esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Output of the Multibody Model  
Findings have confirmed the relevant role of the coefficient of friction on the system performance and revealed a linear (R\({}^{2}\) = 0.99) relationship between this coefficient and the maximum required power. For sake of clarity, since the main objective of the multibody analysis was to minimize the aforementioned power, only the case involving the lowest coefficient of friction of 0.15 is reported in the following.  
The main outputs of the multibody model are reported in Figure 7: the higher the inclination, the smaller the unlocking force, and the higher the locking force; the higher the spring pre-load, the higher the unlocking and locking forces. On the whole, the minimization of the unlocking power that is the main objective of this analysis would require a higher plane inclination (18\({}^{\circ}\)) and a minimum spring pre-load (20 N), able to guarantee pin motion, overcoming friction forces between the pin and the respective hole. In the last conditions, the required power to unlock the plate is equal to 18 mW, while re-locking would require above 50 mW; as such, this possibility was discarded for the time being, which meant that the plate could be dynamized, but it could not revert to its locked configuration even if the required force would fall within specifications (20 N).
## 2 Materials and Methods
### Fuzzy Logic Controller  
Artificial intelligence is associated with intelligence in human behavior, such as learning, reasoning, and problem solving. An example is the use of fuzzy logic, which facilitates the transfer of knowledge between domains by referring to the closest solutions of the most similar cases. It has been demonstrated in industry that the application of a method based on fuzzy logic techniques is very beneficial, for example, in fault detection [26; 27].  
Fuzzy logic interprets linguistic knowledge into control rules and uses set theory for decision-making [28]. An FLC is developed in three steps [29; 30].  
Fuzzification: The value of input variables is converted into membership functions based on intuition or deduction.  
Fuzzy rules are described using inference methods; an example is the Mamdani method described in (19).  
\[If\ \ \ x_{1}=A_{1}\ \ and\ \ \ x_{2}=A_{2}\ \ then\ \ y=B \tag{19}\]  
Defuzzification: The fuzzy set is converted to a real value by methods that satisfy mathematical expressions such as: Centroid, height, mean of maximum, centroid of area, center of sums, or bisector.  
Figure 3: Gain values for different response criteria.  
The proposed FLC consists of determining the PID gain values for the pitch angle controller, so that the rotor rotation speed is consistent with the variability in the wind speed. The fuzzy weights and rule generation are determined by a subject matter expert based on the influence of the wind force on the rotational speed of the turbine and historical wind behavior data.
## 3 Hybrid Method
### GPU Kernel Routines
#### 3.3.2 Key-Equation Kernel  
The KEK is the only single thread routine, in our proposal, because of the iterative nature of the iBM algorithm. Algorithm 2 represents the pseudocode for iBM implementation in the GPU routine. There are other methods like the simplified iBM (siBM) algorithm [7] proposed for the key-equation solver module, but experimental results have proven that siBM does not have significant improvement on the performance of the GPU kernel routines.  
Figure 5: Decoder execution sequence. PG, Page.  
```
1:procedurekeqeqkernel(\(\Lambda,S\))
2:\(\Lambda^{(0)}\gets 1+S_{1}x\)
3:if\(S_{1}=0\)then
4:\(d_{p}\gets 1;\beta^{(1)}\gets x^{3};l_{1}\gets 0\)
5:else
6:\(d_{p}\gets S_{1};\beta^{(1)}\gets x^{2};l_{1}\gets 1\)
7:endif
8:for\(r\gets 1,t-1\)do
9:\(d_{r}\leftarrow\sum\limits_{i}^{r}\Lambda_{i}^{(r)}S_{2r-i+1}\)
10:\(\Lambda^{(r)}\leftarrow\underset{d_{p}}{\overset{\leftarrow}{\leftarrow}}d_{p} \Lambda^{(r-1)}+d_{r}\beta^{(r)}\)
11:if\(d_{r}=0\,or\,r<l_{r}\)then
12:\(\beta^{(r+1)}\gets x^{2}\beta^{(r)};l_{r+1}\gets l_{r};d_{p}\gets d _{p}\)
13:else
14:\(\beta^{(r+1)}\gets x^{2}\Lambda^{(r)};l_{r+1}\gets l_{r}+1;d_{p} \gets d_{r}\)
15:endif
16:endfor
17:endprocedure
```  
**Algorithm 2** Key-equation kernel.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 3 System Model and Problem Formulation
### Channel Modeling for UAV-Assisted Non-Terrestrial Networks
#### 3.3.1 Direct Path  
Let \(h_{d}\in\mathbb{C}\) denote the complex channel coefficient for the direct path between the NTN node and the user. This coefficient accounts for three key propagation effects: path loss, large-scale shadowing, and small-scale fading. It is modeled as  
\[h_{d}=\underbrace{\sqrt{\text{PL}_{d}}}_{\text{Path loss}}\cdot\underbrace{ \sqrt{10^{\text{Sh}_{d}/10}}}_{\text{Shadowing}}\cdot\underbrace{\tilde{h}_{d} }_{\text{Fading}}, \tag{2}\]  
where \(\text{PL}_{d}=\left(\frac{\lambda}{4\pi d_{d}}\right)^{2}\) represents the path loss for the direct link, with \(\lambda\) denoting the carrier wavelength and \(d_{d}\) the distance between the NTN node and the user. The variable Sh\({}_{d}\) captures the shadowing effect, which is typically modeled as a log-normal random variable. Finally, \(\tilde{h}_{d}\) represents the small-scale fading component, which is modeled as a complex Gaussian random variable to account for the multipath nature of the wireless environment.
