esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Analysis of the Main Controlling Factors of the IB Prediction Model  
It is generally believed that the parameters obtained by fitting the accumulated deformation of the surface are called surface prediction parameters. Actual experiments have shown that from the moment of mining to the moment when the surface stops moving, the surface prediction parameters are not static but change with the change in the degree of mining. The premise of constructing the DIB prediction model is that the subsidence factor is the main controlling factor of the prediction model, and during the mining process, the other prediction parameters are less variable, while the subsidence factor is more variable. Below, we took the Guqiao South 1414 (1) working face [36] as an example to explore the changing law of surface prediction parameters during mining.  
According to the principles of the IB prediction model, the displacement deformation values of any point \(i\) on the ground surface at time \(t_{i}\) are \(W_{t_{i}}\) and \(U_{t_{i}}\). If the measured subsidence value of the working face at this time is \(W_{st_{i}}\) and the horizontal displacement value is \(U_{st_{i}}\), then the parameter fitness function at this time is  
\[F=1/\sum\left((W_{st_{i}}-W_{t_{i}})^{2}+(U_{st_{i}}-U_{t_{i}})^{2}\right), \tag{24}\]  
In order to obtain the changing law of the IB prediction model with the degree of mining, according to the MPGA parameter-seeking principle and based on the geological mining conditions at the time \(t_{i}\) and the obtained surface movement and deformation, the dynamic IB prediction parameters corresponding to each period can be obtained.  
A total of seven data periods during the advancement of the 1414 (1) working face were selected for parameter calculation (mainly the measured data of the 1414 (1) working face strike direction), and the specific parameter values are shown in Table 4. In order to avoid the influence of accidental errors on the parameter calculation results, each period of data was inverted 20 times, and the average value of the 20 times was selected as the dynamic parameter of this period.  
From the data in Table 4, as far as \(q\) is concerned, \(q\) gradually increased with the change in time, and when the mining time approached the complete mining time (343 days), the dynamic IB predicted a parameter value that was actually close to the actual IB predicted parameter. For \(\tan\beta_{1}\) and \(\tan\beta_{2}\), the parameter changed little with the change in time. \(\theta\) did not change much with the change in time and tended to be stable. As far as the inflection point offset value is concerned, the parameter value changed little and irregularly  
Figure 13: A 1222 (1) comparison of the measured and fitted subsidence values of the working face.
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## 3 OSP-MSK Performance Analysis
### OSP-MSK ABER Performance
#### 3.1.1 Rayleigh Fading Channel  
Following the aforementioned procedure, the conditional \(ABER_{E|\text{H}}\) over the Rayleigh fading channel of the proposed OSP-MSK can be obtained by replacing the term \(E_{b}/N_{o}\) in Equation (27) by \(\overline{\Psi}\) in Equation (28) as follows:  
\[ABER_{E|H}=erfc\Big{(}\sqrt{\overline{\Psi}}\Big{)}-\frac{1}{4}erfc^{2}\Big{(} \sqrt{\overline{\Psi}}\Big{)}. \tag{31}\]  
Substituting Equation (31) into Equation (30), the ABER of the OSP-MSK over Rayleigh fading channel (\(ABER_{Ray}\)) can be expressed as follows:  
\[ABER_{Ray}=\int_{0}^{\infty}erfc\Big{(}\sqrt{\overline{\Psi}}\Big{)}\;p_{ \Psi}(\Psi)d\Psi-\frac{1}{4}\int_{0}^{\infty}erfc^{2}\Big{(}\sqrt{\overline{ \Psi}}\Big{)}\;p_{\overline{\Psi}}(\Psi)d\Psi, \tag{32}\]  
where \(p_{\Psi}(\Psi)\) is the PDF of the Rayleigh fading channel. Using the well-known PDF of the Rayleigh fading, the \(ABER_{Ray}\) can be expressed as follows:  
