esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Prediction Results of Mining Surface Subsidence of Northern Pei County for Reusing Land Resources
### The Influence of Geological and Mining Conditions on Prediction Parameters
#### 3.1.8 Roof Management Methods  
The roof management methods can be usually divided into caving mining, strip mining and backfilling mining. The above research of the influence law of geological and mining conditions on the prediction parameters are for the caving approach.  
The basic means of strip mining is to allocate the areas to be mined into the formal stripped shape. The mining operation exploits one strip at a time and keeps one strip. The remaining strip coal pillars can support the load of the overlying strata and cause slight surface movement and deformation,thus realizing the goal of recovering some coal resources and controlling surface subsidence. Compared with caving mining, the mining degree of backfilling mining became smaller. Therefore, the subsidence coefficient and the horizontal movement coefficient of backfilling mining are smaller than the parameters of the caving mining. The correction formula of the subsidence coefficient and the horizontal movement coefficient of strip mining can be expressed By Equations (15) and (16) [28]:  
\[\frac{\eta_{s}}{\eta_{c}}=0.2663e^{-0.5753M}\times\left(\frac{b}{a+b}\right)^{ 2.6887}\times\ln\left(\frac{bH}{a}\right)+0.0336 \tag{15}\]  
\[\frac{b_{s}}{b_{c}}=-0.0002\frac{H(a+b)}{b}+0.8786 \tag{16}\]  
where:  
\(a\) is the retaining width of the pillar, m;  
\(b\) is the mining width of the pillar, m;  
\(H\) is the mining depth, m:  
\(M\) is the mining thickness, m;  
\(\eta_{s}\) is the subsidence coefficient of the strip mining;  
\(\eta_{c}\) is the subsidence coefficient of the caving mining;  
\(b_{s}\) is the horizontal movement coefficient of the strip mining;  
\(b_{c}\) is the horizontal movement coefficient of the caving mining.  
Compared with the caving mining, the surface movement and deformation of the backfilling mining is relatively moderate and leads to the relatively smaller tangent of the major influence angle. The correction formula of the tangent of the major influence angle for strip mining can be expressed by Equation (17) [28]:  
\[tan\beta_{s}=0.7847e^{-0.0012 PH}tan\beta_{c} \tag{17}\]  
where: \(tan\beta_{s}\) is the tangent of the major influence angle of the strip mining; \(P\) is the comprehensive evaluation coefficient of the overburden strata; \(H\) is the mining depth, m; \(tan\beta_{c}\) is the tangent of major influence angle of the caving mining.
## 6 Cost and Benefit Analysis
### Energy Consumption Cost  
Regarding energy consumption cost, the system optimizes motor power distribution, significantly reducing energy consumption during low-load operations. Experimental data indicate that after power redistribution, the output efficiency of each motor increased by approximately 1.48%. Specifically, before energy savings, each motor's output power was 9.318 kW, with a total input power of 55.907 kW. After energy savings, each motor's output power was 9.391 kW, with a total input power of 54.010 kW. The reduction in energy consumption is evident not only in daily operations but also accumulates substantial savings over extended use. For instance, considering a 3 km-long belt conveyor with 100 motors running 4 h per day and 200 days per year, the annual energy savings would be approximately 14,720 kWh. With an electricity cost of 0.5 RMB per kWh, the annual savings would amount to about 7360 yuan. In the long term, the saved electricity costs will significantly offset the initial installation and maintenance expenses, further enhancing the system's economic viability.
