esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Passenger Comfort Based on Human-Bus-Road Coupled Vibration
## 5 Parametric Study
### Effect of Interlayer Bonding Condition of Asphalt Pavement  
In the Specification for Design of Highway Asphalt Pavement [10], the adjacent layers of the asphalt pavement are assumed to be in two extreme conditions: full-bond and no-bond. However, during the service of the asphalt road, the real interlayer bonding condition lies somewhere between full-bond condition and no-bond condition, and the stress and displacement are transmitted between the layers through the interface. The friction coefficient between layers is selected as 0.1, 0.3, 0.5, 0.7, and 0.9, respectively, which corresponds to the five bonding conditions: extremely poor, poor, medium, good, and excellent. Figure 18 shows the vertical, pitch, and roll acceleration amplitudes of a human under the five bonding conditions.  
As can be seen in Figure 18, the human acceleration decreases with the increasing of interlayer friction coefficient. Compared with the case with a friction coefficient of 0.9, the vertical acceleration, pitch acceleration, and roll acceleration in the case with a friction coefficient of 0.1 are increased by 2.44, 1.88, and 3.31 times, respectively. It indicates that the influence of the interlayer bonding condition on the acceleration of the human body is obvious.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Road Surface** & **Weighted RMS** & **Reactions** & **Annoyance** \\
**Condition** & **Acceleration/m-s\({}^{-2}\)** & **Rate** \\ \hline A level & 0.1432 & not uncomfortable & 0.0009\% \\ B level & 0.3748 & slightly uncomfortable & 9.33\% \\ C level & 0.9862 & uncomfortable & 52.90\% \\ D level & 3.4663 & extremely uncomfortable & 94.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of human comfort under four road surface conditions.  
Figure 18: The acceleration amplitude for different interlayer bonding conditions.  
The weighted acceleration RMS and annoyance rate of the human body, considering different interlayer bonding conditions, were obtained, as shown in Figure 19. With the increase of the interlayer friction coefficient, the weighted RMS acceleration of the human body decreases, and the annoyance rate of the human body decreases gradually, which means human comfort becomes better.  
Human comfort is evaluated according to both the ISO 2631 standard and the annoyance rate indicator, as shown in Table 5. In the cases with friction coefficients of 0.7 and 0.9, the weighted acceleration RMSs are 0.278 and 0.227 m/s\({}^{2}\), respectively, which are less than 0.315 m/s\({}^{2}\), and there is \"not uncomfortable according to the ISO 2631 evaluation standard. However, the annoyance rate results show that there are still 2.48% and 0.69% of passengers feeling uncomfortable, even though the evaluation result is \"not uncomfortable\" according to the ISO 2631 standard. In summary, the increase of the interlayer friction coefficient will increase the strength and integrity of the asphalt road and eventually increase the comfort of passengers.
## 3 Results and Discussion  
### Determination of Centrifuge Capacity Depending on Centrifugation Duration and Rotation Speed  
According to the results of the experiments we plotted the dependences of the most important parameters. The graphs below show the dependence of meat-bone crackling yield on the processing time at different speeds: \(n=\) 1000 rpm, \(n=\) 1500 rpm, \(n=\) 2000 rpm, \(n=\) 2500 rpm (Figure 2).  
Among the various conditions tested, the maximum yield of defatted meat-bone slurry was observed when the drum was rotated at 2000 rpm for 5 min, with a yield of 68.97%, while the lowest yield was observed when the drum was rotated at 1000 rpm for 15 min, with a yield of 55% (Figure 2A). Generally, as the drum speed increases from 1000 to 2000 rpm, the cracklings yield also increases. However, at 2500 rpm, the yield decreased compared to 2000 rpm, suggesting an upper limit beyond which the yield starts to decrease. The centrifugal force created by the rotation of the drum played a significant role in separating fat from cracklings. Higher drum speeds likely lead to better separation, but there seems to be an upper limit beyond which the yield starts to decrease, as seen at 2500 rpm. This information is valuable for optimizing the operation of the centrifuge in a meat and bone processing facility, allowing for the selection of appropriate conditions to maximize the yield of cracklings, which can have economic and efficiency implications.  
The centrifuge capacity, representing the output of defatted meat-bone slurry, was the highest at 2000 rpm for 5 min (79.98 g/min) and the lowest at 15 min and 1000 rpm (30 g/min). This aligns with the trend observed in yield. The higher speed likely enhanced the separation efficiency, resulting in higher capacity. Longer durations generally decreased the capacity, reflecting the impact of prolonged centrifugation on process efficiency [22; 23].  