## Nomenclature  
List of Abbreviations  
AEMO  
Australian Energy Market Operator  
ACE  
Area Control Error  
BS  
Black Start  
CIGRE  
Conseil International des Grands Reseaux Electriques  
(International Council on Large Electric Systems)  
DER  
Distributed Energy Resources  
DSO  
Distribution System Operator  
DTS  
Dispatcher Training Simulator  
EAS  
European Awareness System  
ENTSO-E  
European Network of Transmission System Operators for Electricity  
HI  
Higher Importance  
LI  
Lower Importance  
LS  
Load Shedding  
NBS  
Non-Black Start  
OLTC  
On-Load Tap Changer  
PMU  
Phasor Measurement Unit  
Purnsylvania New Jersey Maryland  
RCC  
Regional Coordination Centers  
RSC  
Regional Security Coordinators  
TSO  
Transmission System Operator  
RES  
Renewable Energy Sources  
RG-CE  
Regional Group Continental Europe  
SCADA  
Supervisory Control and Data Acquisition  
UCTE  
Union for the Coordination of the Transmission of Electricity  
ULTC  
Under Load Tap Changing  
List of Symbols  
B  
Total number of buses  
b  
Bus number  
C\({}_{G_{m}}\)  
The maximum output active power of unit m  
F\({}_{1}\)  
The first objective function (incorporates the total restoration time)  
F\({}_{2}\)  
The second objective function (minimization of load shedding)  
L  
Total number of loads.  
\(\ell\)  
Number of load  
M  
The number of BS units  
\(N_{g}\)  
The total number of the restored units  
\(N_{b}^{l}\)  
The number of the restored transmission lines at t-th step  
\(N_{m}^{l}\)  
The number of restored buses at t-th step  
\(o_{m}\)  
The coefficient of the minimum technical output of the unit m.  
\(P_{h}\)  
The active load on bus b  
\(Q_{D_{m}}^{l}\)  
The reactive load of bus m at t-th step  
The reactive power absorbing capability of the restored unit n  
r\({}_{\text{b}}\)  
Variable indicating there is a PMU unit  
T  
The programming horizon.  
t  
The set of the time period  
\(T_{H_{m}}\)  
The maximum hot start time of unit m  
\(T_{L_{m}}\)  
The minimum cold start time of unit m  
\(T_{G_{m}}^{*}\)  
The startup time of unit m at the t-th step  
\(\tau_{t,b,t}\)  
Time that the load (\(\ell\)) of the bus (b) is not supplied.  
W  
The number of subsystems  
\(z_{\text{bj}}\)  
Binary decision variable  
\(\Omega_{E(S)}\)  
The energized block of grid elements that has been restored
# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 5 Simulation Experiment Results and Analysis
### CFKLQ-CFAR Detection Performance
#### 5.1.1 Monte Carlo Simulation Experiment
In various environments, the performance of the CFKLQ-CFAR detector is superior to the above comparison methods, demonstrating good detection robustness.The average detection probability of each detector in different environments with SNR ranging from 0 dB to 35 dB is shown in Table 3.  
