esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results Discussion and Optimization Analysis
### Discussion and Managerial Insights  
Our results have managerial implications for decision makers, concerning specific parameters that can be improved to increase the effectiveness of the proposed system. These effects can be applied to the health helpline centers mentioned in Section 5. The change in parameter values affects the average buffer length of the health helpline center and other system performance indices. The following findings are based on the numerical simulations and sensitivity analyses performed.  
* To increase productivity, chief physicians initiate outgoing calls when there are no incoming calls to the system, reducing their idle time. An excessively high outgoing generation rate (\(\alpha\)) will only lead to track congestion, while an outgoing generation rate that is too low will cause the attending the attending physician to be idle for too long, leading to a waste of personnel.
* When the maintenance time is long (\(\eta\to 0\)), the entire service facility is always under maintenance, leading to track congestion. As \(\eta\) increases, the probability that the chief physician is in a regular service period increases, reducing the loss of customers, thereby improving the loss of income. However, the larger the value of \(\eta\), the shorter the service facility maintenance time and the average buffer length at the health helpline center. The results show that the appropriate control of the maintenance rate can effectively reduce track congestion and achieve efficient operation.
## 4 Power Consumption and Energy Efficiency Models  
Total power consumption similar to that applied in, for example, [5, 16, 20, 36, 37, 38], is used in the considered uplink cell-free massive MIMO systems. Therefore, the power consumed by \(m_{th}\) AP fronthaul link to the CPU depends on the amount of traffic on the link that should be transferred. Thus, the power consumed by \(m_{th}\) fronthaul link is given as  
\[P_{\mathrm{FH}_{m}}=\frac{P_{\mathrm{FH}_{m}\mathrm{str}}R_{\mathrm{FH}_{m}}}{ C_{\mathrm{FH}_{m}}}, \tag{20}\]  
where \(R_{\mathrm{FH}_{m}}\) gives the actual fronthaul rate between \(m_{th}\) AP and the CPU and is expressed as  
\[R_{\mathrm{FH}_{m}}=\frac{2K(\tau_{c}-\tau_{p})\alpha_{m}}{T_{c}}, \tag{21}\]  
where \(\alpha_{m}\) and \(T_{c}\) represent the number of quantization bits at \(m_{th}\) AP and the coherence time (in seconds), respectively. For simplicity, we assume that all APs have the same value of \(P_{\text{FH}_{m}},\alpha_{m}\), and \(C_{\text{FH}_{m}}\). In addition, the power consumed by the RF chain circuit at \(m_{th}\) AP is expressed as  
\[P_{\text{RF}}=P_{\text{M}}+P_{\text{LO}}+P_{\text{LPF}}, \tag{22}\]  
where \(P_{\text{M}},P_{\text{LO}},\) and \(P_{\text{LPF}}\) are the power consumed by the mixer, local oscillator, and low-pass filter, respectively. The total circuit power consumption for all APs in the considered uplink cell-free mm-Wave massive MIMO systems is expressed as  
\[P_{\text{T}}=(\sum_{k=1}^{K}R_{\text{CP}_{k}} +P_{\text{TX}_{k}})+\sum_{m=1}^{M}P_{m}^{\text{FH}}+MN_{r}(P_{ \text{LNA}}+P_{\text{SP}})++M(\sum_{k=1}^{K}\mathcal{Z}_{k,m}P_{\text{SW}}\] \[+K(N_{Q}P_{\text{CPS}}+P_{\text{C}}(N_{Q}+1)+P_{\text{RF}}+P_{ \text{ADC}})), \tag{23}\]  
where \(P_{\text{TX}_{k}}\) and \(P_{\text{CP}_{k}}\) are the transmit power of \(k_{th}\) UE and the amount of power required to operate the circuit components, respectively. \(P_{\text{TX}_{k}}\) is shown as \(P_{\text{TX}_{k}}=\rho\sum_{k=1}^{K}\frac{\mathbb{E}[|x_{k}|^{2}]}{\eta_{k}}\), where \(\eta_{k}\) denotes the power amplifier efficiency at \(k_{th}\) UE. In addition, all UEs have the same value of both \(\eta_{k}\) and \(P_{\text{CP}_{k}}\). Furthermore, \(P_{\text{LNA}},P_{\text{SP}},P_{\text{SW}},P_{\text{CPS}},P_{\text{C}}\), and \(P_{\text{ADC}}\) are the power consumed by the low-noise amplifier, splitter, switch, CPSs, combiner, and analog-to-digital converter, respectively. Thus, the total uplink energy efficiency (EE), which is measured in bits per Joule, is given as  
\[\text{EE}=\frac{B\times\text{SE}}{P_{\text{T}}}. \tag{24}\]
# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 3 Model Construction and Volume Measurement Strategy and Deployment
### Volume Measurement Strategy
#### 3.2.2 Spatial Point Height Measurement Model  
To measure the height of the cargo, we needed to obtain the height value of any spatial point in the trailer. Inspired by the PnP [26; 27] method, we obtained the height value using the projection from the image plane to the camera coordinate system. As shown in Figure 10, \(O_{C}\) was the camera's optical center, also known as the center of projection; \(P_{1}\), \(P_{2}\) and \(P_{3}\) in \(\left\{\,P\right\}\) were three known 3D points in the camera coordinate system; and \(P_{1}{}^{I}\), \(P_{2}{}^{I}\) and \(P_{3}{}^{I}\) in \(\left\{\,P^{I}\right\}\) were their projections on the image plane, respectively. We solved angle \(\theta\) using the projection of 3D coordinates \(\left\{\,P\right\}\) to 2D coordinates \(\left\{\,P^{I}\right\}\) and constructed a spatial geometry model to measure the height values in the trailer. \(P_{1}\), the center of the QR code, was perpendicular to the bottom of the camera as a reference point for solving spatial angle \(\theta\).  