The power consumption for the centrifugation process increased with both the drum speed and duration. At 2500 rpm for 15 min, the highest power input was recorded at 26.49 kW, while the lowest was at 1000 rpm for 5 min with 10.33 kW. The correlation between power consumption and drum speed is expected, as higher speeds require more energy. The increase in power consumption with longer durations may be attributed to sustained operation and maintenance of the centrifuge over time [24].  
The optimal conditions for both yield and capacity were observed at 2000 rpm for 5 min, indicating an efficient balance between speed and duration. Longer durations  
Figure 2: Graph of the dependence of yield of defatted meat-bone slurry (**A**), centrifuge capacity (**B**), power requirement for centrifugation (**C**), and maximum yield of defatted meat-bone slurry (**D**) on rotor speed and centrifugation time (different letters above the lines indicate significant differences between measurements, \(p<0.05\)).  
(15 min) generally resulted in lower yields and capacities, suggesting that extended centrifugation may not be cost-effective.
## 4 Results and Discussion
### Numerical Study on the Performance of Three-Layer Covers with Capillary Barrier
#### 4.4.1 Back Analysis of the Physical Model Test  
The model's geometry and boundary conditions were based on the laboratory model employed. Flux boundary conditions were used to apply precipitation on the soil surface by using a net infiltration rate [2; 3; 18; 51]. Zero-flux boundary conditions were assigned to the left and right sides of the soil column to create an impermeable condition [3; 52]. A unit gradient boundary condition was imposed at the bottom of the soil column to facilitate water drainage [3]. The model's geometry and boundary conditions for the soil columns can be observed in Figure 10.  
The calibrated SWCCs, representing the wetting properties of the soils, are presented in Figure 11. Experimentally measuring hydraulic conductivity curves can be a time-consuming and expensive effort. Therefore, methods for estimating hydraulic conductivity based on SWCCs and the saturated permeability of the soil have provided reasonable results in both scientific research and practical site applications [53]. In this study, the van Genuchten-Muallem [37; 54] method was selected to define the hydraulic conductivity of the soils in the numerical model.  
Figure 11: Calibrated SWCCs and HCFs of the soils.  
Figure 10: Model geometry and boundary conditions.  
Calibrated SWCC data were utilized to derive the curves, as illustrated in Figure 11. Moreover, the permeability of the bottom layer used in the analyses was reduced to \(4\times 10^{-8}\) m/s, below the measured value. This adjustment accounted for the double compaction applied to the bottom layer. This assumption was based on the percolation observed during the gravel test.  
The numerical analysis illustrates the variation in volumetric water content across the entire depth of the soil column. Figure 12 represents this variation over time, derived through back analysis for each test. Additionally, the experimental test results are integrated into Figure 10 for comparison.  
Given the calibration of soil water characteristic curves (SWCCs) and Hydraulic Conductivity Functions (HCFs) for each soil layer, the changes in the volumetric water content obtained from the numerical analysis align remarkably well with the experimental results. Breakthrough occurs almost simultaneously with the laboratory tests. The trend in the volumetric water content changes calculated from the numerical analysis mirrors that observed in the laboratory test results.  
While experimental measurements offer limited data points along the column, their strategic placement in areas demonstrating significant changes ensures an accurate representation of the soil column. Notably, the findings from the numerical study correspond with the observed low water retention capacities in the experimental section.
## 4 Prototype Experiment and Results
### Motor Force Test  
To verify the model and to test the force characteristic of the proposed planar motor, a prototype motor was manufactured. The prototype parameters are given in Table 4. Figure 12 shows the planar motor force experimental system.  
Figure 13 shows the force characteristics versus current. Both of the forces in the \(x\) and \(y\) directions increase linearly with the increase of the current. The measured forces agree well with the analytical model. Under the rated current \(I\) = 1.8 A, the measured forces in the \(x\) and \(y\) directions are 52.3 N and 52.9 N, respectively. The corresponding force coefficients are 29.06 N/A and 29.39 N/A, respectively. The force coefficient difference due to the directions is only 1.1%. Therefore, the force characteristics of the proposed planar motor reflect a good symmetry in the \(x\) and \(y\) directions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Symbol** & **Value** & **Unit** \\ \hline Magnet type & - & N38 & - \\ Magnet thickness & \(h_{M}\) & 5.5 & mm \\ Pole pitch & \(\tau\) & 54 & mm \\ Total air-gap length & \(h\) & 13 & mm \\ Main coefficient 1 & \(\lambda\) & 4.15 & - \\ Main coefficient 2 & \(\gamma\) & 0.42 & - \\ Pole-arc coefficient & \(\alpha_{i}\) & 0.515 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: The prototype parameters.  