\[ABER_{Ray}=\underbrace{\frac{1}{\overline{\Psi}}\int_{0}^{\infty}erfc\Big{(} \sqrt{\overline{\Psi}}\Big{)}\;e^{-\frac{\overline{\Psi}}{4}}\;d\Psi}_{I_{1}} \;-\;\underbrace{\frac{1}{\overline{\Psi}}\;\int_{0}^{\infty}\;erfc^{2}\Big{(} \sqrt{\overline{\Psi}}\Big{)}\;e^{-\frac{\overline{\Psi}}{4}}\;d\Psi}_{I_{2}}. \tag{33}\]Integral \(I_{1}\) in Equation (33) is determined in detail in Appendix A, and it can be expressed as follows:  
\[I_{1}=1-\sqrt{\frac{\overline{\Psi}}{1+\overline{\Psi}}}. \tag{34}\]  
However, the second integral \(I_{2}\) in Equation (33) can be written in the following form:  
\[A=\int_{0}^{\infty}\lambda^{k_{1}-1}\;erfc^{2}\Big{(}\sqrt{\lambda}\Big{)}\;e^ {-k_{2}\lambda}\;d\lambda, \tag{35}\]  
which can be easily solved as in [29] as follows:  
\[A=\mathcal{V}-\frac{4\mathcal{V}}{\pi}\sum_{j=0}^{k_{1}-1}\frac{(k_{2})^{j}}{2 j+1}2F_{1}\Big{(}j+\frac{1}{2},j+1;j+\frac{3}{2};-(k_{2}+1)\Big{)}, \tag{36}\]  
where \(\mathcal{V}\) is given as follows:  
\[\mathcal{V}=\frac{(k_{1}-1)!}{(k_{2})^{k_{1}}}. \tag{37}\]  
Hence, the second integral \(I_{2}\) in Equation (33) is determined by substituting \(k_{1}=1\) and \(k_{2}=1/\overline{\Psi}\) in Equations (36) and (37) as follows:  
\[I_{2}=\frac{1}{4}-\frac{1}{\pi}*2F_{1}\bigg{(}\frac{1}{2},1;\frac{3}{2};- \bigg{(}\frac{1}{\overline{\Psi}}+1\bigg{)}\bigg{)}. \tag{38}\]  
Substituting Equations (34) and (38) into Equation (33), the ABER over the Rayleigh fading channel can be expressed as follows:  
\[ABER_{Ray}=\frac{3}{4}-\sqrt{\frac{\overline{\Psi}}{1+\overline{\Psi}}}\;+ \frac{1}{\pi}*2F_{1}\bigg{(}\frac{1}{2},1;\frac{3}{2};-\bigg{(}\frac{1}{ \overline{\Psi}}+1\bigg{)}\bigg{)}. \tag{39}\]
## 2 Presentation of Components, Data Acquisition Methodology and the System
### Preparing the Signal for Classification  
The investigation was based on the analysis of the archived signal samples provided by dedicated software. Signal measurements are taken with frequency of \(f_{e}=128\) Hz. Hence, each second of the measurement gives 128 signal samples. The application of rolling integration is a significant and innovative idea. Its task is signal integration from each electrode separately in the time of 1 s.  
An example situation may be assumed that a registered 10 s of a signal will consist of \(V=1280\) samples and a 4-s signal will consist of \(V=512\) samples. Variable \(V\) denotes the number of samples in an archived signal. It is necessary for calculating variable \(k\), which shows the number of full one-second periods in the signal measured. The dependence is shown in Equation (1).  
\[k=\frac{V}{128}, \tag{1}\]  
Variable \(S\) is defined for each electrode: \(S_{1}\)-\(S_{32}\) (Equation (2)). Each of them is a table containing signal samples of a given electrode integrated for one second. Hence, the particular values of the integrated waveform are given under consecutive values of table \(S^{(1...k)}\). For example, taking into account Equations (1) and (2), for a 5-s signal, the integrated samples coming from the first electrode will be contained in table \(S^{(1...k)}_{1...32}\) where \(k=5\).  
\[S^{(1...k)}_{1...32}=\Bigg{[}\int_{(((1...k)-1)\cdot 128)+1}^{(1...k)\cdot 128} \big{|}E_{1...32}\big{|}\Bigg{]}. \tag{2}\]  
The operation on the signal takes place in a continuous way, where the absolute value of the 32 electrodes marked \(E_{1...32}\) is integrated by the period of 128 samples, after which the sample value is written to value \(S\). Thus the consecutive data come from the signal range \(1\div 128,129\div 256\ldots\), which is shown in Equation (2).  
Such an original approach is not pointless. This way the chances for erroneous treatment of a signal in the case of interference or other negative artefacts are minimized. It could happen in the case of EEG signal integration for a shorter period. One-second integration periods make it possible to obtain uniqueness in further signal classification. Section 3 contains the results of the signal processing carried out. It presents detailed results of the performed investigations.  