## 3 Kinematic Analysis
### Velocity Analysis  
The end effector velocity can be obtained from mechanical joint velocities through the Jacobian matrix, as in Equation (9):  
\[\left[\begin{array}{c}\mathbf{v}\\ \mathbf{\omega}\end{array}\right]=\mathbf{J}(\mathbf{q})\cdot\dot{\mathbf{q}}=\left[\begin{array} []{ccc}J_{11}&\ldots&J_{li}\\ J_{a1}&J_{ai}\end{array}\right]\dot{\mathbf{q}}, \tag{9}\]  
where \(\dot{\mathbf{q}}=\left[\dot{a}_{0},\dot{\theta}_{1},\dot{\theta}_{2},-\dot{\theta }_{2},\dot{\theta}_{3}\right]^{\mathrm{T}}\) represents the joint velocities; \(\mathbf{v}\) and \(\mathbf{\omega}\) are the linear and angular velocities of the end effector; \(\dot{J}_{li}\) (linear) and \(\mathbf{J}_{ai}\) (angular) represent the velocity connections between the joint \(i\) and the end effector. The \(\mathbf{J}_{li}\) and \(\mathbf{J}_{ai}\) could be calculated from Equation (10):  
\[\left[\begin{array}{c}\mathbf{J}_{li}\\ \mathbf{J}_{ai}\end{array}\right]\dot{q}_{i}=\left[\begin{array}{cc}R_{i}^{ \mathrm{T}}&-R_{i}^{\mathrm{T}}S(\mathbf{p}_{i})\\ 0&R_{i}^{\mathrm{T}}\end{array}\right]\left[\begin{array}{c}\mathbf{v}_{i}\\ \mathbf{\omega}_{i}\end{array}\right], \tag{10}\]  
where \(R_{i}^{\mathrm{T}}\) is the transpose of the rotation matrix in \({}_{6}^{i}T\); \(\mathbf{p}_{i}\) is the position vector in \({}_{6}^{i}T\); \(\mathbf{v}_{i}\) and \(\mathbf{\omega}_{i}\) are linear and angular velocities in frame \(i\); \(S(\mathbf{p}_{i})\) is the skew-symmetric matrix related to \(\mathbf{p}_{i}\), and it is shown as:  
\[S(\mathbf{p}_{i})=\left[\begin{array}{ccc}0&-p_{z}&p_{y}\\ p_{z}&0&-p_{x}\\ -p_{y}&p_{x}&0\end{array}\right]. \tag{11}\]  
Figure 6: Parallel mechanism model (**a**) and parallelogram mechanism model (**b**).  
As \(\dot{\mu}_{3}\) and \(\dot{q}_{4}\) are the same variables for \(\dot{\theta}_{2}\), \(\left[\begin{array}{cc}I_{\beta}&J_{a3}\end{array}\right]^{\mathrm{T}}\) and \(\left[\begin{array}{cc}J_{l4}&J_{a4}\end{array}\right]^{\mathrm{T}}\) could be combined together. Finally, _J(q)_ is calculated as:  
\[\mathbf{J(q)}=\left[\begin{array}{cccc}cos(\theta_{1}+\theta_{3})&-l_{2}cos\theta _{2}sin\theta_{3}-l_{1}sin\theta_{3}&-l_{2}sin\theta_{2}cos\theta_{3}&0\\ sin(\theta_{1}+\theta_{3})&l_{2}cos\theta_{2}cos\theta_{3}+l_{1}cos\theta_{3} &-l_{2}sin\theta_{2}sin\theta_{3}&0\\ 0&0&l_{2}cos\theta_{2}&0\\ 0&0&0&0\\ 0&0&0&0\\ 0&1&0&1\end{array}\right]. \tag{12}\]
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Cluster Formation Phase  
This phase begins with advertisement messages sent out by the newly elected CHs to announce their new status and ends with the join message sent by each node to its optimum CH. The cluster formation schemes are categorized into event-driven and optimal clustering schemes. Optimal clustering schemes strive to either manipulate the cluster size based on the application type and data transmission, or to balance and reduce energy usage by considering factors such as relay traffic, residual energy, and data correlation. Event-driven schemes strive to extend the network lifespan by removing dispensable clustering from the network and triggering the cluster formation stage only when necessary. Figure 7 illustrates the cluster formation process.
