esa-sceva/satcom-chunk-collection huggingface dataset:
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 3 Methods
### Autoregressive Integrated Moving Average Model  
The Autoregressive Integrated Moving Average (ARIMA) [26] model is a widely used time series analysis method. ARIMA models capture the linear relationships among the data points and make predictions based on those relationships. An ARIMA model consists of three components: autoregression (AR), differencing (I), and moving average (MA). The AR component represents the linear relationship between the current observation and a lagged value, i.e., the value of the time series at some previous time step. The I component involves differing the time series to make it stationary, i.e., removing any trends or seasonality. The MA component represents the linear relationship between the error term and a lagged value of the error term.  
The order of an ARIMA model is specified by three parameters: p, d, and q, where p is the order of the AR component, d is the degree of differencing, and q is the order of the MA component. The selection of the values for these parameters depends on the characteristics of the time series being analyzed and can be determined using various methods such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Once the parameters are determined, an ARIMA model can be used to make predictions for future values of the time series. The accuracy of the predictions depends on the quality of the model and the stability of the time series.  
The deformation data of a dam at a certain moment is determined by the past state variables and external disturbances. The linear part of the deformation sequence in ARIMA is represented by AR, which is determined only by the past state, and the MA represents the sequence determined only by random disturbances such as the environment. As time progresses, the state variables often become non-stationary, making it difficult to fit using ARMA. Therefore, the sequence is subjected to several difference operations to make it stationary before using the ARMA model to predict the deformation of the dam.
## 3 The Proposed Model
### Design of CEPOC Technique
By integrating the fitness and location values of penguins, ideal scores are produced, and the penguins' positions are updated based on these scores [38]. As a result, this converges to the optimum solution via the use of reduction factors and acquires the ideal cluster required for effective communication based on the assumed parameters [39]. Following cluster formation, the next step is CH election. As a result, the following variables are used: grid size, node density, and node broadcast range. These variables correspond to the weights assigned in FF. An FF is calculated for the purpose of selecting the optimum solution from among the candidate solutions. An FF is critical to the strategy. Selecting the optimal CH lengthens the life of clusters and may aid network energy conservation. The following approach is used to calculate fitness values. \[Fitness\ function=w_{1}\times a_{1}+w_{2}\times a_{2} \tag{15}\]  
where \(a_{1}\) & \(a_{2}\) describes the \(delta_{difference}\) and \(distance_{neighbor}\), respectively. In Equation (15), \(delta_{difference}\) represents the difference, and \(distance_{neighbor}\) denotes the average distance of node. \(delta_{difference}\) is employed as a condition for LB. \(w_{1}\) indicates the weight assigned to \(delta_{difference}\) and \(w_{2}\) means the weight assigned to \(distance_{neighbor}\). In certain cases, each cluster might contain equivalent number of nodes; however, in a real-time situation, it is not easier because of the sensor's location variations due to water current and another impact \(delta_{difference}\) is employed to measure the difference from an ideal degree to motion of a node from its neighbor. It is computed by:  
\[delta_{difference}=abs\left(ideal_{deg}-node_{deg}\right) \tag{16}\]  
As a result of the current study's findings that static CH election conditions increase the likelihood that a single variable will skew the FF and cause an incorrect CH election, the presented solution dynamically assigns weights to each parameter based on its negative impact on the FF and situations [40]. The starting values of all parameters are normalized between 0 and 10 in this approach, and the deviations of all parameters are calculated as follows:  
\[Dev(p)=\left[mean-parameter(p)\right] \tag{17}\]  
The overall weight should be equivalent to one. Every node's fitness value is defined in Equation (15), in which the values of parameters are employed and weight allocated to all the parameters.
# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 4 Experimental Results
### Pointer Meter Reading Recognition Experiment
#### 4.3.2 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **No.** & \(\mathbf{v}\) & \(\mathbf{v}^{\prime}\) & \(\mathbf{v}^{\prime}-\mathbf{v}\) & \(\mathbf{L}\) & \(\mathbf{\varepsilon}\) \\ \hline \multirow{3}{*}{Original Image} & 1 & 0.81 & 0.81675 & 0.00675 & 1.6 & 0.422\% \\  & 2 & 0.086 & 0.08637 & 0.00037 & 0.16 & 0.231\% \\  & 3 & 5.2 & 5.16308 & \(-\)0.03692 & 10 & \(-\)0.369\% \\ \hline \multirow{3}{*}{Blurred Image} & 1 & 0.81 & 0.79725 & \(-\)0.01275 & 1.6 & \(-\)0.797\% \\  & 2 & 0.086 & 0.08646 & 0.00046 & 0.16 & 0.288\% \\  & 3 & 5.2 & 5.16158 & \(-\)0.03842 & 10 & \(-\)0.384\% \\ \hline \multirow{3}{*}{Darkness Image} & 1 & 0.81 & 0.81781 & 0.00781 & 1.6 & 0.488\% \\  & 2 & 0.086 & 0.08656 & 0.00056 & 0.16 & 0.35\% \\  & 3 & 5.2 & 5.16155 & \(-\)0.03845 & 10 & \(-\)0.385\% \\ \hline \multirow{3}{*}{Overexposure Image} & 1 & 0.81 & 0.81811 & 0.00811 & 1.6 & 0.507\% \\  & 2 & 0.086 & 0.08633 & 0.00033 & 0.16 & 0.206\% \\  & 3 & 5.2 & 5.16986 & \(-\)0.03014 & 10 & \(-\)0.301\% \\ \hline \multirow{3}{*}{Tilted Image} & 1 & 0.81 & 0.81860 & 0.0086 & 1.6 & 0.538\% \\  & 2 & 0.086 & 0.08627 & 0.00027 & 0.16 & 0.169\% \\ \cline{1-1}  & 3 & 5.2 & 5.19130 & \(-\)0.0087 & 10 & \(-\)0.087\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Meter recognition errors for blurred, darkness, overexposure, and tilted images. Figure 11: Recognition results of pointer meter reading.
## 3 Results
### Omics Analysis Using Proteomics and Metabolomics Techniques  
Thirty proteins with increased and decreased expression determined by LC-MS/MS are listed in Table 3. During EF fumigation, about eight proteins in adults showed at least 2-fold different expression. Figure 4: Gene expression levels of exposure to lethal concentration LC\({}_{10}\) and LC\({}_{50}\) values of phosphine (PH\({}_{3}\)) for 20 h on _P. citri_ developmental stages. Relative gene expressions of eggs (**a**), nymphs (**b**), and adults (**c**) were normalized using the expression level of glyceraldehyde-3-phosphate. Gene abbreviations were as follows: chitin synthase (chs), UDP-N-acetylglucosamine enolpyruvoyl transferase (murA), UDP-N-acetylenolpyruvatevoylglucosamine reductase (murB), homocysteine S-methyltransferasease (hnt), V-ATPase (atp), and diaminopellate decarboxylase (dapdc). Data were analyzed using one-way ANOVA, followed by post hoc Tukey’s tests. Different lowercase letters indicate significant differences among groups; a \(>\) b \(>\) c.  
Among them, the decreased proteins are putative dihydropolyI lysine-residue acetyltransferase, putative 50S ribosomal protein L11, mitochondrial aconitase 1 (isoform B), calmodulin, dihydropolyI dehydrogenase (DLD), putative 50S ribosomal protein L9, and putative phosphoribosyl formimino-5-aminoimidazole carboxamideriobotide isomerase, and only one protein, putative heat shock protein IbpA, was elevated. In PH3 fumigation, 17 proteins with two-fold lower expression were pyruvate kinase, putative dihydropolyI lysine-residue acetyltransferase, putative 50S ribosomal protein L11, ATP synthase subunit alpha, mitochondrial aconitase 1 isoform B, DLD, 26S proteasome regulatory subunit 8, 30S ribosomal protein, putative 50S ribosomal protein L9, putative 50S ribosomal protein L7/L12, putative phosphoribosyl formimino-5-aminoimidazole carboxamideriobotide isomerase, 26S proteasome regulatory subunit 4, calcium pump, histone H4 replacement isoform A, vacuolar H[+]-ATPase 55 KD subunit isoform A, and ATP-dependent chaperone protein ClpB. Only two proteins, acetohydroyacid isomeroreductase and putative heat shock protein IbpA, were elevated after the PH3 fumigation. When these two fumigants were treated simultaneously, putative dihydropolyI lysine-residue acetyltransferase, putative 50S ribosomal protein L11, DLD, homocysteine S-methyltransferase transferase, putative 50S ribosomal protein L9, putative phosphoribosyl formimino-5-aminoimidazole carboxamideriobotide isomerase, and calcium pump were significantly decreased when compared to the control group and both single treatments of EF and PH3. On the other hand, acetohydroyacid isomeroreductase and putative heat shock protein (IbpA) in the combinational treated group were up-regulated when compared to the control  
Spectrums of MALDI-TOF MS analysis and lipids differentially generated in _P. citri_ adults are reported in Figure 5 and Table 4. Eleven phospholipids were significantly
# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 1 Introduction
### Prior Works
As a result, resource scheduling optimization emerges as an effective approach to eliminate inter-cluster interference. In a conventional UAV swarm system, the central access point periodically transmits information to UAVs to achieve various objectives, such as collision avoidance and mission planning [25]. To accomplish this in UAV swarms, it is envisioned to adopt a semi-persistent scheduling approach with periodic allocation of time slots, similar to the 3 GPP standard of cellular vehicle-to-everything (C-V2X) sidelink Mode 4 [26]. Since UAVs may vary in type and mission, the required scheduling time intervals for UAVs could differ accordingly. In such cases, when aiming to reduce interference between UAV clusters through scheduling, the UAV scheduling scheme must adhere to specific periodic constraints. We have compiled a summary of the relevant literature in Table 1, presenting the various research themes and scenarios investigated in each study, while emphasizing the differences from our research. Compared to the existing literature, this article explores the utilization of RIS on UAVs to enhance cellular-connected UAV communication in scenarios involving multiple UAV clusters, while taking into account periodic transmission constraints.
