esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees
## 6 Conclusions and Recommendations for Future Work  
In this paper, a random tree and C4.5 DT models were applied to predict underground mines' pillar stability. The models were trained and tested using the pillar stability database of underground mines collected from the USA and India. The developed models predict the pillar stability using the most important input variables such as pillar width (\(W\)), pillar height (\(H\)), pillar \(W\)/\(H\) ratio, uniaxial compressive strength, and average pillar stress. The main findings of this study are as follows:  
1. Unlike most soft computing techniques, the developed models are very simple to use and do not require exhaustive training. The proposed models present an explicit relationship between explanatory (input) and target (output) variables. Study results reveal that relationships obtained are compatible with engineering judgments and intuitions.
2. The classification accuracy of the random tree and C4.5 DT is 100% and 95.65%, respectively during the training and testing phases, which indicates that both models are efficient and reliable for practical applications.
3. Compared to the SVM model, the developed random tree model shows at par results and the random tree model application is easier due to a simple graphical outcome, while the information gained by the SVM algorithm during the training phase is implicitly processed that make it very difficult to explain well the overall structure of the network. So, it may be argued that the SVM model has little insight into the fundamental mechanism of the current problem.
4. The comparison of the developed models' performances showed that the random tree model achieved more reliable predictions than the C4.5 DT model.
5. It is evident that the proposed models are open to further development in the future and that the acquisition of more data will improve the capacity for prediction.  
For future research, it is recommended to work with a larger and more balanced pillar stability database to further evaluate the reliability of these models for the prediction of pillar stability. These models can also be refined by considering additional parameters pertaining to ground type and structural geology.  
Conceptualization M.A., X.-W.T., N.A.A.-S.; methodology M.A., X.-W.T., H.M.-A.-A.; software M.A., F.A.; validation M.A., X.-W.T., N.A.A.-S.; formal analysis M.A., F.A., A.J., H.M.A.-A.; investigation M.A., A.J., F.A.; resources H.M.A.-A., X.-W.T., N.A.A.-S.; data curation M.A., F.A.; writing--original draft preparation M.A., A.J.; writing--review and editing M.A., A.J., N.A.A.-S.; supervision X.-W.T., N.A.A.-S., H.M.A.-A.; project administration X.-W.T., N.A.A.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors acknowledge and appreciate the support provided by the King Fahd University of Petroleum and Minerals (KFUPM).  
The authors declare no conflict of interest.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 2 Materials and Methods
### Fusion Embedding Layer  
The fusion embedding layer is a cornerstone of STFEformer, designed to integrate native information with spatial-temporal features comprehensively. This layer processes the traffic data to create a multi-faceted representation. Upon undergoing processing within the fusion embedding layer, the input \(T\) time steps' traffic flow tensor is embedded into a high-dimensional representation, enhancing the model's ability to capture complex features in traffic data. Specifically, we employ a spatial-temporal embedding mechanism which includes adaptive spatial embedding for extracting the spatial dependency and temporal embedding to capture both short-term and periodic temporal features in traffic data. Additionally, we utilize a fully connected layer and temporal positional encoding to obtain native information from raw data.