## 3 Methodology
### Closed-Form Solution for Replenishment Quantity  
Given a set of replenishment routes, the lead time of each demand location is an exogenous variable for a sub-problem. In Section 3.1, a linear programming model for sub-problems is proposed. In this section, we further derive a closed-form solution for replenishment quantity. When \(\bar{d}=\bar{d}_{t}\) and \(\hat{d}=\hat{d}_{t}\) for any \(t=\{1,\ldots,H\}\), the following proposition derives an approximate optimal solution under the continuous time condition.  
**Proposition 1**.: _For any demand under a box uncertainty set \(U\), let \(\lambda=(s+h)\bar{d}_{i}+(s-h)\hat{d}_{i}\) the robust optimal replenishment quantity satisfy:_  
1. _For any demand location_ \(i\) _replenished by a single replenishment mode, the optimal replenishment quantity is_ \[q_{i}=\begin{cases}\dfrac{(sH+hl)}{(s+h)^{2}}\lambda-I_{i0}&I_{i0}<\dfrac{ \lambda}{s+h}H\\ 0&I_{i0}\geq\dfrac{\lambda}{s+h}H\end{cases}\] (26)
2. _For any demand location_ \(i\) _replenished by two replenishment modes, the faster and slower optimal replenishment quantities are_ \[q_{i}^{f}=\begin{cases}\dfrac{(sI^{s}+hl^{f})}{(s+h)^{2}}\lambda-I_{i0}&I_{i0} <\dfrac{\lambda}{s+h}I^{s}\\ 0&I_{i0}\geq\dfrac{\lambda}{s+h}I^{s}\end{cases}\] (27) _and_ \[q_{i}^{s}=\begin{cases}\dfrac{(sH+hl^{s})}{(s+h)^{2}}\lambda-I_{i0}-q_{i}^{f}&I _{i0}+q_{i}^{f}<\dfrac{\lambda}{s+h}H\\ 0&I_{i0}+q_{i}^{f}\geq\dfrac{\lambda}{s+h}H\end{cases}\] (28)  
Appendix A is the proof of Proposition 1.
## 4 Experimentation and Results
### Comparison of Results  
The findings were compared to those that were acquired using the most cutting-edge approaches reported in the academic literature. Table 3 overviews the many methods that may be applied. As a direct consequence of these findings, the CNN (Luna Challenge) in 2016 [41] and [PERSON] et al. (2017) [42] both accomplished an accuracy rate of 82.7%. [PERSON] et al. (2019) [43] obtained an accuracy of 78.4% using the same dataset for their CNN experiment, but they did so using a different technique. The accuracy of these discoveries was compared to those obtained by a matching algorithm (CNN) on the same dataset, which was found to have significantly improved.
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 2 Data Consumers in an SPS
### On-Site Data Consumers  
On-site data consumers are IoT devices that work with the data generated from devices that are in its proximity. Figure 2a-d depict some of the IoT devices that are employed in individual smart parking lots. Smart LED bulbs are used to provide visual clues for drivers in a closed parking system that has less day light visibility. These smart LEDs can be connected to other IoT devices through WiFi or BlueTooth technology. Similarly,smart alarms devices are also available and could be integrated to the central parking management system. The availability of systems on chips (SoC) supporting multiple radios in a single chip equips an IoT device to switch between different types of communication channels for device-to-device interaction. For instance, Qualcomm QCA4020 SoC provides intelligent multi-radio connectivity with WiFi, BlueTooth and 802.15.4 support [9].  
The ticketing booths could be a simple hand-held device with ticket or receipt-printing capability. They would need occupancy duration and timings for calculating relevant parking charges. They could also be a complex system, complete with an automated toll gate to allow passage for vehicles after verification. Figure 3 shows a simple LCD screen display showing the aisle numbers and the respective numbers of lots that are vacant in them. Such a display screen, placed at the entrances of different levels, help users in a multi-level parking system. It could also be a complex system complete with a map in a very large parking lot.  
Figure 3: A display screen placed in a smart parking lot, showing parking availability.  
Figure 2: Examples of on-site data consumers. (**a**) Smart bulb. (**b**) Buzzer for alarm. (**c**) Ticketing booth. (**d**) Display screen.