Figure 2: The detection probability of each detector:when (**a**) in homogeneous environment, (**b**) there is one interference in the leading reference window, (**c**) there are three interferences in the leading reference window, (**d**) there is one interference in both the leading and lagging reference windows. As shown in Table 3, compared with KLQ-CFAR, the average detection probability of CFKLQ-CFAR increased by 1.05% in a homogeneous environment, 2.67% in an environment with one interference in the leading window, 2.71% in an environment with three interferences in the leading window, and 2.76% in an environment with one interference in both windows. The time for KLQ to perform a target recognition is 0.5055 seconds, and that for CFKLQ-CFAR is 0.5098 seconds. Compared with KLQ, CFKLQ has improved performance, but the computation time has increased by 0.0043 seconds. In order to test the impact of different weights on the detection performance of CFKLQ-CFAR, the performance of CFKLQ-CFAR with different weights is tested in a multiple-target environment with one interference in both windows, as shown in Table 4.  
As shown in Table 4, different weights correspond to different average detection probabilities of CFKLQ-CFAR. When the weights assigned to the four fusion methods are [0.25, 0.25, 0.25, 0.25], the average detection probability of CFKLQ-CFAR is 55.41%, which has poor detection performance. Because among the four fusion methods, MINKLQ-CFAR and APKLQ-CFAR have poorer detection performance than the other two methods, reducing the weights of MINKLQ-CFAR and APKLQ-CFAR and increasing the weights of MAXKLQ-CFAR and ASKLQ-CFAR. When the weight distribution is [0.5, 0, 0, 0.5], the average detection probability is 55.60%, which improves the average detection probability of CFKLQ-CFAR. Because ASKLQ-CFAR is better than MAXKLQ-CFAR, the weight of ASKLQ-CFAR is increased and the weight of MAXKLQ-CFAR is reduced. When the weight distribution is [0.4, 0, 0, 0.6], the average detection probability is 55.70%. When the weight distribution is [0.1, 0, 0, 0.9], the average detection probability is 56.02%. However, when the weight distribution is [0.08, 0, 0, 0.92], the average detection probability decreases to 55.93%. When the weight distribution is [0.12, 0, 0, 0.88], the average detection probability is 55.98%. Therefore, the experiment shows that when the weight distribution is [0.1, 0, 0, 0.9], it has better detection performance.
# Immuno-PET for Glioma Imaging: An Update
## 2 Gliomas
### Clinical Management
#### 2.3.2 Radiotherapy  
The role of radiotherapy in gliomas has been long debated. The best way to employ radiation therapy for this particular population has not yet been determined by clinical trials conducted in the last ten years. The important points to be decided by clinicians are: the timing of use of ionizing radiations, their optimal dose and which chemotherapy represents the best concomitant or sequential active treatment. In order to improve life expectancy, radiotherapy is currently considered fundamental in gliomas' clinical management. Many studies have been carried out and are currently ongoing, seeking to understand which is the best timing in the postoperative setting--either early, immediately following surgery, or later, when the illness recurs. Both early and delayed treatment are appropriate at this time, given the challenging balance between reducing radiation-induced side effects and increasing tumor control by delaying recurrence. Patients with LGG often receive radiation doses of 45-54 Gy in 1.8-2 Gy fractions; these dosages represent a fair trade-off between tumor management and reducing neurological adverse effects [17; 18; 19]. Moving on to HGG, an initial brachytherapy treatment using local sources of 125I may cause a tumor shrinking that will eventually allow for surgery in those patients where surgical intervention is initially impossible due to the infiltration of key neurological areas. Studies conducted in vitro have demonstrated that dividing the overall dosage of radiation therapy into fractions ten times smaller than the typical 2.0 Gy (also known as ultra-hyper-fractionated radiotherapy, or ultra-hyper FRT) significantly increases the therapeutic efficiency of the radiation towards HGG cells [20].