Figure 9: Spatial model of the camera and the loading platform.  
We obtained the height value (\(h_{2}\)) of \(P_{2}\) using Formula (11). We determined the lengths of \(O_{C}P_{1}\) and \(O_{c}P_{2}\) in the depth map, and \(O_{C}P_{1}\approx h_{0}\).  
\[h_{2}=h_{0}-O_{C}P_{2}\cdot\cos\theta \tag{11}\]
# NOMA-Based VLC Systems: A Comprehensive Review
## 3 Integration of NOMA-Based VLC with Emerging Technologies
### NOMA-VLC with OFDM
A generic overview of two-user NOMA-based VLC system with QAM and O-OFDM is presented in Figure 8.  
In Figure 9, we have provided BER performance analysis for NOMA-DCO-OFDM for two users (User1 and User2). We analyzed the impact of power allocation on BER performance for each user. In Figure 9a, we consider power allocation as 0.9 and 0.1 for User1 and User2, respectively. While in Figure 9b, we consider power allocation as 0.6 and 0.4 for User1 and User2, respectively. Our results show that a significant power allocation difference between both users can give better BER performance.
## 2 Derivation of Amplification Factors (_A_Fs)
### Input Waveforms Sa(T)\({}_{b}\)  
This section discusses and describes the selected input accelerograms, b(t), used in the above algorithm for the calculation of the amplification factors in this study.  
From the RESORCE database [69], sets of 14 input waveforms (S1 to S14), recorded on outcropping rock, are selected for this study, and their characteristics are listed in the Supplementary File, section (b). The selected accelerograms are drawn from real earthquakes and satisfy certain conditions to ensure a representative average amplification factor that is not biassed by a spectral content that is too rich in either short, medium, or long frequency [35]. The main characteristics of the 14 reference acceleration time histories are summarized in the Supplementary File. Figure 1 presents the spectra of the selected signals normalized to a \(PGA\) of 1 g. Each normalized seismic signal is scaled by the appropriate factor to achieve the chosen eleven \(PGA\) values (0.01 g, 0.05 g, 0.1 g, to 0.9 g, with a step of 0.1 g and 1.05 g). We then compute 49 896 time-history seismic responses (14 records \(\times\) 11 \(PGA\) levels \(\times\) 324 soil profiles) using the shear modulus degradation curve of the clay, and the same process and number of seismic responses are also carried out for sand.
## 3 Finite Element Modeling (FEM) of Human-Seat Interactions
### Human Modeling  
A simplified human body model was developed using anthropometric data derived from a volunteer participant [14; 15]. The volunteer's biometric characteristics (Table 1) represented an average user profile for automotive seating studies.  
The model comprised two distinct components: the skeletal structure (Figure 4a) and the soft tissue layer (Figure 4b). The skeletal structure included the sacrum, pelvis, and femur, modeled using standard 3D dimensions based on referenced studies [16; 17]. The soft tissue layer, encompassing skin, muscles, and connective tissues, was geometrically simplified using basic shapes such as cylinders and spheres. These shapes were refined and assembled to reflect the anatomical proportions and seated posture, as shown in Figure 4.  