## 3 Simulation Results and Discussion
### Measurements of Soil Plug  
Figure 9a,b show the _PLR_ and _IFR_, respectively, obtained from the DEM simulation along with the experimental results.  
The significant trends observed in the numerical analysis results are as follows: the _IFR_ started at 1 at the beginning of penetration and decreased to 0 when the penetration depth reaches 516 mm, accounting for 60-70% of total penetration depth. As the impact driving progressed further, the _IFR_ increased again, approaching approximately 0.4 in its final state. This pattern indicates that initially, the pile was fully coring. As penetration continued, gradual clogging began to occur, reaching a fully plugged state at a depth of 516 mm. Then, the plugging effect dissipated, and soil from the base of the pile began to intrude, resulting in a partially plugged state. This trend is consistent with the previously analyzed trends of soil plug length [32,44]. Particularly, the determination of the fully plugged state could be clearly made through the _IFR_. By comparing the length indices from the simulation results with values from the actual model pile test, validation of the soil-pile interaction was successfully completed. This process also effectively verifies the DEM model with the raw value of the plug length.
## 4 Experiment and Results
### Performance Evaluation  
#### 4.2.1 Stock Prediction Evaluation  
To evaluate the prediction error rates and model performance, the mean absolute error (MAE) and mean squared error (MSE) were used to measure the difference between the predicted and practical data. MAE and MSE was calculated as follows:  
\[MAE=\frac{\sum_{i=1}^{T}\left|y_{i}-y_{i}^{\prime}\right|}{T}, \tag{18}\]  
\[MSE=\frac{\sum_{i=1}^{T}\left(y_{i}-y_{i}^{\prime}\right)^{2}}{T}, \tag{19}\]  
where \(Y=\left(y_{1},y_{2},\ldots,\)\(y_{T}\right)\) is a vector of actual observations, \(Y^{\prime}=\left(y_{1}^{\prime},\)\(y_{2}^{\prime},\)\(\ldots,\)\(y_{T}^{\prime}\right)\) is a vector of predicted values, and \(T\) is the number of prediction time horizons.  
At first, we conducted two variant types of RNN LSTM and GRU models to predict the stock price. Machine learning models such as LR and SVR were also employed to compare the effectiveness of the LSTM prediction model.
# Design and Numerical Study of Induction-Heating Graphitization Furnace Based on Graphene Coils
## 2 Numerical Model
### Mathematical Model
#### 2.2.3 Equation of Heat Transfer  
This study assumes that the induction furnace is a solid heat transfer medium and neglects the effect of the gas flow on the temperature distribution. Heat is transferred through conduction, as described by the partial differential equation for heat transfer:  
\[\rho\mathrm{C}\frac{\partial\mathrm{T}}{\partial\mathrm{t}}=\frac{\partial}{ \partial\mathrm{x}}\bigg{(}\lambda\frac{\partial\mathrm{T}}{\partial\mathrm{x} }\bigg{)}+\frac{\partial}{\partial\mathrm{y}}\bigg{(}\lambda\frac{\partial \mathrm{T}}{\partial\mathrm{y}}\bigg{)}+\frac{\partial}{\partial\mathrm{z}} \bigg{(}\lambda\frac{\partial\mathrm{T}}{\partial\mathrm{z}}\bigg{)}+\mathrm{Q} \tag{10}\]  
The given equation shows the expression for \(\mathrm{Q}\), which is the thermopower. The \(\mathrm{Q}\) is mainly composed of volumetric thermopower generated by the induced eddy currents and losses on the induction coils. The equation includes the following variables: \(\rho\) for the density in \(\mathrm{kg}/\mathrm{m}^{3}\); \(\mathrm{c}\) for the specific heat in \(\mathrm{J}/(\mathrm{kg}\cdot\mathrm{K})\); \(\mathrm{\lambda}\) for the thermal conductivity in \(\mathrm{W}/(\mathrm{m}\cdot\mathrm{K})\); \(\mathrm{xyz}\) for the right-angle coordinate in meters; \(\mathrm{T}\) for the absolute temperature in Kelvin; and \(\mathrm{\tau}\) for the time in seconds. The expression for the \(\mathrm{Q}\) can be written in terms of the current density and conductivity:  