## 4 Problem Formulation  
As already mentioned, the paper deals with packet throughput maximization, which in the considered network setting is equivalent to the problem of finding a TDMA frame of minimum size such that the network is capable to deliver all packets generated by sensors to all their destinations with a finite delay. This is because the packets are generatedat the beginning of each consecutive frame, thus the shorter the frame the greater the packets arrival intensity. In the paper we will maximize traffic throughput by means of this equivalence. In order to formally formulate the above characterized optimization problem we use a mixed-integer programming formulation using the following sets of variables:  
* \(h_{suc},s\in\mathcal{S},w\in\mathcal{V},c\in\mathcal{C}\), non-negative continuous, expresses the amount od data from stream \(s\) to be broadcast from node \(w\) when c-set \(c\) is applied,
* \(T_{c},c\in\mathcal{C}\), integer, denotes the number of slots in the frame in which c-set \(c\) is active. In order to ensure stable delivery (i.e., with a finite delay) of the packets to their destinations, the following constraints are imposed:  
\[\sum_{c\in\mathcal{C}(a)}h_{sb(a)c}\geq n(s)y(s,a),\quad s\in \mathcal{S},a\in\mathcal{A} \tag{2a}\] \[\sum_{s\in\mathcal{S}}h_{suc}\leq B(m(c,w))T_{c},\quad c\in \mathcal{C},w\in\mathcal{W}(c), \tag{2b}\]  
where \(\mathcal{C}(a)\) denotes the set of c-sets in \(\mathcal{C}\) containing arc \(a\), \(n(s)\) denotes data volume (expressed in Mb) generated at \(s\) at the beginning of each consecutive frame, and \(y(s,a)\) is a given parameter equals 1 if, and only if, arc \(a\) belongs to the routing tree \(B(s)\). Constraint (2a) ensures that data from a given stream will be transmitted over all arcs belonging to its routing trees within a single frame, while constraint (2b) ensures that the number of slots \(T_{c}\) is sufficient to realize all scheduled broadcasts. Note that the above constraints assume fixed (predefined) routing trees. However, if TPC and dynamic MCSs assignment are to be embedded in the frame minimization problem it is essential to optimize the routing trees as well (since adjusting the transmitting power and MCS used by a node influences the signal range, and in consequence the predefined routing trees can easily exclude some power levels or MCSs from the feasible solutions). This can be achieved by treating the parameters \(y(s,a)\) as binary variables (denoted by \(y_{sa}\)) and adding flow variables \(z_{snu},s\in\mathcal{S},w\in\mathcal{D}(s),a\in\mathcal{A}\). The flow variables corresponding to a given sensor \(s\in\mathcal{S}\) specify the paths from \(s\) to its destinations in \(\mathcal{D}(s)\): the set of arcs forming the selected path from \(s\) to \(w\in\mathcal{D}(s)\) is determined as \(\{a\in\mathcal{A}:z_{snu}=1\}\). Certainly, the flow variables depend on variables \(y_{sa},a\in\mathcal{A}\), that define the routing tree \(\mathcal{B}(s)\) (with \(A(s)=\{a\in\mathcal{A}:y_{sa}=1\}\)).
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 5 Case Studies
### Results of Local MGs  
#### 5.3.1 Rolling Optimization of ESSs  
In the proposed method, the local ESS is charged in the case of a PV generation surplus or low electricity price, and discharged in the case of a PV energy deficit or high electricity price to reduce the cost at which the MG purchases the electricity from the power grid. Figure 6 shows the dispatching results of the ESSs in the local MGs. It can be seen that the ESS charged when the internal selling price was equal to the valley price of the power grid in the period of 0:00-6:00. In the period of 7:00-9:00, as load demands increased, the local PV energy was insufficient, the internal selling price was high and discharge of the ESS occurred to reduce the cost of buying energy from the power grids in that period. During the period of 15:00-18:00, the PV generation surplus was absorbed by the ESS. During the period of 18:00-22:00, when the electricity price was high, the absorbed energy was released to reduce the cost of power consumption in the MG.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distribution Network** & **Prices (kWh/h)** & **Hours** \\ \hline \multirow{3}{*}{Selling} & Peak: 1.189 & 8:00–11:00; 13:00–16:00; 18:00–22:00 \\  & Flat: 0.738 & 7:00–8:00; 11:00–13:00; 16:00–18:00 \\  & Valley: 0.423 & 0:00–7:00; 22:00–24:00 \\ \hline \hline Buying & 0.352 & 0:00–24:00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time-of-use tariff.  
Figure 5: Comparison of internal prices and grid prices.  
In order to further analyze the benefits of ESS scheduling, the benefits of ESS charging and discharging in each period are shown in Figure 7. These benefits were calculated on the basis of the internal prices. The results show that the positive benefits of each ESS in a day were higher than the total negative benefits. The increased benefits of three MGs were CNY 130.08, CNY 133.66 and CNY 143.96, respectively. This validates the necessity and rationality of ESS scheduling in the first stage.  
Figure 6: Optimal scheduling of energy storage systems (ESSs). SOC: state of charge.  
Figure 7: Impact of ESS scheduling on the benefits of MGs.
# A Comparative Study of the Pullout Strength of Geostraps and Geogrids in Reinforced Soil
## 3 Results
### Comparison of Different Grades of Geostraps  
The pullout test for different grades of geostraps was conducted using ABAQUS software. Figure 5 shows a plot of the pullout force and the displacement of various grades of geostraps. As per the results obtained, the M65-grade geostrap exhibits 15.25 kN m\({}^{-1}\) of pullout resistance per unit length, followed by 14.5 kN m\({}^{-1}\), 13.4 kN m\({}^{-1}\), and 12.1 kN m\({}^{-1}\) for the M50, M37.5, and M25 grades, respectively. The judicial use of the desired grade of geostrap, considering the soil type and load, may cut project costs and achieve sustainability goals. The failure envelope can be effectively used to determine the pullout force per unit length for intermediate grades of geostraps.