# Shared Logistics--Literature Review
## 5 Answering the Research Questions  
### What Topics Related to Shared Logistics Are Covered?  
The most frequently discussed topics are digital tools that facilitate the implementation of shared logistics; most often they are digital platforms that propose connecting all stakeholders to a common network, where they can share information and even assets. A higher degree of cooperation and digitization in logistics is the Physical Internet, which the European Commission defines as a future logistics target enabling decarbonization, declaring that it will support a smooth transition to this model. This area is also noticed by researchers, but to a small extent.
## 5 Achieved Results and Performance Comparison
### Performance Comparison
#### 5.3.2 Virtex-5 Platform Comparison  
The proposed architecture reduces area by 53.8 percent when compared to [18]. In terms of latency, it is 1.97 times faster which is clear evidence of quick computational performance. As a result, our system delivers 6.01 times better throughput/area, highlighting its effectiveness. Similarly, our architecture outperforms [19] in terms of processing performance. Although it is a little more resource-intensive, the large improvement in processing speed results in significant throughput benefits. Our design also outperforms [21] by utilizing 1.28 times fewer hardware resources with 1.38 times faster latency. As a result, 1.78 times greater throughput/area ratio is achieved. Compared to the first solution in [26], the area is reduced by 77.1 percent while operating 1.35 times slower. Nevertheless, it still manages to achieve a commendable throughput/area ratio of 2.63 times higher. Our architecture utilizes 47.3 percent fewer hardware resources compared to the second solution in [26] while concurrently maintaining a 1.35 times faster processing speed. Thus, our approach achieves 2.56 times greater throughput/area ratio. These results highlight the great performance and resource efficiency of our architectural solution when adapted for Virtex-5.
## 3 Results and Discussion
### Comparison with Other Modeling Methods  
To further evaluate the effectiveness of the proposed combined modeling approach, this section includes a comparison with other advanced modeling methods. Two comparison algorithms chosen are gradient boosted decision trees (GBDT) and random forests (RF), which are two renowned ensemble modeling techniques. RF enhances model accuracy and stability by constructing and aggregating predictions from multiple decision trees. In contrast, GBDT predicts outcomes by combining multiple decision trees, where each new tree is sequentially trained to correct the prediction errors of the previous tree. Implementations of RF and GBDT were carried out using the GradientBoostingRegressor and RandomForestRegressor functions from the sklearn library, respectively. For RF, the number of estimators was 700 and the maximum depth of the trees was 20. For GBDT, the number of estimators was 1200. A comparison of the prediction results between the combined model and these two models is presented in Table 4.  
It can be observed that the combined model still maintains a certain predictive advantage over these two ensemble modeling methods. While the RF model achieves a relatively low MAPE, the presence of overfitting in some trees may have led to a larger MaxAPE. GBDT demonstrates good control over maximum error. However, as the improvement in prediction errors by GBDT is applied to the entire dataset, it does not outperform the combined model. The combined model approach considers the concept of bias fitting from the GBDT algorithm while also considering the varying operating conditions of gas turbines. This allows the model to make more fine-grained adjustments for each specific operating condition, resulting in more accurate model outcomes in this study.