\[\mathrm{Q}=\frac{|\mathrm{J}|^{2}}{\sigma} \tag{11}\]  
To solve the governing equations, boundary conditions must be defined. In particular, at the symmetry axis, the radial gradients of all the dependent variables should be zero. Furthermore, at the outer boundary of the simulation domain, the magnetic vector potential should be zero, and the temperature should be the same as the ambient temperature. Special attention should be paid to the fact that the temperature of the coil and the furnace wall should also be set to the ambient temperature due to the effective cooling by water. If the temperature of the furnace surface is not kept constant, the thermal equilibrium conditions will change:  
\[-\mathrm{n}\cdot\mathrm{k_{f}}\
abla\mathrm{TF}-\mathrm{q_{rl}}+\mathrm{n} \cdot\mathrm{k_{amb}}\
abla\mathrm{T_{amb}}=0 \tag{12}\]  
The notations 'f' and 'amb' are used to denote 'furnace' and 'ambient', respectively. In addition, the term '\(\mathrm{qrl}\)' is used to denote the amount of heat lost by radiation from the surface.
## 2 STAR Algorithms to Convert Carrier Phase to Excess Phase and Bending Angle Profiles
### Flow Chart for Converting COSMIC-2 L0 Observations to L2 Data Products  
The flow chart for converting COSMIC-2 L0 observations to L2 data products at STAR is shown in Figure 2 (also see Figure 4 in [35]). Using a reading script provided by the UCAR CDAAC, we can decode the COSMIC-2 L0 data into L1a files, including the formatted carrier phase and pseudo-range measurements at a different rate for the POD antenna and occultation (OCC) antennas (Figure 2).  
Table 1 summarizes the STAR RO L0-L2 implementations and specifies the input data and output products for each processing step (highlighted in Figure 2 as an orange box line). The input file format description can be found on the UCAR CDAAC website [39], and is not further detailed here.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Processing Step** & **Input Data** & **Main Output Data** & **Processing Methods/Libraries** \\ \hline Decoding L0 Data Set & COSMIC-2 L0 Datasets & COSMIC-2 L1a Dataset (opnGns and podCrx) & UCAR’s L0 to L1a Perl script \\ \hline Extract RO events and pair with POD observations for reference GNSS & OpnGns datasets podCrx datasets & Separate RO events and associated reference POD observation & opnGnss decoder (Perl) podCrx/RINEX reader, Matlab/IDL/bash scripts \\ \hline The LEO Precise Orbit and receiver Clock estimation & COSMIC-2 Attitude Files; COSMIC-2 podCrx POD observations; CODE Final Orbit Products & COSMIC-2 POD (opnGns) and podCrx/RINEX reader, used in this paper) and Receiver Clock bias at 2 s intervals (leoCIK) & Bernese POD software Bash scripts for data preparation \\ \hline GNSS/LEO Satellite Antenna Position (phase center), Geometric range calculation & CODE Final Orbit Products COSMIC-2 OpnGns and podCRX COSMIC-2 LeoOrb (UCAR), leoCIK & GNSS Satellite Attitude Solution Model, GNSS transmitter position instrument ECEF/ECI conversion, Orbital polynomial interpolation, Signal propagation time calculation \\ \hline \hline \end{tabular}
\end{table}
Table 1: STAR implementation of the RO processing chain.  