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm
### Clutter Map CFAR Detector
#### 2.1.2 Probability of Detection  
Reference [17] provides the conditional density functions for determining whether the target is present (alternative hypothesis H\({}_{1}\): presence of useful signal and clutter) or not (null hypothesis H\({}_{0}\): presence of clutter only). The probability densities corresponding to the presence and absence of the target at the detector output are given, respectively, by the following:  
Figure 1: Division of the radar space into map cells (Cell-Map).  
Figure 2: Block diagram of the CMAP-CFAR detector.  
\[\Pr(q\mid H_{0})=\begin{cases}\Pr(q\mid H_{0})=\frac{q^{p-1}}{(p-1)!}\exp(-q)\\ \Pr(q\mid H_{1})=\frac{q^{p-1}}{(p-1)!(1+\mathrm{SNR})^{p}}\exp(-\frac{q}{1+ \mathrm{SNR}})\end{cases} \tag{3}\]  
where \(\Pr(q\mid H_{0})\) is the probability of false alarm (Pfa) and \(\Pr(q\mid H_{1})\) is the probability of detection (Pd).  
Therefore, the probability of detection, given a fixed false alarm rate, is obtained by the following formula [14]:  
\[P_{D}=\int_{0}^{\infty}\Pr(y)\int_{Th}^{\infty}\Pr(q/H_{1})dqdy \tag{4}\]  
Substituting (3) (case of hypothesis 1) into (4), we obtain the expression for the detection probability:  
\[P_{D}=\sum_{k=0}^{n-1}\prod_{n=0}^{m}{n+k-1\choose k}(Th\frac{\alpha\times(1- \alpha)^{n}}{1+\mathrm{SNR}})^{k}(1+Th\times\frac{\alpha\times(1-\alpha)^{n}} {1+\mathrm{SNR}})^{-(n+k)} \tag{5}\]
## 3 Results and Discussion
### GC-MS Analysis of Extracts
#### 3.4.3 GC/MS Analysis of the Extract from the MAE
The mechanism for these chlorination reactions may involve some free radicals [25]: normally, coal contains a small number of metals such as copper and iron; these metals can possibly induce a generation of CCl\({}_{3}\) free radicals according to Reaction (2). The reactive-CCl\({}_{3}\) free radicals will attack some of the compounds (RH) extracted in CCl\({}_{4}\) in a mode of chain propagation as described by Reactions (3) and (4). Finally, Reactions (5)-(7) are probably the chain termination reactions for the free radicals. The high percentage of C\({}_{2}\)Cl\({}_{6}\) found in the extract (about 30%, Peak 10) is strong support to this mechanism. \[M+CCl_{4}\to M^{-}+CCl_{3}+Cl^{-} \tag{2}\]  
\[RH+\cdot CCl_{3}\to R\cdot+HCCl_{3} \tag{3}\]  
\[R\cdot+CCl_{4}\to RCl+\cdot CCl_{3} \tag{4}\]  
\[CCl_{3}+CCl_{3}\to Cl_{3}C-CCl_{3} \tag{5}\]  
\[R\cdot+\cdot CCl_{3}\to R-CCl_{3} \tag{6}\]  
\[R\cdot+R\cdot\to R-R \tag{7}\]  
In the MAE process, the particles in the solvent can be energized by microwave, which causes thermal and mechanical effects between CCl\({}_{4}\) and Daxing coal. Microwave can weaken the diffusion resistance of the extracts in the solvent; consequently, the extraction yield can be improved significantly. When the power of the microwave is kept in a range, degradation of the extracts may occur [26]. Figure 6: Relative contents (RC) of the extracted substances according to the categories referred to Figure 4:0  
On the other hand, degradation of the coal residues can also take place when the extraction process is maintained a long time at a relatively high temperature; therefore, more substances can be extracted out. The results obtained in the work are in line with those found by [PERSON] and coworkers [27]. Non-thermal effects of MAE facilitate the extraction of the oxygen-containing compounds, improving the coal rank and increasing the decomposition of minerals [28].
# Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning
## 2 Clutch Temperature Prediction Model Construction Based on Bi-LSTM and Migration Learning
### Model Construction Using Transfer Learning  
The model construction process comprises the following steps:  
(1) Data Preparation: Two types of clutch pressure plates, the Fuda clutch pressure plate (same model, different experimental environment) and the Valeo clutch pressure plate (different model), were selected for experimentation. Temperature measurements were recorded at three distinct positions on each clutch pressure plate. The experiment was conducted over 28 cycles of clutch pressure plate engagement, corresponding to a duration of 840 s. The collected data was used as the target domain for transfer learning. The data underwent cleaning and organization, and time intervals of 0.05 s and 1 s were set for the initial 6 s and the last 24 s, respectively. Finally, the data was normalized to reduce errors.  
(2) Model Architecture: The Bi-LSTM model was imported, and two identical model frames were set up simultaneously to serve as encoders, projecting the two sets of data into a unified space. A regressor and a discriminator were also established. The regressor received the extracted features as inputs and learned the mapping relationship of the predicted target variables on the target domain, while the discriminator distinguished between the trained data (source domain) and the data ready for training (target domain).  
(3) Training the Model: The new data was divided into ten parts, with nine parts allocated as the training set and one part as the validation set. Hyper-parameters such as training times and batch size were set, and the new model was trained accordingly. The validation set was used for prediction to ensure that the model effectively fitted the target domain data.  
(4) Temperature Prediction and Experiment: The model was employed to predict the temperatures at three different points on the platen for the time interval of 840-870 s. Simultaneously, the actual temperatures were collected during experimentation using the LZDZX-430A bench. A comparison and summary of the two datasets were conducted.  
(5) Continued Prediction and Experimentation: The predicted data from step (4) was used as input parameters to further predict the platen temperature for the next 30 s (870-900 s). The experiment was conducted for comparison. This process was repeated for three different time intervals: 840-870 s, 870-900 s, and 900-930 s.
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 3 Existing Security Incidents and Limitations of Security Products
### Cases of Security Threats in Korea and Abroad
#### 3.1.1 Kaseya Incident  
In July 2021, during the U.S. Independence Day holiday, the IT management platform VSA, provided by Kaseya, an IT management solutions provider, was exploited by the REvil ransomware group as a distribution pathway. This supply chain attack led to ransomware being updated through VSA servers, affecting approximately 200 customer companies, resulting in substantial damage [11].
# Flexural Performance of Novel Nail-Cross-Laminated Timber Composite Panels
## 3 Experimental Results and Analysis
### Effects of Different Panel Design Parameters
#### 3.3.2 Effect of Nail Type  
Test specimens KWG30070235240, KWS30070235240, and KWL30070235240 were used for comparison of the effect of the nail type. It can be deduced from Table 4 that the ultimate bearing capacity, deflection, and strain of the threaded nail NCLT panels were the largest, followed by the polished rod NCLT panels, then the screw NCLT panels. From morphological analysis, the polished rod NCLT panels incurred the most serious damage, followed by the screw NCLT panels, then the threaded nail NCLT panels. Accordingly, the threaded nails seemed to be most beneficial at improving the bending resistance of the NCLT panels; the strong pullout force of the threaded nails could prevent cracks from developing from the outside towards the inside, thereby protecting the inside structure and preventing panels from being completely bent and broken.  
According to the Canadian CLT handbook [20], the actual experimentally measured ultimate bearing capacities of the three types of NCLT panels were greater than the corresponding theoretically calculated values expected for conventional CLT panels, indicating advantages of the nailing method versus using glue. The ultimate bearing capacities of the three types of panels were 47.96 kN for polished  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen Number** & **Ultimate Bearing Capacity/kN** & **Deflection/mm** & **Maximum Strain Value** \\ \hline KWG30070235240 & 47.96 & 106.42 & 4293 \\ KWL30070235240 & 58.46 & 133.72 & 5503 \\ KWS30070235240 & 45.46 & 104.22 & 3844 \\ \hline \end{tabular}
\end{table}
Table 4: Ultimate bearing capacity, deflection, and strain.  