Figure 12: Planar motor force experimental system. (DC: Direct Current).
## 6 Challenges in Linking Agricultural Tickets with Parcel Information  
One of the key challenges in the integration of agricultural ticket data with the corresponding parcel information lies in the incomplete or inconsistent data provided by farmers. While the tickets contain essential details like the harvest date, weight, and quality of the crops, they often lack the necessary codes to directly link them to the geospatial data of the parcels.  
In many cases, the ticket data provided by farmers are incomplete or formatted in a way that makes it difficult to automatically associate it with the correct parcel. This gap in data can result from farmers using different formats or having varying levels of record-keeping practices, which complicates the process of linking tickets to specific parcels.  
Another significant challenge arises from the difficulty in obtaining parcel information. While some basic parcel data can be accessed through public services such as the SIGPAC (System for Agricultural Parcel Information), much of the more detailed cadastral data, particularly in relation to land ownership and specific land use, require specialized access.  
In this study, obtaining this detailed information involved submitting formal requests to the Consejeria de Agricultura (Ministry of Agriculture), which can be time-consuming and may not always guarantee timely responses or full access to the necessary datasets. The process of obtaining this detailed information is complex, as it often involves navigating bureaucratic procedures and fulfilling specific regulatory requirements. However, thanks to the collaboration with stakeholders, the information provided was of high quality, which facilitated the integration process and improved the overall accuracy of the data.
# Impact of Temperature on the Moisture Buffering Performance of Palm and Sunflower Concretes
## 4 Numerical Study and Discussions
### Impact of the Moisture Buffering Capacity on Indoor Relative Humidity of an Office Room  
#### 4.2.1 Description of Studied Case and Simulation Conditions  
In the previous sections, it was shown that the moisture buffering capacity is a property which depends on several factors such as porosity and temperature. The indoor relative humidity is one of most important factors to consider for evaluating indoor air quality and hygothermal comfort [23]. Low and high indoor humidity (lower 40% or upper 60%) promotes the dehydration of materials and people (fissure and health problem), the development of molds and the survival of viruses and bacteria and increases the concentrations of polluting particles [32, 50]. Therefore, to meet the hygothermal comfort conditions, it is interesting to elucidate the impact of the moisture buffering capacity on the indoor relative humidity. This section aims is to study the effect of the moisture buffering capacity at different temperatures of palm and sunflower concretes on the indoor relative humidity of an office room. In addition, the impact of the MBV will be assessed by a sensitivity study.  
In this section, the impact of the MBV on the hygric performance was investigated by considering an office room of 55 m\({}^{3}\) (4 m \(\times\) 5.5 m \(\times\) 2.5 m), depicted in Figure 10. The effective capacitance model presented in Section 2.2. was used [44]. An effective surface area of 37.5 m\({}^{2}\) is considered as the reference case (the roof and the floor are considered impermeable to water vapor; therefore, they are without moisture buffering capacity). The outdoor relative humidity is 50%. The office room is occupied from 9:00 a.m. to 17:00 p.m. (8 h a day) by two persons, and a ventilation rate of 0.65 ACH is considered. The values of the moisture buffering capacity obtained by the simulation at different temperatures in the previous section are used to perform the simulations.  
Figure 9: Variation in the MBV as function of temperature.