A biomechanical model was created by meshing the skeletal and soft tissue components with a total of 13,346 four-node tetrahedral elements. These included 5779 elements for the femur, 5575 for the pelvis, and 1992 for the sacrum (Figure 4c). The modeling process was conducted using MSC.Marc simulation software, ensuring high fidelity in representing the physical and mechanical characteristics of the human body.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stature (mm)** & **Body Mass (kg)** & **Thigh Length (mm)** \\ \hline
1720 & 72 & 430 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The size and anthropometry characteristics of the model.  
The mechanical properties of bones were defined using data from [PERSON] [18], with a [PERSON]'s modulus of 16,700 MPa, [PERSON]'s ratio of 0.3, and density of 1700 kg/m\({}^{3}\) (Table 2). For soft tissues, the [PERSON] hyperelastic material model (Equation (1)) was employed, capturing nonlinear deformation behavior. Material parameters were set as \(C_{10}\) = 1.65 kPa, \(C_{01}\) = 3.35 kPa, and \(D_{1}\) = 3.653, based on [PERSON]'s studies [19].  
\[W=C_{10}(I_{1}-3)+C_{01}(I_{2}-3)+(1/D_{1})(J-1)^{2} \tag{1}\]  
where:  
\(C_{10}\), \(C_{01}\) are material parameters representing shear characteristics;  
\(D_{1}\) is a material parameter that represents the compressibility of the material;  
\(I_{1}\), \(I_{2}\) are two invariants of the isochoric (volume-preserving) form tensors of the Cauchy-Green strain tensor;  
\(J\) represents the volume change ratio of the material before and after deformation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Young’s Modulus (E/MPa)** & **Poisson’s Ratio (\(\mu\))** & **Density (\(\rho\)/kg.m\({}^{3}\))** \\ \hline
16,700 & 0.3 & 1700 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters of bones [18].  
Figure 4: The skeletal component (**a**), soft tissue part (**b**), and biomechanical finite element (**c**) of the model. Colors were used to distinguish different types of component, including the sacrum (pink), pelvis (purple), femur (or-ange), the soft tissue layer (green), biomechanical finite element (Transparent color).
## 2 Spectrum Sensing Problem  
The radio spectrum is a limited natural resource. Many techniques, such as cooperative communication systems and heterogeneous networks, have been developed to deal with this scarcity of the radio spectrum [26]. However, none of them can meet the strong demand for radio spectrum. The cognitive radio introduced by [PERSON] [27; 28] is a promising solution allowing dynamic access to the radio spectrum [1]. Dynamic spectrum access (DSA) is defined as a technique by which the operating spectrum of a radio network can be selected dynamically from the available spectrum [26]. The DSA allows the SU to exploit the holes in the spectrum dedicated to the PU. The great challenge of DSA for the SU remains the spectrum sensing stage, during which the SU should detect the presence of the PU on a given bandwidth.
## 2 Methodology
### Data Analysis  
Listener transcription data were initially calculated for each listener and then these data were collapsed across listeners for each speaker. Overall, the group of listeners preferred and provided their transcriptions using IPA in more than 94% of the total responses to more than 12,000 total stimuli (835 transcriptions \(\times\) 15 listeners). In those instances where orthographic transcription occurred (approximately 750 responses), these transcriptions were confirmed by one of the authors at the end of the listening session and then converted to IPA for further analysis. Based on this procedure, individual speaker scores for each of the three lists were initially compiled for all five speakers. All scoring of listener responses was carried out at the phonetic level, but two accuracy scores were obtained, a whole-word score and a phoneme or \"consonant\" score. Data were analyzed further to identify accuracy patterns by phonetic class (stops, fricatives, affricates, nasals, and liquid-glides), as well as for pre- and postvocalic position within the target word. Whole-word scores were obtained and compared. Statistical analyses of the data were performed to determine differences across speakers and across stimulus lists. A repeated-measures, one-factor analysis of variance was computed using Statistical Package for the Social Sciences (SPSS, Version 18, Minneapolis, MN, USA) on the overall listener scores for each of the three lists.
# Investigation of the Two-Way Injection Slurry-Supply Method for the Cu CMP Process
## 2 Materials and Methods  
### Slurry-Supply System  
To confirm the effect of the slurry-supply method, the slurry and H2O2 were separately injected from two nozzles. Figure 1. shows a schematic and an image of the general injection method and the two-way injection system. The evaluation was conducted by changing the flow rates of each nozzle for the slurry and H2O2, and the total flow rate injected from the two nozzles was set at 150 mL/min. These results were compared with those of the general slurry-supply method (premixed slurry and H2O2). Since the total amount of additives during the CMP process must be the same, the concentration of additives was slightly varied with the flow rate.  
Figure 1: Images of **(a)** the general injection method and **(b)** the two-way injection method.
