esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Theoretical Solutions of the Surround Rock Plastic Zone of the Borehole
### Effect of Hole Diameter on the Permeability-Increasing Circle of the Borehole Surrounding Rock  
The hole diameter of gas extraction has a significant impact on the permeability-increasing radius of the surrounding rock. Increasing the hole diameter is a crucial step toward increasing the rate of gas extraction. Figure 12 depicts the distribution pattern of plastic zones with borehole radii of 0.1 m, 0.3 m, 0.5 m, and 0.7 m. Figure 13 depicts the link between the surrounding rock permeability-increasing radius and the borehole radius.  
Figure 11: The permeability-increasing radius of the borehole surrounding rock and gas pressure.  
Figure 10: Distribution patterns of plastic zones in surrounding rocks of borehole holes under different gas pressures. (**a**) Round (**b**) Oval (**c**) Butterfly.  
If the confining pressure ratio, coal strength, and gas pressure remain constant, changing the hole diameter will not change the distribution pattern of the plastic zone. However, it will affect its size, and the original distribution pattern of the plastic zone will increase with increasing hole diameter. As shown in Figure 12. According to the study of Figure 13, there is a linear positive association between the surrounding rock's permeability-increasing radius and the hole diameter, which means that the permeability-increasing radius grows linearly as the hole diameter increases. When the mechanical qualities of coal are constant, and the gas pressure is constant, the confining pressure ratio dictates the rate of expansion of the surrounding rock's permeability-increasing radius with hole diameter. The permeability-increasing radius of the high surrounding rock ratio (such as the butterfly plastic zone) grows at a faster pace than the tiny confining pressure ratio (such as the circular plastic zone). When conditions allow, the hole diameter should be expanded as much as feasible to widen the permeability-increasing radius and generate a larger range of gas flow channels.
# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 4 Mapping and Coverage Analysis of TRIZ Rules in DIKWP
### Mapping the 40 TRIZ Principles to the DIKWP Model
#### 4.1.3 Data (D) \(\rightarrow\) Knowledge (K)  
The cognitive subject systematically analyzes Data semantics by identifying their similarities and differences, thereby organizing them into structured Knowledge and forming a logically coherent Knowledge system. Through this process, the cognitive subject not only categorizes and integrates disparate Data points but also establishes a comprehensive and interconnected framework of Knowledge. This structured organization facilitates a deeper understanding of the relationships and underlying patterns within the Data, ultimately contributing to the development of a cohesive and consistent Knowledge system that can support complex decision-making and reasoning processes. The following are several TRIZ principles related to this transformation process, along with their application examples:  
* **Principle 6: Universality** Extract general principles or models from specific Data points and extend localized insights to broader contexts. For example, in collaborative environments such as cross-functional software development teams, applying universal principles from one industry can enhance team Knowledge sharing and efficiency across different sectors [55].
* **Principle 24: Intermediary** Utilize intermediate models, such as simulations or analytical frameworks, as bridges between raw Data and refined Knowledge to facilitate the understanding and interpretation of complex datasets. For example, in financial risk management, intermediary models like stochastic differential equations are used to model economic scenarios and bridge the gap between complex raw Data and actionable insights [56].
# A Brazilian Space Launch System for the Small Satellite Market
###### Abstract  
At present, most small satellites are delivered as hosted payloads on large launch vehicles. Considering the current technological development, constellations of small satellites can provide a broad range of services operating at designated orbits. To achieve that, small satellite customers are seeking cost-effective launch services for dedicated missions. This paper deals with performance and cost assessments of a set of launch vehicle concepts based on a solid propellant rocket engine (S-50) under development by the Institute of Aeronautics and Space (Brazil) with support from the Brazilian Space Agency. Cost estimation analysis, based on the TRANSCOST model, was carried out taking into account the costs of launch system development, vehicle fabrication, direct and indirect operation cost. A cost-competitive expendable launch system was identified by using three S-50 solid rocket motors for the first stage, one S-50 engine for the second stage and a flight-proven cluster of pressure-fed liquid engines for the third stage. This launch system, operating from the Alcantara Launch Center, located at 2\({}^{\circ}\)20\({}^{\prime}\) S, would deliver satellites from the 500 kg class in typical polar missions with a specific transportation cost of about US$39,000 per kilogram of payload at a rate of 12 launches per year, in dedicated missions. At a low inclined orbit, vehicle payload capacity increased, decreasing the specific transportation cost to about 32,000 US$/kg. Cost analysis also showed that vehicle development effort would claim 781 work year, or less than 80 million dollars. Vehicle fabrication accounted for 174 work year representing less than 23 million dollars per unit. The launch system based on the best concept would, therefore, deploy small satellite constellations in cost-effective dedicated launches, 224 work year per flight, from the Alcantara Launch Center in Brazil.  