Figure 4: The observation of raw and filtered EEG signals.
## 4 Results
### Comparative Evaluation
#### 4.2.2 Priority Responsiveness (High-Priority Job Delay)  
PIP-only and DRL-only models often failed to meet the time-sensitive demands of high-priority jobs. In contrast, DRLCC significantly reduced high-priority job delay, confirming that adaptive scheduling combined with strict concurrency filtering yields more responsive behavior. Table 5 lists the corresponding numerical means, while Figure 10 reports the mean \(\pm\) standard deviation delay experienced by high-priority jobs. Across all six workload  
Figure 9: Total completion time (mean \(\pm\) SD) achieved by five schedulers under six workload configurations: (a) 4 AMRs, 80 jobs; (b) 4 AMRs, 100 jobs; (c) 5 AMRs, 100 jobs; (d) 5 AMRs, 120 jobs; (e) 6 AMRs, 100 jobs; (f) 6 AMRs, 120 jobs.  
configurations, DRLCC consistently attains the lowest delay, outperforming the next-best DRL-Inheritance baseline. The narrow error bars in Figure 10 indicate that these gains are statistically stable. These results confirm that combining adaptive scheduling with dynamic priority ceiling filtering yields markedly faster service for urgent jobs than either DRL or classical real-time locking used in isolation.  
Figure 10: High-priority job delay (mean \(\pm\) SD) for five schedulers under six workload configurations: (**a**) 4 AMRs, 80 jobs; (**b**) 5 AMRs, 120 jobs; (**c**) 5 AMRs, 100 jobs; (**d**) 6 AMRs, 120 jobs; (**e**) 6 AMRs, 100 jobs; (**f**) 4 AMRs, 100 jobs.  
Table 6 shows that PCP never experiences priority inversion, but at the cost of strict sequential execution, this can leave high-priority jobs stuck behind a long queue. DRLCC, by contrast, learns when it is safe to let a lower-priority task proceed first (for example, when a robot's battery is high or no urgent jobs are waiting) and only delays the most critical work. In applications where absolute avoidance of priority inversion is non-negotiable, classic PCP remains preferable thanks to its deterministic mutual-exclusion guarantees. Conversely, if minimizing the latency of high-priority tasks is paramount, our DRLCC framework consistently delivers faster turnaround for urgent jobs without degrading overall throughput. Finally, in scenarios requiring both strong average completion rates and low delays for critical jobs, DRLCC matches PCP's aggregate performance while drastically reducing wait times for top-priority tasks.
# Material Characterization for Sustainable Concrete Paving Blocks
## 3 Results and Discussion
### Compressive Strength  
Figure 8 shows the relationship between the different replacement levels of materials and the compressive strength of specimens. It is clear that all the replacement materials had an adverse effect on the concrete paving blocks' compressive strength except the crushed glass, which maintains a higher performance in compressive strength with the increase in the percentage of crushed glass. Specimens mixed with RCCA maintained a stable compressive strength until the replacement level reached 40%. The compressive strengths reached 41.07 MPa, 38.04 MPa, and 40.38 MPa when replacement levels were 0%, 20%, and 40%, respectively. After the replacement level exceeding 40%, the compressive strength decreased in an almost linear fashion from 40.38 MPa at 40% to 21.04 MPa. According to BS EN 1338: 2003 [15], the compressive strength of concrete paving blocks shall be equal to or higher than 30 MPa. The specimens with replacement level higher than 60% were unable to meet the specification [16]. According to Figure 8, the specimens mixed with CG had higher compressive strengths than the one mixed with natural coarse aggregate when the replacement levels of crushed glass increased from 10% to 40%. The strength peaked to the highest value, 43.45 MPa, at the replacement level of 20%. Specimens with RCFA and crumb rubber as a replacement of fine aggregates have lower compressive strength compared to specimens without any replacement. When the mass content of crumb rubber in the block increased from 1% to 3%, the compressive strength sharply drops from 31.27 MPa to 21.00 MPa. With regard to RCFA, the reduction of compressive strength was from 32.51 MPa to 23.00 MPa with the replacement level changed from 10% to 30%. The compressive strength of blocks mixed with GGBS kept the same level, nearly 40 MPa when the replacement levels were 0%, 30%, and 50%, respectively. However, the strength sharply decreased to 31.06 MPa when the GGBS content reached 70%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Types of Aggregates** & **Water Absorption of Aggregates** \\ \hline Natural aggregate & 1.53\% \\ Recycled concrete coarse aggregate & 6.28\% \\ Crushed glass & 0.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Water absorption of aggregates.  