## 4 Analysis
### Scenario 1
#### 4.3.2 Model Validation for Scenario 1  
As shown in Figure 9, the model's performance varies with different landslide-to-non-landslide ratios. At a 1:1 ratio, the AUC value is the lowest, at 0.897. As the proportion of non-landslide samples increases, the AUC value exhibits an upward trend, peaking at 0.914 for the 1:5 ratio. However, the AUC value slightly decreases to 0.900 at a 1:50 ratio and drops further to 0.894 at a 1:100 ratio. When the ratio increases to 1:150 and 1:200, the AUC value rebounds to 0.905 and 0.907, respectively. Apart from the 1:1 and 1:100 ratios, the AUC values remain above 0.900 for all other ratios.  
Figure 8: LSIs under imbalanced ratios. The red vertical lines indicate the thresholds separating different landslide susceptibility levels (very low, low, moderate, high, and very high).  
Figure 9: ROC curves for different ratios.
## 3 Methodology
### Phase 2: Optimal Architecture Synthesis
#### 3.2.3 Framework of the Optimal Fake News Detection Model  
In this section, we present the detailed steps of the optimal Fake News Detection (FND) model. The general architecture of the model is illustrated in Figure 2, and each step is discussed in the following:  
1. Input Text Acquisition: Raw news text (e.g., from news articles or social media platforms) is fed into the system. This input could include complete articles, headlines, or specific claims.
2. Tokenization: The input text is tokenized using the bert-base-uncased tokenizer. This process involves * Splitting the text into subword tokens; * Assigning token IDs to each subword; * Applying padding and truncation to standardize input length (128-512 tokens). This step converts human-readable text into a numerical format suitable for model processing.
3. Embedding Layer: Each token ID is mapped to a dense vector representation using an embedding matrix, initialized with Xavier uniform initialization. The embeddings are scaled by \(\sqrt{d}\) (where \(d\) is the model dimension) to ensure appropriate magnitude and stability.
4. Positional Encoding: Sinusoidal positional encodings are added to token embeddings to provide sequence-order information, as transformers do not have an inherent understanding of token positions. Dropout (typically 0.1-0.2) is applied to regularize the model and prevent overfitting.  
* 5 Transformer Encoder Stack (3-5 Layers): Each encoder layer consists of the following: 1. Multi-Head Attention: Computes self-attention using Query, Key, and Value matrices. This allows the model to focus on contextually relevant tokens. Removing it caused a 15-50% accuracy drop in ablation studies. 2. Feedforward Network: Applies a fully connected two-layer network with GELU activation. Dimensions are kept lightweight (typically 256-768). Dropout, residual connections, and normalization are used for stability and generalization.  
Figure 2: Proposed Optimal Fake News Detection Model.  
* Layer Normalization and Residual Connections: LayerNorm is applied after both the attention and the feedforward sublayers, helping to stabilize training and accelerate convergence.
* Final Layer Normalization: The output of the last transformer layer is passed through a final LayerNorm operation to ensure consistent scaling and improve training dynamics.
* Enhanced Classification Head: The vector corresponding to the special [CLS] token is used for classification. It passes through the following components:
* Linear layer;
* GELU activation;
* Layer normalization
* Final linear layer projecting to 2 output classes (fake or real).  
This classification head design is inspired by DeBERTa and improves decision-making capacity compared to standard linear heads.
* Softmax and prediction: Softmax is applied to obtain the class probabilities \(P\) (Fake) and \(P\) (Real). During training, CrossEntropy loss with label smoothing (0.1) is used. The final class prediction is determined by the highest softmax probability (i.e., argmax on the output logits).
## 3 Modeling and Signal Analysis Method  
### Vehicle-Track Rigid-Flexible Coupling Dynamics Model  
In order to simulate the dynamic responses of axle-box acceleration to polygonal wheel defects, consider as an example high-speed electric multiple unit vehicle and a China Railways Track Structure (CRTS) II slab ballastless track of the Chinese high-speed railway lines [24]. Based on the mechanical model of the vehicle-track coupled system shown in Figure 2a, a numerical model of a vehicle-track coupling system was developed, representing a passenger car running on a slab track. The simulation is implemented by using both Universal Mechanism (UM) and ANSYS software.  