Figure 2: STAR RO COSMIC-2 processing procedure. Different data product levels (level 0 to level 2) are also shown using the UCAR naming convention.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
###### Abstract  
Dual connectivity (DC) was first proposed in 3 GPP Release 12 which allows one piece of user equipment (UE) to connect to two base stations in heterogeneous networks (HetNet) at the same time, to increase the flexibility of resource utilization. DC has been further extended to multiple connectivity in 5G New Radio (NR). On the other hand, different UE tends to have different bandwidth requirements. Thus, in DC, one of the challenging issues is how to integrate resources from two base stations to enhance the quality of service (QoS) as well as the data transfer rate of each UE. In this paper, we proposed novel resource management mechanisms to improve the QoS of UE in the co-channel dual connectivity network. In terms of resource allocation, we designed the Max-Min Threshold Scheduler (MTS) which, in principle, allocates a resource block to the UE with the best channel quality while considering the issues of intercell resource allocation and the QoS requirement of each UE. In order to balance the load of different cells, we designed a novel cell selection scheme based on the HetNet Congestion Indicator (HCI) which considers not only the signal quality of UE but also the remaining resources of each base station. To improve the QoS of cell edge UE, cell range expansion (CRE) and the Almost Blank Subframe (ABS) were proposed in 3 GPP. In this paper, based on Q-learning, we designed an adaptive mechanism which dynamically adjusts the ABS ratio according to the network condition to improve resource utilization. Our simulation results showed that our MTS scheduler was able to achieve a 31.44% higher data rate than the Proportional Fairness Scheduler; our HCI cell selection scheme yielded a 2.98% higher data rate than the signal-to-interference plus noise ratio (SINR) cell selection scheme; the QoS satisfaction rate of our Q-learning dynamic ABS scheme was 4.06% higher than that of the Static ABS scheme. Finally, for the cell edge users who often suffer poor data transfer rate, by integrating the mechanisms of DC, CRE, and ABS, our experimental results showed that the QoS satisfaction ratio of cell edge UEs could be improved by 10.76% as compared to the single connectivity and no ABS situation.  
heterogeneous network; dual connectivity; quality of service; resource allocation; cell selection +
Footnote †: journal: Applied Soft Soft
# Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency
## 3 AI-Based Approaches in Building Energy Management
### Commonly Used Machine Learning Methods
#### 3.2.12 Hybrid Models  
Hybrid models use multiple machine-learning techniques. Data pre-processing in combination with optimization methods are used to improve the prediction accuracy [97]. Hybrid models are being applied to the day-ahead prediction of power generation in the hybrid renewable energy systems and micro-grids [98] and the prediction of energy consumption in buildings [99]. Table 2 presents a comparison of machine learning models used in the smart building system. This comparison was made by considering the complexity, user-friendliness, and speed of the models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ML Models** & **Area of Application** & \multicolumn{2}{c}{**Domain of Application in BMS**} & \multicolumn{2}{c}{**Advantages**} & \multicolumn{1}{c}{**Disadvantages**} \\ \hline ANN & \begin{tabular}{c} Modelling, forecasting, \\ and curve-fitting of \\ non-linear processes \\ \end{tabular}  & \ \begin{tabular}{c} HVAC energy consumption \\ modelling \\ \end{tabular}  &
\begin{tabular}{c} • High accuracy \\ •  
Previous studies have focused on comparing data-driven models with physics-based models, comparing machine learning with statistical techniques, evaluating group methods, and analyzing machine learning, with the objective to forecast building energy use. [PERSON] and [PERSON] in 2019 provided a comprehensive review of the studies published since the year 2000 that have applied artificial neural networks for forecasting building energy use and demand; they particularly focused on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations [100]. [PERSON] et al. in 2012 published a review focusing on the main approaches for energy prediction and forecasting in buildings, and they compared machine learning, statistical, and physics-based models [101]. A comparison between the machine learning-based models and other conventional methods was carried out by [102]; the comparison indicated a lack of flexibility of the current conventional methods in dealing with non-linear patterns, especially in forecasting building electrical consumption. The application of hybrid models within building energy prediction was explored by [55], who highlighted the role of ANN models and their improved performance compared with single prediction models. [PERSON] et al. in 2018 provided a review of e wide range of practical applications of ANN models that can be used in the prediction of energy loads in buildings [103]. [PERSON] et al. in 2018 provided a substantial review on the four main ML approaches, including artificial neural network,