improvement (\%)** \\  & **Delay of SA-FRL** & **Compared to SA-RL** & **Compared to** \\  & **Relay Selection** & **Relay Selection** & **Random Relay** \\ \hline
400 & 1.017 & 1.9\% & 14.1\% \\
800 & 1.016 & 2.1\% & 14.2\% \\
1200 & 1.014 & 2.3\% & 14.4\% \\
1600 & 1.013 & 2.4\% & 14.5\% \\
2000 & 1.013 & 2.4\% & 14.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Normalized access delay of SA-FRL with different amount of pre-training data, simulated channel. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Training** **Rounds**} & **Reward of SA-FRL** & **Improvement (\%)** & **Improvement (\%)** \\  & **Relay Selection** & **Relay Selection** & **Random Relay** \\ \hline
400 & \(-0.088\) & 33\% & 80\% \\
800 & \(-0.085\) & 35\% & 81\% \\
1200 & \(-0.078\) & 40\% & 82\% \\
1600 & \(-0.078\) & 40\% & 82\% \\
2000 & \(-0.078\) & 40\% & 82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reward of SA-FRL with different amount of pre-training data, simulated channel.
# Finite Element Analysis of Combined Energy Piles with Long and Short Heat Exchanger Tubes
## 3 Model Overview  
### Basic Assumptions  
Energy pile operation is a complex 3D unsteady heat transfer process [23] that requires the following assumptions:  
1. The operation of energy piles is a complex 3D unsteady state transmission, regardless of the influence of surface temperature changes on the temperature of the soil and pile body. The influence of liquid flow in the heating tube on the temperature distribution as well as the influence of temperature changes on the mechanical properties of the contact interface is not considered.
2. Precast piles, cement soil, heat exchanger tubes, and soil are homogeneous, their thermophysical properties and mechanical parameters do not change with temperature changes during operation, and heat is transferred between each part by heat conduction.
3. The initial temperature of the soil and pile body is consistent, and the heat exchanger tubes act as a stable heat source to transfer heat to the surroundings.
4. Groundwater-bearing soil layers are filled with groundwater, and the speed of the water flow does not change during the flow, regardless of its change along the direction of depth.
5. The pile body and cement soil are always in the elastic deformation range during the loading process, the friction coefficient between cement soil and soil remains unchanged, and the soil around the pile is elastoplastically deformed.
# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 4 Emotion Recognition
### Emotion Measurements  
Most of the studies used various stimuli to provoke the emotional states of the participants. Various methods have been described, including viewing video clips [163; 165; 165; 166; 173; 182; 183; 184], images [27; 49; 169], listen to music [8; 185; 186; 8], read texts [6; 32], and doing physical activities [177; 187; 188]. Emotions can be assessed through subjective and objective methods.  
In the first method, people record subjective measurements on Positive and Negative Affect Schedule (PANAS), and Self-Assessment Manikin (SAM) instruments [63; 173; 189]. During the process of eliciting emotional states, the user performs a self-analysis of what \"he/she feels\" and assigns the ratings to each of the SAM parameters (arousal, valence, or dominance) on a nine-point scale. Meanwhile, PANAS evaluates two 10-item scales (rating from 1: not at all to 5: very much) to estimate positive affect on the vertical axis and negative affect on the horizontal axis. Furthermore, valence and arousal dimensions are in a 45-degree rotation about these axes [190].  
The second method uses sensors or wearable devices for the measurement of physiological signals, as in [12] defined a framework that recommends songs based on the variability of the heart rate of users, a music database classified into four categories based on the degree of arousal (0 extremely low HRV to 1 extremely high HRV ) and in the degree of valence (1 very negative to 5 very positive). Also, in [47] used four domains of the emotional semantic space model (arousal, valence, sense of control, and ease of finding a goal) [181] to categorize users' affective states interacting with a video game.  
Alternatively, the consolidation of multimodal datasets has prompted the analysis of emotional stimuli with publicly available physiological data such as the Database for Emotion Analysis Using Physiological Signals (DEAP) shared by [PERSON] et al. [180], The International Affective Picture System (IAPS) published by [PERSON] et al. [191] and Nencki Affective Picture System (NAPS) proposed by [PERSON] et al. [192]. Particularly DEAP [180] contains information on peripheral physiological signals, brain activity signals, levels of arousal, valence, and dominance, and the subjective rating of the emotions perceived by 32 participants during video viewing. Based on DEAP in [184] proposed a music recommendation framework, and in [163] developed an emotional model on users' behavior in an educational environment. While IAPS [191] and NAPS [192] have a repository of photographs with the arousal and valence scores registered in SAM.  
In conclusion, these datasets have been used to design affective models for images classification [27; 49] and in the simulation of quantifiable emotional stimuli to obtain physiological data [27].