# Improved Branch-and-Bound Antenna Selection Algorithm for Massive MIMO
## 3 Efficient Antenna Selection via Enhanced Branch-and-Bound Optimization  
This paper considers a large-scale MIMO system equipped with \(N_{t}\) transmit antennas and \(N_{r}\) receive antennas, which can be conceptualized as a communication system comprising a base station with \(N_{r}\) receive antennas and \(N_{t}\) users, as illustrated in Figure 1. The study focuses on receive-side antenna selection. It is assumed that the channel undergoes independent and identically distributed (i.i.d.) small-scale Rayleigh flat fading, with large-scale fading effects such as path loss being eliminated. Channel State Information (CSI) is exclusively available at the receiver. The transmitted signal components follow independent Gaussian distributions with equal power allocation. The received signal is defined as:  
\[\mathbf{y}=\sqrt{\rho}\mathbf{H}\mathbf{x}+\mathbf{\mu}, \tag{3}\]  
where \(\mathbf{H}\in\mathbb{C}^{N_{r}\times N_{t}}\) is a complex Gaussian random variable whose mean is 0 and variance is \(1\); \(\mathbf{x}\in\mathbb{C}^{N_{t}\times 1}\) is a transmitted signal vector; \(\mathbf{\mu}\) is an additive complex Gaussian noise vector following \(\mathbf{\mu}\in\text{CN}(0,\,\mathbf{I}_{N_{t}})\); \(\rho\) is a signal to noise ratio (SNR). In this paper, we consider \(N_{t}<N_{r}\); the channel capacity in the MIMO system is defined as [35]:  
\[C=\log_{2}\text{det}(\mathbf{I}_{N_{t}}+\frac{\rho}{N_{t}}\mathbf{H}^{\text{H} }\mathbf{H}), \tag{4}\]  
where \((\cdot)^{\text{H}}\) denotes the Hermitian transpose. We investigate the antenna selection problem where \(d\) antennas are selected from an original array of \(N_{r}\) receive antennas (\(d<N_{r}\)). Let \(\mathbf{H}_{s}\in\mathbb{C}^{d\times N_{t}}\) denote the resultant channel submatrix obtained by selecting the corresponding \(d\) rows from the original channel matrix \(\mathbf{H}\); then, the channel capacity of the antenna subset obtained after antenna selection is as follows:  
\[C^{*}=\log_{2}\text{det}(\mathbf{I}_{N_{t}}+\frac{\rho}{N_{t}}\mathbf{H}_{s}^ {\text{H}}\mathbf{H}_{s}). \tag{5}\]  
Figure 1: Massive MIMO system.  
Therefore, antenna selection can be translated into the following optimization problems:  
\[\mathbf{H}_{s}^{opt}=\arg\max_{\mathbf{H}_{s}\in\mathrm{A}_{s}}\ \log_{2}\det \left(\mathbf{I}_{N_{t}}+\frac{\rho}{N_{t}}\mathbf{H}_{s}^{H}\mathbf{H}_{s} \right), \tag{6}\]  
\(\mathrm{A}_{s}\) is the set of matrices corresponding to all possible antenna subsets.
## 3 Related Work
To support several mobility and real time applications, we have introduced BN-MAC protocol that reduces energy consumption and improves scalability. BN-MAC also controls the congestion based on LDSNS and energy aware-routing protocol (EAP) [31] to maximize the throughput, reduce the latency and prolongs the network lifetime.
# An Experimental Study of Industrial Site and Shaft Pillar Mining at Jinggezhuang Coal Mine
## 6 Prediction of Ground Movement
### Subsidence and Deformation of Surface Buildings  
The new design considered the safety of the shaft. However, the surface buildings were damaged because of excavation. Therefore, it was necessary to predict the deformation of the surface buildings. There are several buildings in the industrial square (the images of a few buildings are shown in Figure 12) and a few of them are deformation sensitive. These buildings are key production facilities and should be repaired regularly if they are damaged owing to ground deformation. The positions of these buildings are shown in Figure 13. In order to evaluate the impact of mining on buildings, we adopt the evaluation standard provided by the Coal Industry Bureau of China (CIB), which is a deformation damage evaluation index based on the statistical summary of China's masonry structures. It provides a reference basis for evaluating the damage of surface buildings caused by mining subsidence. The reference criterion is provided in Table 3. As the index shown  
\begin{table}
\begin{tabular}{c c c c c} \hline \(W\) & \(i\) & \(K\) & \(U\) & \(\varepsilon\) \\
**mm** & **mm/m** & **mm/m\({}^{2}\)** & **mm** & **mm/m** \\ \hline
7419 & 40 & \(-\)0.465\(\sim\)0.273 & 3620 & \(-\)32.196\(\sim\)17.516 \\ \hline \end{tabular}
\end{table}
Table 2: Maximum deformation of ground.  
Figure 11: Subsidence contours and displacement vectors.  
in Table 3, building damage is divided into four levels according to surface deformation, inclination, and curvature. Among which, rank I and II are the small deformation influence level and III, IV grades are the serious ones.  
Figure 12: Images of a few deformation sensitive buildings. (**a**) Main Shaft. (**b**) Auxiliary Shaft. (**c**) Air Shaft. (**d**) Office Building. (**e**) Community Building. (**f**) Water Tower.  
Figure 13: Deformation level partition map. AS: Air Shaft, Aus: Auxiliary Shaft, CB: Community Building, FD: Fire Department, FR: Fan Room, GST: Gangue Separation Tower, HT: Hostel HR1: Hoister Room of Main Shaft, HR2: Hoister Room of Auxiliary Shaft, MS: Main Shaft, and WT: Water Tower.  