launch vehicle; small satellites; TRANSCOST model; Alcantara Launch Center 1
## 2 Methodology  
### Data Preprocessing  
The original videos were converted into YUV frames before they were encoded using a HEVC/H.265 video coder. We modified the coder to produce low-level features, which are discussed in this section. The HEVC codec is used to compress the videos; hence, rich feature sets can be extracted from it based on the quadratic recursive splitting of the coding units (CUs) in the HEVC. An overview of the process of acquiring the HEVC feature set is shown in Figure 2.  
CUs in HEVC can vary in depth from 0, which is typically equivalent to a maximum block size of 64 \(\times\) 64 pixels, to 3, which is equivalent to a block of 16 \(\times\) 16 pixels. The CUs are then split to prediction units (PUs) of a size from 4 \(\times\) 4 to 32 \(\times\) 32, which are then further split into transform units (TUs) of a size from 4 \(\times\) 4 to 32 \(\times\) 32. Figure 3 illustrates the partitioning scheme followed in HEVC coding. We based our feature vectors on the partitioning and prediction information found in the output bit streams.  
Figure 2: MPEG to HEVC video conversion process to extract HEVC features.  
For video summarization, we presented a set of 64 feature variables. The variables were chosen to quantify the spatio-temporal activity of the video frames. Table 1 presents a list of the variables. The feature variables in Table 1-A are averaged per frame, and the rest in Table 1-B are not. The tables use the abbreviations MVD for motion vector difference, SAD for the sum of absolute differences, and CU for coding unit.  
These features were chosen as they capture the spatio-temporal activities of the video frames; they also rely on motion estimation and compensation with previous video frames and thus preserve the temporal dependencies.
## 5 Conclusions  
In conclusion, our study delved into the photothermal and thermomechanical effects resulting from short-pulse laser irradiation on normal tissues. By analyzing a range of laser variables, including wavelength, intensity, beam size, and exposure time, we explored their influence on the heat-affected region within tissues. Utilizing a three-layered, three-dimensional model implemented in a polar coordinate system, numerical simulations were conducted using COMSOL Multiphysics.  
Integration of the Pennes bioheat transfer model, Beer-Lambert law, and Hooke's law facilitated the simulation of coupled biophysics phenomena, enabling the examination of temperature and stress distributions following laser radiation. Qualitative verification of our model's accuracy was achieved through comparisons of temperature and mechanical variations with relevant studies.  
Box-Behnken analysis revealed that beam size (S) exhibited negligible impact on response variables, with \(p\)-values exceeding 0.05. Temperature (T\({}_{\text{max}}\)) demonstrated sensitivity to both beam intensity (I) and exposure time (T), collectively contributing to 89.6% of the observed variation. Conversely, while beam size (S) showed no significant effect on stress value (S\({}_{\text{max}}\)), wavelength (W), beam intensity (I), and exposure time (T) collectively accounted for 71.6% of observed variation in equivalent stress (Smax).  
Our research envisions extending the photomechanical model to analyze tumor cell necrosis and drug delivery using shorter laser pulses, enhancing computational predictions, and refining blood perfusion data from imaging.  
In summary, this research advances our understanding of micro- and nano-scale heat transfer in biological tissues, offers promising applications in laser-based medical interventions, and provides deeper insights into thermal and mechanical interactions within living organisms.  