# Cohesive Subgraph Identification in Weighted Bipartite Graphs
## 3 Online Solution  
Before introducing the detailed algorithms, Figure 2 shows the general framework of the proposed techniques in this paper. To identify the \((k,\omega)\)-core, an online solution is first developed in Section 3. To efficiently handle large networks and different input parameters, an index-based solution is further proposed in Section 4. The index-based solution consists of two phases: an index construction phase and query phase. In addition, different optimization techniques are proposed to ensure a balance between the index construction time and index space.  
For the online solution, we introduce a baseline algorithm, named GCore, by extending the solution for \((\alpha,\beta)\)-core computation. The main idea of GCore is to iteratively remove nodes with a degree less than \(k\) in \(L\) and a weight less than \(\omega\) in \(R\). GCore terminates until the size of \(G\) stays unchanged, i.e., there is no node that violates the constraints. Then, we output the remaining graph as \((k,\omega)\)-core. The details are shown in Algorithm 1. In Lines 2-5, we check the degree constraint for nodes in \(L\). For each node \(u\in L\) with \(d(u)<k\), we remove it with its adjacent edges. Then, we update the weight of node \(v\) in \(N(u)\), i.e, subtract the weight of corresponding removed edge \(e(u,v)\) from the total weight \(w_{t}(v)\). In Lines 6-9, we examine the weight constraint for nodes in \(R\). For each node \(v\) with \(w_{t}(v)<\omega\), we remove it with its incident edges. Accordingly, we decrease the degree of \(u\) by \(1\) for each \(u\) in \(N(v)\), which may cause the node to violate the degree constraint. The algorithm terminates until both constraints are satisfied and finally returns the \((k,\omega)\)-core of \(G\).  
```
Input : Bipartite graph: \(G=(L,R,E,W)\), degree constraint: \(k\), weight constraint: \(\omega\) Output : The \((k,\omega)\)-core of \(G\) while\(\exists u\in L\) with \(d(u)<k\vee\exists v\in R\) with \(w_{t}(v)<\omega\)do
1 foreach\(u\in L\wedge d(u)<k\)do
2 foreach\(v\in N(u)\)do
3\(w_{t}(v)\gets w_{t}(v)-w(u,v)\);
4\(L\_remove(u)\);
5 foreach\(v\in R\wedge w_{t}(v)<\omega\)do
6 foreach\(u\in N(v)\)do
7\(d(u)\gets d(u)-1\);
8\(R\_remove(v)\);
9
10 return\(G\)
```  
**Algorithm 1**Generate \((k,\omega)\)-core  
Discussion: The time complexity of Algorithm 1 is linear to the size of the graph. However, as discussed in the introduction, the method is still not affordable, especially for massive graphs and queries.
# Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees
## 1 Introduction
Typically, this is achieved through a trial and error process. Another main drawback of the ANN model is the black-box nature of the prediction process. Further, the relationship between input and output variables is given in terms of a matrix of weight and biases that cannot be accessed and easily undertood by the user [20]. Recently, decision trees i.e., random trees (RT) and C4.5 decision trees (DT) have been effectively applied in various domains and applications such as in the assessment of soil liquefaction potential [21; 22], landslide susceptibility [23], classification of power quality problem [24] and surface settlement prediction owing to tunneling [25], however, their application in rock mechanics and mining are limited. The main purpose of this study is to predict the pillar stability of underground coal and stone mines. To achieve this objective, RT and C4.5 DT algorithms are implemented to develop two models for pillar stability prediction. A critical review of existing literature suggests that despite the successful implementation of these techniques in various domains, their implementation in the analysis of rock mechanic mining is scarcely explored. Unlike other soft computing methods, RT and C4.5 DT algorithms can provide an easy-to-use, comprehensible tree structure. The remainder of this paper is organized as follows: Section 2 briefly provides the description of datasets used for pillars stability for underground mines. Section 3 presents the methodology used to predict pillars stability for underground mine; an overview of the random tree and C4.5 decision tree techniques. Section 4 presents the development of the prediction models. Detailed results of the proposed models are discussed by performance criteria and comparisons with similar previous models are presented in Section 5, followed by conclusions and recommendations in Section 6.