Figure 7: Front and side faces of one concrete paving block. (**a**) plan view of a concrete paving block; (**b**) lateral view of a concrete paving block.
## 2 Materials and Methods  
Three composite clay samples were selected from Grahamstown, South Africa, for investigation. The geologic map of Grahamstown can be seen in Figure 2. The red dots on the map indicate the three locations where the clays were obtained. The first location is Eluoxolweni (EXW), which falls within the Dwyka Group of the Karoo Supergroup, though currently covered by the Tertiary Silcrete. The second location is Mayfield North (MFN), and the last location is Beaconsfield, with Beaconsfield 1 North (BFN) and Beaconsfield 2 South (BFS). Both the Mayfield and Beaconsfield deposits fall within the Witterberg Group of the Cape Supergroup. However, the sample from BFS was left out of this study, as it is similar to BFN. Therefore, the three clay samples are labelled EXW, MFN, and BFN.  
The collected clay samples were processed by grinding and oven-drying at 100 \({}^{\circ}\)C for 24 h, then sieving to achieve a maximum grain size of 60 \(\upmu\)m. This processing was carried out to guarantee similar fineness to the PC to maximise the physico-chemical interaction of the clay as pozzolans. Finally, the materials were calcined at 650 \({}^{\circ}\)C for 1.5 h and cooled at room temperature. At this temperature (see TGA results in Section 3.1.2), the mass loss rate becomes constant, signifying significant dehydroxylation for the formation of active silica and alumina.  
Usually, LC\({}^{3}\) could be LC\({}^{3}\)-50 or LC\({}^{3}\)-65, with the number 50 or 65 indicating the content of cement clinker. The limestone and clay will then usually be in a ratio of 1:2. Typically, LC\({}^{3}\)-50 will consist of 50% ground Portland clinker, 30% ground calcined clay, 15% ground limestone, and 5% ground gypsum. However, some studies have investigated other combinations of LC\({}^{3}\), such as ratios of 1:1.25, 1:2, and 1:3.5, and limestone contents ranging from 10% to 20% [17]. In some countries, including South Africa, CEM I 52.5 N (ground cement clinker and about 5% gypsum) is no longer commercially produced. Thus, for this study, Portland-limestone cement, Suretch CEM II/A-L 52.5 N supplied by Pretoria Portland cement (PPC), South Africa, was used. This choice was geared towards an on-site implementation of LC\({}^{3}\) where commercially available PC in South Africa can be readily deployed for use rather than Portland clinker. In the same vein, commercially available Idwala Kulubrite 5 limestone from Idwala Industrial Holdings (Pty) Ltd. (Port Shepstone, South Africa) and gypsum (CaSO\({}_{4}\).2H\({}_{2}\)O) from Associated Chemical Enterprises (ACE) (Johannesburg, South Africa) were used. The LC\({}^{3}\) was mixed with locally sourced sand (Malmesbury sand) to make mortar samples and cast into moulds. A polycarbonate ester base superplasticiser (CHRYSO Premia 310) was used to achieve the desired workability of the mortar mixture.  
Figure 2: Geological map of Grahamstown.
## 2 Related Work  
### Network Congestion Detection  
Active network congestion detection [4; 5] obtains network information by analyzing the behavior of probes actively constructed under the changes in network status, such as ping, traceroute and iPerf. The operation is simple and fast, but there is an 'observer effect'; it injects probes, and adds extra bandwidth overhead to the network, which may change the original network status.  
Passive network congestion detection [6; 7] captures the information of the packets through the detection point and sends it to the server to obtain traffic characteristics and network status, such as NetFlow, sFlow and IPFL; it does not generate extra bandwidth overhead, but the design of mechanisms such as sampling will also sacrifice accuracy of detection.  
The SDN-based network congestion detection [8; 9; 10] can detect network delay, packet loss rate, etc. [PERSON] et al. [11] proposed a measurement scheme OPENnetmon, which actively polls traffic and destination switches and monitors link delay and packet loss through the controller. [PERSON] et al. [12] proposed a delay monitoring framework SLAM, which triggers control messages by actively sending probe packets, and analyzes the time when the messages arrive at the controller to obtain the delay. [PERSON] et al. [13] proposed an efficient passive lightweight predictor EPEle, which uses the Openflow protocol for detecting packet loss. The passive detection method reduces the overhead.  