Software, formal analysis, validation: H.M.N.; conceptualization, methodology: B.A.; conceptualization, data curation, visualization, writing--original draft: H.D.; methodology, supervision, writing--review and editing: M.A.D.; data curation, supervision, writing--review and editing: D.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available upon reasonable request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Traffic Demand  
An OD matrix is an effective data form in the field of traffic planning with a wide range of applications. The OD matrix specifies the traffic demand between the origin and destination points in a network. The OD matrix data usually consists of two parts: one describes the distribution and numbering of points/areas in the road network, the other presents the volume of traffic from one point/area to another. The OD matrix used in this work contains 750 points/areas and covers an area over 800 km\({}^{2}\). Of all the areas, 596 are in Duisburg, and 153 are around Duisburg. The outer city areas contain the cities of Mulheim an der Ruhr, Oberhausen, Dinslaken, Hunxe, Wesel, Rheinberg, Kamp-Lintfort, Neukirchen-Vlyn, Moers, Krefeld, as well as the northern part of city Dusseldorf and Ratingen. The time length of the OD matrix is 24 h a day. The passenger cars and trucks are divided into two O-format files.
## 4 Results
### Validation of the Proposed Approach on the Emotional Speech Dataset (ESD)
The MLP also demonstrated strong individual performance, while the 1D-CNN and RF showed competitive results. Additionally, the cross-linguistic analysis suggests the potential for models trained on diverse English datasets to effectively classify emotions in other languages, although further investigation is needed to confirm this. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Overall PREC** & **Overall REC** & **Overall F1-Score** & **Overall ACC** \\ \hline \(1\)D-CNN & 0.87 & 0.87 & 0.87 & 0.87 \\ \hline SVM & 0.83 & 0.83 & 0.83 & 0.83 \\ \hline RF & 0.84 & 0.84 & 0.84 & 0.84 \\ \hline GNB & 0.43 & 0.41 & 0.39 & 0.41 \\ \hline MLP & 0.92 & 0.92 & 0.92 & 0.92 \\ \hline
**DBMM** & **0.98** & **0.97** & **0.98** & **0.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overall performance using multiple classifiers on ESD. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**(a) 1D-CNN** & \multicolumn{8}{c}{**(b) SVM**} \\ \hline  & **PREC** & **REC** & **F1** & **Support** & **PREC** & **REC** & **F1** & **Support** \\ \hline Happy & 0.88 & 0.77 & 0.82 & 699 & Happy & 0.75 & 0.78 & 0.77 & 699 \\ \hline Neutral & 0.9 & 0.88 & 0.89 & 723 & Neutral & 0.84 & 0.85 & 0.84 & 723 \\ \hline Sad & 0.89 & 0.94 & 0.92 & 692 & Sad & 0.87 & 0.87 & 0.87 & 692 \\ \hline Angry & 0.82 & 0.9 & 0.86 & 702 & Angry & 0.84 & 0.79 & 0.82 & 702 \\ \hline Surprise & 0.85 & 0.85 & 0.85 & 684 & Surprise & 0.84 & 0.84 & 0.84 & 684 \\ \hline
**ACC** & \multicolumn{8}{c}{0.87} & 0.87 & 3500 & **ACC** & \multicolumn{8}{c}{} & 0.83 & 3500 \\ \hline Macro Avg & 0.87 & 0.87 & 0.87 & 3500 & Macro Avg & 0.83 & 0.83 & 0.83 & 3500 \\ \hline Weight Avg & 0.87 & 0.87 & 0.87 & 3500 & Weight Avg & 0.83 & 0.83 & 0.83 & 3500 \\ \hline \multicolumn{8}{c}{**(c) RF**} & \multicolumn{8}{c}{**(d) GNB**} \\ \hline  & **PREC** & **REC** & **F1** & **Support** & **PREC** & **REC** & **F1** & **Support** \\ \hline Happy & 0.8 & 0.76 & 0.78 & 699 & Happy & 0.37 & 0.25 & 0.3 & 699 \\ \hline Neutral & 0.84 & 0.89 & 0.86 & 723 & Neutral & 0.39 & 0.84 & 0.53 & 723 \\ \hline Sad & 0.9 & 0.9 & 0.9 & 692 & Sad & 0.41 & 0.26 & 0.31 & 692 \\ \hline Angry & 0.82 & 0.8 & 0.81 & 702 & Angry & 0.57 & 0.3 & 0.39 & 702 \\ \hline Surprise & 0.84 & 0.84 & 0.84 & 684 & Surprise & 0.4 & 0.4 & 0.4 & 684 \\ \hline
**ACC** & \multicolumn{8}{c}{0.84} & 3500 & **ACC** & \multicolumn{8}{c}{} & 0.41 & 3500 \\ \hline Macro Avg & 0.84 & 0.84 & 0.84 & 3500 & Macro Avg & 0.43 & 0.41 & 0.39 & 3500 \\ \hline Weight Avg & 0.84 & 0.84 & 0.84 & 3500 & Weight Avg & 0.43 & 0.41 & 0.39 & 3500 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification results per emotion on ESD.