As shown by the results in Figures 11 and 13 and Table 4, some of the buildings and facilities will be damaged by the surface deformation. However, the office building (OB), main shaft (MS), auxiliary shaft (Aus), air shaft (AS), and water tower (WT) are outside the scope of influence. Those important buildings or facilities can ensure that the production of the coal mine is not affected. The influenced buildings are listed in Table 4 and these can be used as the design accordance for maintenance. However, other buildings at the engineering site should also be considered in the maintenance plan.
## 3 Cascaded Controller and ISTSMC for the VTOL System
### Cascaded Control Algorithm  
Figure 3 shows the cascaded control strategy based on PID and PI control for the VTOL system to track the \(I_{m}\) to \(I_{ref}\) in the inner loop, referred to as a current control loop. This loop takes the measured value \(I_{m}\) applied to the VTOL plant to the reference value \(I_{ref}\). The DC motor operates the propeller assembly to attain the desired altitude. The outer loop is based on a PID controller that tracks the \(\theta_{m}\) at the output side to the reference value \(\theta_{ref}\) in terms of the pitch (deg) of the aluminum arm of the VTOL body along the horizontal axis.  
The PID control topology in [29; 30] can be redefined for the VTOL system as  
\[u=k_{p}(\theta_{ref}-\theta_{m}(t))+k_{i}\int{(\theta_{ref}-\theta_{m}(t))dt}+ k_{v}\frac{d}{dt}(\theta_{ref}-\theta_{m}(t)) \tag{14}\]  
Applying the Laplace transform to Equation (14),  
\[U=k_{p}\theta_{ref}-k_{p}\theta_{m}(s)+\frac{k_{i}}{s}\theta_{ref}-\frac{k_{i} }{s}\theta_{m}(s)-k_{v}s\theta_{m}(s) \tag{15}\]  
Substituting the value of \(I_{m}(s)\) as an input to the plant in Equation (15) from Equation (4), we have  
\[\theta_{m}(s)J\bigg{(}s^{2}+\frac{B}{J}s+\frac{K}{J}\bigg{)}\frac{1}{K_{t}}=k _{p}\theta_{ref}-k_{p}\theta_{m}(s)+\frac{k_{i}}{s}\theta_{ref}-\frac{k_{i}}{ s}\theta_{m}(s)-k_{v}s\theta_{m}(s) \tag{16}\]  
Figure 3: Complete functional block diagram of closed-loop VTOL system based on cascaded loop controller.  
Rearranging Equation (16), we obtain  
\[\theta_{m}(s)J\left(s^{2}+\frac{B}{J}s+\frac{K}{J}\right)\frac{1}{K_{t}}+k_{p} \theta_{m}(s)+\frac{k_{i}}{s}\theta_{m}(s)+k_{v}s\theta_{m}(s){=}k_{p}\theta_{ ref}+\frac{k_{i}}{s}\theta_{ref} \tag{17}\]  
Finally, simplifying Equation (17), we have  
\[\frac{\theta_{m}(s)}{\theta_{ref}}=\frac{K_{t}\big{[}k_{p}s+k_{i}\big{]}}{ \left[s^{3}+s^{2}\frac{1}{J}(B{+}K_{t}k_{v})+s\frac{1}{J}(K+k_{p}K_{t})+\frac{ 1}{J}K_{t}k_{i}\right]} \tag{18}\]  
The roots of the third-order characteristic polynomial equation can be defined as \(p_{o}\) percent overshoot:  
\[(s^{2}+2\zeta\omega_{n}s+\omega_{n}^{2})(s+p_{o})=s^{3}+(2\zeta\omega_{n}{+}p_ {o})s^{2}+(\omega_{n}^{2}+2\zeta\omega_{n}p_{o})s+\omega_{n}^{2}p_{o} \tag{19}\]  
The gains of the PID controller can be derived by comparing Equation (18) with Equation (19) as  
\[k_{p}=\frac{J\omega_{n}^{2}+2J\zeta\omega_{n}p_{o}-K}{K_{t}},\;k_{i}=\frac{J \omega_{n}^{2}p_{o}}{K_{t}},\;k_{v}=\frac{J(2\zeta\omega_{n}{+}p_{o})-B}{K_{t}} \tag{20}\]  
Hence, the following gain values of the PID controller are obtained by substituting various parameters in Equation (20):  
\[k_{p}=0.90,k_{i}=0.13,\;k_{v}=0.12 \tag{21}\]