The INT-based network congestion detection [14; 15; 16] exploits the programmability of the network data plane to collect network status information. [PERSON] et al. proposed a packet  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Carrier**} & **Whether to Occupy** & **Whether to Modify** & \multirow{2}{*}{**Detection Speed**} \\  & & **Extra Bandwidth** & **Packet Content** & \\ \hline Watermark-based detection & IPD & No & No & Fast \\ Active detection & Probe packets & Yes & No & Slow \\ Passive detection & Mirrored packets & No & No & Slow \\ SDN-based detection & Probe packets, etc. & Yes/No & No & Slower \\ INT-based detection & Packet header field & Yes/No & Yes & Fast \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of network congestion detection methods.  
level network telemetry system, Everflow [17], which actively constructs telemetry packets to obtain network status information; it has the advantages of flexibility, efficiency, and configurability. HULA [18] uses special probes to collect global link utilization information and update it periodically, which may lead to a large bandwidth overhead. [PERSON] et al. proposed INT-path [19] by encapsulating the source routing label into the probe packets, which can telemetry the specified network path status.
# Graph Convolutional Spectral Clustering for Electricity Market Data Clustering
## 3 Experiment
### Integration of End-User Electricity Consumption Information Big Data  
Firstly, the validation of the data anomaly identification method is conducted using one month of raw power data from the intelligent meters of end-users in the collection system. The validation environment includes a Win10 system, an i7 processor, and Python language version 3.8. The specific parameters for the Isolation Forest theory are designed as follows: the number of samples for training is set as \(sample\_size=512\), the number of trees built for training is \(n\_trees=200\), the true anormaly rate is 0.0163, and the initial value for the anomaly score is \(desired\_TPR=0.81\).  
To validate the Isolation Forest algorithm for identifying abnormal data and implementing data classification, typical binary classification evaluation metrics are employed [34]. Thisincludes constructing a confusion matrix to calculate the true positive rate (TPR), the false positive rate (FPR), the F1 score, and the average precision (avgPR). The specific results are illustrated in Figure 4.  
Figure 4 displays the scoring situation of normal and abnormal data, demonstrating the effectiveness of the Isolation Forest algorithm in achieving binary classification of normal and abnormal values. The score threshold after training is 0.689. The TPR is 0.975, which correct identification rate of abnormal data, and FPR is 0.0059, indicating normal data being mistakenly classified as abnormal. The F1 score is 0.8194, and avgPR is 0.941, validating the effectiveness of the algorithm.  
After identifying the anomalous data, the corresponding time coordinates can be obtained. By sampling 10 data points around each anomaly and inputting the data into the trained RBF neural network, the reconstructed normal data can be obtained. The results are shown in Figure 5a,b. Figure 5a shows the power values curve containing anomalies, and Figure 5b shows the power values curve after processing with the RBF neural network. It is evident that the anomalies have been reconstructed, achieving a smoothing effect on the user's power values curve.  
Figure 4: Abnormal value detection results.  
Figure 5: The results of load data processing. (**a**) Load data before abnormal data processing. (**b**) Load data after abnormal data processing.
# Multi-Focus Image Fusion Based on Decision Map and Sparse Representation
## 3 Experiment and Analyses
### Parameter Setting  
The \(8\times 8\) image patches were used in the computation of sparse coefficients for each pixel location. Besides that, the block size of the sliding window used for clarity level comparison in the clarity score map was also fixed to \(8\times 8\). The threshold \(K_{1}\) for binarizing clarity score maps was set as \(K_{1}\)\(=0.65\). The overcomplete dictionary \(\Phi\) used in sparse representation had a size of \(64\times 256\), which was trained globally from a large set of natural images. The residue error of the SOMP algorithm was set as \(\varepsilon\)\(=5\). The DCHWT method was implemented based on multiscale transform toolboxes downloaded from MATLAB Central [33], and its level of wavelet decomposition was set to 4. The codes for the GF and IM methods can be found on [PERSON]'s homepage [34], and the codes for the NSCT-PCNN are available on [PERSON]'s homepage [35]. The parameters of these methods were set to their recommended values.