# A Noncontact Method for Calibrating the Angle and Position of the Composite Module Array
## 5 Conclusions  
The present study proposed a noncontact method for calibrating the angle and position of the composite module array. This method mainly used the self-developed angle sensor based on the principle of optical self-collimation and the established mathematical model to complete the calibration of the angle and position of the composite module array. In addition, the influence of array placement error on the calibration data was analyzed. With the help of laser interferometer, CMM and angle sensor, a series of experiments were completed which showed that the angle repeatability was within 0.4\({}^{\prime\prime}\) around the X-axis and within 0.3\({}^{\prime\prime}\) around the Y-axis, and the position repeatability was within 0.4 \(\upmu\)m in the X direction and within 0.7 \(\upmu\)m in the Y direction, exhibiting high repeatability. Through comparison experiments with a high-precision laser interferometer and uncalibrated array, the total RMS of angle measurement was 0.55\({}^{\prime\prime}\) and the total RMS of position measurement was 0.65 \(\upmu\)m, thus verifying the reliability of the calibration data, and indicating that the calibration array can greatly reduce measurement error. The composite module array has great potential in the field of freeform surface measurement and is of great significance for its calibration. This study contributes important reference data for the application of composite module arrays in practice and provides reference significance for the calibration of freeform surface arrays.  
[PERSON] and [PERSON] proposed the method and modified the paper; [PERSON]. ([PERSON]) designed the experiments and wrote the paper; [PERSON] and [PERSON] developed system software and designed optical structure; [PERSON], [PERSON] ([PERSON]), and [PERSON] helped [PERSON]. ([PERSON]) perform the experiments. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Natural Science Foundation of China (NSFC) (No.51775378), the National Key R&D Program of China (No.2017 YFF0108102), and the Natural Science Foundation of Shanxi Province, China (Grant No. 201801D121180).  
The authors declare no conflict of interest.
## 3 Noise Improvement Plan  
### Analysis of Noise  
The results of the active cancellation procedure in our previous study [8] are shown in Table 1. Test results showed that the primary source of noise, dust collector vibrations, was effectively decreased through the anti-vibrations produced by the AVC method. An approximately 11 dB(A) decrease in intensity of sound pressure was observed after implementation of the AVC method. In graph C of Table 1, which shows each result, the fast Fourier transform (FFT) analysis enlarges the frequency domain by log scale. Graph B shows the frequency components such as partials, overtones, and harmonics as a linear scale at a glance through FFT analysis.  
Figure 2: Location of recording microphone and sound level meter.  
amounts of frequency control. A complex tone, which is a sound with a timbre particular to the instrument, is composed of the lowest harmonic and its integer multiples [14]. As shown in Table 1, the frequency of a noise with a line spectrum of the characteristics of a milling machine has similarities to the timbre and harmonic structure of a general musical instrument. Therefore, we concluded that it would be possible to design a sound associated with this noise that contained musical qualities.