# An Integrated Decision Support System for Low-Disturbance Surface Mining
## 1 Introduction
By comparing it with the ecological carrying capacity of the mining area, the degree of sustainable development of the mining area is judged. (ii) The theoretical system of disturbance index [17; 18; 19] for open pit mining was successively proposed and continuously developed by Chinese scholars. The theory is based on the mining disturbance effect evaluation theory and control technology and reveals the disturbance mechanism of open pit mining activities on the external elements of the mine by defining the open pit mining disturbance index. By studying the degree of disturbance, the trend of change of disturbance effect, and the compensation mechanism of disturbance, it guides open-pit mines to realize green and high-efficiency mining. The above studies mainly focus on the quantitative expression of the evaluation indexes of low-disturbance mining in open pit mines, and no systematic theory has been formed on the relationship between the low-disturbance indexes of open pit mines and the optimization path. A well-developed metrics framework system can help mining companies implement LDM. Therefore, the ultimate goal of this paper is to propose a framework of indicators for low-disturbance mining decision-making systems to provide technical assistance for low-disturbance mining in open pit mines. The framework focuses on the five main processes in the production of an open pit mine, which are perforation, rock blasting, extraction and loading, ore transportation, and rock drainage. The proposed framework is therefore divided into four leading indicators, namely \"Control of stripping works (CS)\", \"Control of emission rock (CE)\", \"Control of pollution emission (CP)\", and \"Control of drainage (CD)\". There are 13 sub-indicators under the leading indicator. All indicators were assessed in terms of both the current state of the LDM indicators (reality) and the significance in the development of LDM (desirability). Unlike previous studies [17; 18; 19] that focused only on the weight and ranking of each indicator, we used a combined method of fuzzy hierarchical analysis and grey clustering. A method that first quantifies the indicators by determining their weights evaluates and analyzes each indicator and then constructs a hierarchical system of frameworks for the challenges that impede the development of disturbed open-pit mines. In response to the leading indicator challenges, their optimization paths were ranked separately, and finally, a decision support system for low-disturbance mining in open pit mines was constructed to facilitate the transition of the traditional open pit mining industry to low-disturbance open pit mining development.
## 2 Proposed Method and Environment
### Deep Learning Network  
The deep learning neural network architecture proposed in this study was based on VGG19 [6]. VGG19 is a model that is widely used as a basic deep learning method because it is relatively easy to implement and modify because it uses only 3 \(\times\) 3 convolutional layers. In this study, the number of parameters was reduced using Global Average Pooling (GAP) to eliminate the Fully Connected Layer (FCL), which is one of the parts of VGG19 that requires a large number of computations, and to match with the output layer. The deep learning architecture was constructed, as shown in Figure 4 (Down).  
The size of the spectrogram images in Figures 6 and 7 used as training data was changed by converting a rectangular shape (432, 288) to a square shape (298, 298) before using it in the experiment. For convergence of the learning errors, an attempt was made to find the global minimum error using the learning scheduler [5], which changes the learning rate each epoch. The other hyperparameters were set, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper Parameter** & **Value** \\ \hline Learning Rate & 0.001 \\ Batch Size & 4 \\ Warm-up Train phase & 10 \\ Weight Decay & 0.0001 \\ Optimizer & SGD (Stochastic Gradient Descent) \\ Epoch & 200 \\ Early Stopping patience & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters used in model training.  
Figure 7: Example of signal changes in the spectrograms of the Mel Frequency Cepstral Coefficients (MFCC) signals.  
An initial learning rate of 0.001 was set for faster training speeds. A batch size of four was used to set the maximum batch size in the environment to speed up learning. The early 10 epochs were used to warm-up [46] the training phase and adjust the learning rate according to the complexity of the training data. The first 200 epochs were used for a more robust model. Lastly, to avoid overfitting, an early stopping [47] technique was introduced based on the verification data, and the patience was set to 10.
## 4 Results and Discussion
### Effectiveness Analysis of Students' Coding Literacy
#### 4.2.2 Analysis of Students' Computational Thinking Abilities  
This study examined the students' computational thinking abilities. Five types of questions were asked, including abstraction, problem decomposition, algorithm thinking, pattern recognition, and debugging. The analysis results are shown in Table 5.  