The vehicle model consists of one rigid car body, two rigid bogies, and four elastic wheelsets. The car body, bogies, and wheelsets are connected by linear springs and viscous dampers representing the primary and secondary suspensions. The vehicle can have vertical, lateral, and longitudinal motion. The polygonized wheels are suspected of producing vibrationsin the middle- and high-frequency ranges [25]. Therefore, a finite-element model in the ANSYS environment is used to calculate the model wheelset parameters by treating the wheelsets as flexible bodies. The wheel tread is of LMA worn type, and the wheel polygonaredescribed by sinusoidal functions to determine the harmonic deviation of the wheel radius from a constant value [4]. For the flexible-track model, we transform the CRTS II slab ballastless track finite-element model into UM. The finite-element model of the CRTS II slab ballastless track is composed of two CHN60 rails, a concrete slab layer, a concrete-asphalt (CA) mortar filling layer, and afoundation, which are all represented by three-dimensional solid elements in ANSYS. The fasteners connecting the rails and the concrete slab layer are modeled as spring elements. The connections between the three layers (i.e., concrete slab layer, CA mortar layer, and the foundation) are modeled as contact elements. Figure 2b shows an enlarged view of the flexible wheelsets and tracks, and Table 1 lists the parameters of the vehicle and track systems [24].  
Figure 2: Vehicle-track rigid-flexible coupling dynamics system for UM-ANSYS simulation:(**a**) Mechanical model of train-vehicle coupling system; (**b**) Enlarged view of flexible wheelsets and tracks.  
The vertical wheel-rail contact forces are calculated by using the Kik-Poitrowski model, which considers the nonlinear contact mechanics between wheel and rail. Creep and tangential forces in the contact area are calculated by using [PERSON]'s linearized theory.  
The vehicle-track rigid-flexible coupling dynamics model is validated in [26].
## 3 Results and Discussion
### Quality Prediction Test
#### 3.2.2 Spectrophotometric Measurement  
In Figure 7, catalase activity in fresh mango extracts was found to range from 14.27-16.70 units mL\({}^{-1}\), with an absorbance difference of 0.096-0.111 arbitrary units (a.u.). However, the extracts obtained from mango slices dried at 40 \({}^{\circ}\)C exhibited a greater change in absorbance and higher catalase activity (0.113 a.u., 17.04 units mL\({}^{-1}\)) compared to 60 \({}^{\circ}\)C (0.059 a.u., 8.20 units mL\({}^{-1}\)) and 80 \({}^{\circ}\)C (0.021 a.u., 2.20 units mL\({}^{-1}\)), respectively. According to Michaelis-Menten reaction kinetics, the reaction rate at a fixed substrate concentration, temperature and pH is directly related to enzyme concentrations. As the enzyme concentration increases, the reaction rate asymptotically approaches its maximum, and the absorbance difference also increases. Thus, the results confirm that the difference in absorbance values of mangoes dried at 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C and 80 \({}^{\circ}\)C is directly related to their residual catalase activity units. The mango with more catalase units decompose the H\({}_{2}\)O\({}_{2}\) substrate at a faster rate, and as a result, a larger change in absorbance values was observed. Hence, by measuring the absorbance values, it's easy to differentiate between the dried samples treated at different temperatures. This approach is also simple, rapid and capable of detecting very low activity levels \(\leq\)1.20 units mL\({}^{-1}\) that the flotation test was unable to detect. Furthermore, the results of catalase activity units for fresh samples and those dried at 40 \({}^{\circ}\)C are closely related for both spectrophotometric and indirect flotation tests. However, spectrophotometric measurements are more accurate for determining catalase activity in samples dried at 60 \({}^{\circ}\)C and 80 \({}^{\circ}\)C, as the indirect flotation test showed  
Figure 6: (**A**) Disc rising time (s), (**B**) activity rate (mm s\({}^{-1}\)) and (**C**) catalase activity (units mL\({}^{-1}\)) of fresh mango and mango dried at 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C and 80 \({}^{\circ}\)C. Different letters in a graph represent significant differences at \(p\) < 0.05, (\(n\) = 10 \(\times\) 3).  
a low accuracy for catalase activity below 5 units mL\({}^{-1}\). Therefore, spectrophotometric measurements are appropriate to deal with fresh samples and samples dried at different temperatures. Several researchers [41; 42] have utilized the spectrophotometric measuring approach based on catalase activity in the field to investigate the biological samples.
# Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
## 4 System Experiment and Analysis Evaluation
### Overlimit Warning Experiment  
#### 4.3.1 Experimental Environment  
In order to test the accuracy of the operation results of the proposed overlimit early warning model, this paper builds a mine filling simulation system for data collection and designs an overlimit early warning model based on Tensorflow to detect and warn the overlimit data. The experimental environment of the mine filling simulation system is KingView 7.5 SP3, and the experimental environment of the overlimit early warning model is the Anaconda virtual environment based on TensorFlow 2.5, using the Python 3.9 language for programming [26; 27; 28].