## 3 Methodology
### Modelling
#### 3.4.5 Artificial Neural Networks  
An artificial neural network (ANN) consists of several interconnected processing units that process information. It contains three types of layers: the first is called the input layer, then the hidden layer, and the last output layer. The transformation is carried out through the centred layer (hidden layer) between the input and output layer through units to detect complex patterns and learns accordingly. The idea of ANN working has been perceived by the working mechanism of the human brain. The brain consists of billions of neurons, and a single neuron is known as a perceptron, and each neuron is connected to others by axons. The neurons are finally connected with the synapses, which allow neurons to pass the signal. The neural network is formed with a large number of simulated neurons.  
Similarly, ANN contains multiple nodes in itself that are connected. The joining among units is denoted by weight. Inputs passed to the ANN consist of different values that are connected with weight vectors. The weight can be either positive or negative. For results generation, the function used to sum the weights and map to output is \(y=w_{1}x_{1}+w_{2}x_{2}\).  
ANN has been used for both supervised and unsupervised learning. This study applied supervised learning because the input and output were known and provided to the model. The model was tuned with different values to adjust the weights to the best to obtain the expected efficient output [35]. In multiclass classification, classifiers are used to predict multiple outcomes. In this study, a multiclass neural network was used to build a classification framework. Let us have \(K\) classes and want to classify one instance from one class. Then, the best choice is to use a linear neural network with multiclass classification. It is an extension of the binary classification setup. The second layer node will generate output as \(0\), \(1\)... \(K\)-\(1\). The basic working principle of a multiclass artificial neural network is shown in Figure 2.  
We have \(|w|=MK\), where \(M\) denotes the number of features and \(K\) represents classes. If \(K=3\) and \(M=3\), then the total weight will be formed as \(9\). To support the neural network view of multinomial logistic regression, we receive help from binary logistic regression (Equation (23)) as:  
\[P(Y=y|X=x_{i},w)=\frac{1}{1+exp(-yw^{T}x_{i})} \tag{23}\]  
In the case of \(K\) number of classes, we will have Equation (24):  
\[P(Y=k|X=x_{i},w)=\frac{\exp\left(w\frac{T}{k}x_{i}\right)}{\sum_{k^{\prime}} ^{K}\exp\left(w\frac{T}{k^{\prime}}x_{i}\right)} \tag{24}\]In the above equation, Y is the dependent variable representing a value we are trying to predict. The variables (_Xi = 1 to n_) are used to predict values for the dependent variable. W represents weight value, one for each data instance. It shows the strength and type of relationship with a particular data instance with Y. Larger values of weight represent a stronger relationship [42].
## 3 Machine Learning for IRS-Assisted Communication Systems
### Federated Learning for IRS-Enhanced Communication Systems  
In ML, FL has become the main point in the large-scale area and distributed optimization due to the exploration of training statistical models by a direct route on distant devices [102]. In the FL, the inaccessibility of personal data is no longer a problem. The FL algorithm is learned at the edge in allocated networks. The FL model can be deployed to design multiple IRS networks because of its privacy-preserving nature. In this network, IRS operates as a distributed trainer, trains its generated data, and also transfers it to local model parameters instead of the raw training dataset to an aggregating unit. In this way, FL can be learned in a decentralized manner for the deployment and design policy. Figure 9 shows an example of an FL-based IRS networking system. Each user has a local ML model trained by a local dataset that sends the learned parameter to the base station. Again, the base station trains a global model by having the parameters from users. In [103], authors proposed an IRS-assisted optimal beam reflection (OBR)-FL algorithm in the single receiving antenna communication system with multiple UEs to increase the data rate. The experimental results suggest that the achievable rate is similar to other centralized machine learning models and that there is no significant difference if the receiver number is changed. In [104], the authors proposed an air federated learning (AirFL) framework to solve resource allocation and device selection problems for aggregation accuracy enhancement and coverage rate improvement. According to the simulation results, the proposed model can converge faster, with a small training loss. In order to contribute to unmanned aerial vehicle (UAV) trajectory application, an MIMO system was designed with the proposed FL-CNN model, where the received pilot signals were used as the input data for the ML model [105]. For reliable channel estimation, the authors proposed that the model should be trained on 15 dB SNR and 5 bit quantization.