The \"Levene test equation of error variance\" was first examined, and the F values were 0.030 (\(p\) = 0.862), 1.369 (\(p\) = 0.244), 1.150 (\(p\) = 0.258), 0.539 (\(p\) = 0.464), and 1.710 (\(p\) = 0.357). The significance level was greater than 0.05, indicating that there was no significant difference in the error variation of the two groups according to the variables, and the two groups were thus homogeneous. Therefore, covariate analysis could be performed. After reviewing the \"test of the inter-subject effect items\", it was found that the t values of \"abstraction\" and \"problem decomposition\" were \(-0.743\) (\(p\) = 0.459) and \(-0.910\) (\(p\) = 0.364), respectively, and that there was no significant difference. The t values of \"algorithm thinking\", \"pattern recognition\", and \"debugging\" were \(-2.121\) (\(p\) = 0.035), \(-2.016\) (\(p\) = 0.046), and \(-0.211\) (\(p\) = 0.036), respectively, and the significance of each of the values was less than 0.05, indicating a significant difference. The adjusted average of the experimental group's \"algorithm thinking\" was 4.67, which is higher than the control group's 4.43. The adjusted average of \"pattern recognition\" was 4.54, which is higher than the control group's 4.32, and the adjusted average of \"debugging\" was 4.57, which is higher than the control group's 4.29.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & **Mean** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Adjusted Mean** &
\begin{tabular}{c} **Adjusted** \\ **Standard** \\ **Deviation** \\ \end{tabular} & \multicolumn{2}{c}{**Effect Item Test**} \\ \cline{2-7}  & 4.09 & 0.804 & 4.11 & 0.081 & \(-1.381\) & 0.169 \\ \cline{2-7}  & 4.33 & 0.758 & 4.30 & 0.109 & & \\ \hline Simple judgment & 3.94 & 0.931 & 3.97 & 0.094 & \(-1.830\) & 0.069 \\ \cline{2-7}  & 4.32 & 0.893 & 4.26 & 0.126 & & \\ \hline Multi-condition judgment & 4.06 & 0.773 & 4.10 & 0.076 & \(-2.037\) & 0.043 \\ \cline{2-7}  & 4.43 & 0.693 & 4.36 & 0.102 & & \\ \hline Repetitive structure & 4.21 & 0.731 & 4.23 & 0.071 & \(-2.039\) & 0.043 \\ \hline Comprehensive application & 4.52 & 0.638 & 4.48 & 0.095 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of covariance (ANCOVA) summary of students’ programming abilities.  
The results showed that the experimental student group exhibited a significantly better computational thinking performance in \"algorithm thinking\", \"pattern recognition\", and \"debugging\" in the DBL Arduino project course than the control group. This is consistent with the results of [PERSON], [PERSON], [PERSON], and [PERSON] [7]. However, there was no significant difference between the two groups in \"abstraction\" and \"problem decomposition\".
## 5 Discussion
### Convergence Observations--Number of Simulations
* Wide span: the purple curve shows a modest left shift (\(\mu\approx 1.62\)) and a slight narrowing; monitoring data begin to influence the FS estimate but the baseline analysis variability still dominates (confirmed by low KL = 0.13 nats). * Very-wide span: the orange curve is practically indistinguishable from the blue baseline (KL \(\approx 0\)); weighting is neutral when the monitoring span covers the full baseline analysis range. Figure 21: Axial-strain \(\epsilon_{1}\) probability densities: baseline analysis versus weighted analyses with different monitoring spans. KL divergence values are presented in the legend. Figure 22: Factor of safety (FS) probability densities: baseline analysis versus weighted analyses with different monitoring spans. KL divergence values are presented in the legend. A narrow monitoring PDF distribution span lowers the estimated safety level (by up to 14% relative to the baseline \(\mu=1.69\)) and tightens its dispersion; these results reflect the variability of the (lower) effective friction angle mobilized at the high confinement. As the span widens, the results eventually revert to the baseline values. Combined with the \(\varepsilon_{1}\) results (Figure 21), it is confirmed that a 1% span offers the best compromise: it meaningfully updates the safety prediction (KL divergence is high for both \(\varepsilon_{1}\) and FS), trims the variance and remains computationally tractable with \(10^{4}\) simulations.
# Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods
### Objectives and Contributions  
The aim of this study is to present a systematic approach to analyzing AIS resilience, along with an analysis of relevant scientific publications based on the proposed taxonomy and ontology of resilient AISs, as well as the recognition of the main trends in the theory and practice of resilient AIS development.  
The key objectives are as follows:  
* analysis of existing threats and vulnerabilities of the AIS;
* construction of a taxonomic scheme of AIS resilience;
* building the AIS resilience ontology;
* analysis of the existing models and methods of ensuring and assessing the resilience of the AIS;
* determination of future research directions for the development of the theory and practice of resilient AIS.  
Structurally, the work consists of the following sections:  
A description of the research methodology is given in Section 2. The analysis of vulnerabilities and threats, taxonomic and ontological schemes of resilience of AIS are presented in Section 3. Section 4 present applications of AIS that require resiliency and relevant threat examples. Section 5 presents the analysis of models and methods which ensure the resilience of AIS. The research results are discussed in the Section 6. Section 7 contains the concluding summary and research limitation, and highlights promising areas for future research.  
The main contribution of this review includes taxonomic and ontological schemes of resilience of artificial intelligence systems, as well as proposals for defining the concept of resilience and resilient AI. In addition, the existing and proposed new methods of measuring and certifying the resilience of the artificial intelligence system to the complex impact of destructive factors are considered.