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
## 3 Results and Discussion
### Interference of Organic and Inorganic Matter on PFAS Removal
#### 3.3.2 Interference of Inorganic Matter on PFAS Removal  
Nitrate, sulfate, bicarbonate, and phosphate are common inorganic anions in groundwater. Two inorganic anions, namely, nitrate and sulfate, were selected to investigate the interference of inorganic matter on the removal of PFAS compounds by MIEX(r) GOLD resin. The influence of inorganic matter on PFAS adsorption was investigated by including 5 ppm nitrate and 20 ppm sulfate (total of 25 ppm inorganic matter) along with 1 ppm of each  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**PFAS**} & **Removal Efficiency** & \multicolumn{2}{c}{**Removal Efficiency**} & \multicolumn{2}{c}{**Removal Efficiency**} & \multicolumn{2}{c}{**Removal Efficiency**} \\  & **(\%) without** & **Std (\(\pm\))** & **(\%) in 20 ppm** & **Std (\(\pm\))** & **(\%) in 5 ppm humic Acid + 5 ppm Tamic** & **Std (\(\pm\))** & **(\%) in 1000** \\  & **Interference** & & **Nitrate** & **Acid** & **(\%) in 5 ppm Tamic** & **Std (\(\pm\))** & **NOM at High** \\ \hline PFHsG & 100 & 0 & 99.88 & 0.09 & 99.82 & 0.02 & 91.00 \\ \hline PFOA & 99.91 & 0.04 & 99.50 & 0.09 & 99.79 & 0.01 & 67.42 \\ \hline PFOS & 100 & 0 & 98.40 & 1.00 & 99.94 & 0.90 & 94.35 \\ \hline \end{tabular}
* N.B. 50 ppm TA + 50 ppm HA + 250 ppm nitrate + 250 ppm sulfate.  
\end{table}
Table 1: Removal efficiency of PFAS compounds with and without the interference of NOM and inorganic matter.  
Figure 5: Interference of inorganic and organic matter on the removal of PFHsS, PFOA, and PFOS by MIEX® GOLD resin.  
PFAS compound (PFHxS, PFOA, and PFOS). The results indicated that for all three PFAS compounds, greater than 98% removal was achieved (Table 1). These results signified very little influence of inorganic matter on PFAS removal using MIEX(r) GOLD resin (Figure 5), even when the concentration is 25 times higher. [PERSON] and co-workers reported that the adsorption capacity of anion-exchange resin IRA67 for PFOS decreased 8.8% when the sulfate concentration was increased from 0 to 1.9 ppm (i.e., 2 mmol/L) [16]. Whereas, MIEX(r) GOLD resin showed no noticeable impact on PFOS adsorption when the sulfate concentration was increased from 0 to 20 ppm.
# Characteristics of HEMP Radiation Field on Typical Transport Aircraft with Frequency Sweep Method
## 3 Results
### Electric Field Strength Distribution of Bottom Layer  
Lastly, the distribution of the electric field strength \(E(t)\) formed by the HEMP radiation field on the aircraft is analyzed. Numerical results of the bottom layer on or outside of the aircraft are calculated and analyzed using the frequency sweep method.  
In Figure 12, the bottom layer is below the aircraft fuselage and wings. Due to the obstruction of the fuselage and wings, the induced electric field strength \(E(t)\) at the bottom layer is smaller than that at the top and inner layers. However, values of \(E(t)\) at most locations on the aircraft are still higher than the initial value \(E_{0}\) (50 kV/m). Figure 13 displays the tendencies of the electric field strength \(E(t)\) at various positions of the bottom layer, outside the aircraft.  
Figure 12: Electric field strength distribution \(E(t)\) of bottom layer with different time \(t\). (**a**) \(t=0\) ns, (**b**) \(t=10\) ns. (**c**) \(t=100\) ns.
# Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens
## 1 Introduction
* Focus on Data-Driven Refinement: The model uses experimental data to fine-tune parameters, improving prediction accuracy and addressing the limitations of traditional trial-and-error methods. * Efficiency and Practicality: The study emphasizes computational efficiency while maintaining high prediction reliability, offering a scalable tool for industrial applications in AM. By incorporating multiple AM configurations, the model demonstrates a strong ability to capture complex relationships, enabling more accurate and reliable predictions. This novel approach not only bridges the gap between theoretical predictions and experimental outcomes but also provides a systematic framework for refining manufacturing parameters. Ultimately, this work broadens the potential applications of AM, offering a data-drivensolution for improving the mechanical integrity and performance of 3D-printed parts across various industries. The structure of the paper is organized as follows. Section 2 provides a concise overview of the ANFIS architecture used for the prediction of mechanical properties. Section 3 describes the evaluation metrics used to assess model performance. Section 4 outlines the experimental framework, including dataset preparation, parameter investigation, and the results of the comparative analysis of grid partitioning and subtractive clustering techniques. Lastly, Section 5 presents the conclusions and suggests potential directions for future research.