Figure 9: Effect of nail penetration angle on load-deflection and load-strain relationship. (a) Load-deflection curve; (b) Load-strain curve.  
rod NCLT panels, 58.46 kN for the threaded nail NCLT panels, and 45.46 kN for the screw NCLT panels. The threaded nail NCLT panels achieved best bending resistance. As shown in Figure 10a,b, the three types of NCLT panels (KWG30070235240, KWS30070235240, and KWL30070235240) displayed superior ductility and large deformation capacity. While threaded nails appeared to be most beneficial for increasing the deflection of the NCLT panels, under similar load, strains of the three NCLT panels were rather comparable, indicating that the type of nail had little effect on strain of the NCLT panels. Generally, the load versus deformation of NCLT panels exhibited desirable linear elastic relationship under normal use (Figure 11a-c).  
Figure 11: Strain curves of different sections in NCLT panels with different nail connections. (**a**) Polished rod nail NCLT panels; (**b**) Screwed NCLT panels; (**c**) Threaded nail NCLT panels.  
Figure 10: Effect of nail type on load-deflection and load-strain relationship. (**a**) Load-deflection curve; (**b**) Load-strain curve.
# Input Selection Methods for Soft Sensor Design: A Survey
## 4 Feature Extraction
Then, classic MDS is performed to compute the reduced-dimensional positions of all the points. LLE has faster optimization and better results than Isomap. LLE also finds a set of the nearest neighbors of each point, so to describe it as a linear combination of them after computing a set of weights for each neighbor. It then finds the lower-dimensional embedding of the points such that each point is still described with the computed linear combination. Laplacian Eigenmaps adopts spectral techniques to perform dimensionality reduction, based on the assumption that the data lies in a low-dimensional manifold that exists in a higher-dimensional space. Another unsupervised procedure to produce a low-dimensional representation of an input space is given by Self-Organizing Maps (SOM, or Kohonen maps) [127], a type of artificial neural network used to perform dimensionality reduction. The task is achieved through Vector Quantization (VQ), which is a classical quantization technique from signal processing that describes a larger set of \(n\) vectors by \(c\) codebook or prototype vectors. The candidate set of inputs is considered as the prototype vectors of the SOM. Similar candidate variables will be identified by the formation of groups, which have the closest proximity to the same prototype vector. The distance measures generally used to evaluate this proximity are linear correlation or covariance in the linear case, otherwise Mutual Information or Entropy in the nonlinear case. The drawback of FE techniques is that the variables in the new space do not have any physical meaning and they become difficult to be interpreted. In addition, in the case of industrial processes, since the original inputs are still needed to obtain the projections, the number of required hardware sensors for the estimation is not reduced, hence losing one of the important advantages brought by the use of a fewer number of inputs.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Convolutional Neural Networks (CNN)
Experimental results demonstrated that by utilizing the Hough transform in conjunction with a pre-trained AlexNet classifier, the classification accuracy for eight modulation formats (including 4/8/16/32/64-QAM and Q/8/16-PSK) reached 100% under clear weather conditions with an SNR of \(\geq\)11 dB. Notably, the required SNR for achieving 100% recognition of high-order modulations (e.g., 64-QAM) was significantly lower than the 18 dB needed for clustering analysis in reference [20]. In foggy conditions, only a 12 dB SNR threshold was necessary for 100% recognition, and when the communication distance was extended to 100 m, AlexNet maintained a classification accuracy of approximately 93%. This approach, through efficient feature extraction combined with deep learning, provides a highly robust modulation recognition framework for dynamic vehicular VLC systems.
## 2 Literature Review
### Summary of Shortcomings  
There were five primary shortcomings in the previous studies that motivated us to conduct this study:  
1. A lack of comparison between RL methods in terms of the applicability to signal control problems. The previous studies often directly specified the RL methods they used, without considering the characteristics of signal control problems.
2. A lack of attention to the time-varying nature of traffic environments and its influence on reward discounting and action-value updating. The previous studies usually defined the time-step size as the variable interval between actions, and discounted rewards every time step.
3. A lack of utilization of intersection symmetry to benefit the RL training. The exception is [PERSON] et al. [37]. However, in his work, the symmetry property was used to design neural network in advance, instead of to expedite training.
4. The neglect of some signal timing constraints, especially the fixed phase sequence and maximum greens. For safety reasons, a signal control technique not abiding by such constraints can hardly implemented in practice.
5. The test-bed intersections were not close-to-reality in terms of vehicle turning and pedestrians. Specifically, the previous studies usually set fixed vehicle turning ratios and had no pedestrian demand. This widened the gap between virtual and real intersections.