esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Minimum Acceptable Structural Pounding Risk \(P_{min}^{\mathrm{d_{g,t}}}\)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**EDP** & **PL 1** & **d\({}_{\mathrm{g,crit}}\) (cm)** & **d\({}_{\mathrm{g,t}}\) (cm)** & **d\({}_{\mathrm{g,t}}\) (cm)** & **P\({}_{\mathrm{dg,t}}^{\mathrm{PO}}\)** & **P\({}_{\mathrm{dg,t}}^{\mathrm{S_{max}}}\)** & **P\({}_{\mathrm{t,crit}}\)** & **P\({}_{\mathrm{min}}^{\mathrm{d_{g,t}}}\)** \\  & IO & 4.99 & 3.0 & 2.43 & 10.65 & 1.980 & 2.43 \\ \multirow{4}{*}{\(\mathrm{P_{min}^{\mathrm{d_{g,t}}}}\)} & \multirow{2}{*}{LS} & \multirow{2}{*}{7.59} & 3.0 & 0.68 & 10.65 & 0.498 & 0.68 \\  & & & 8.0 & 0.49 & 0.42 & 0.498 & 0.49 \\ \multirow{4}{*}{\(\mathrm{P_{min}^{\mathrm{d_{g,t}}}}\)} & \multirow{2}{*}{CP} & \multirow{2}{*}{14.43} & 3.0 & 0.14 & 10.65 & 0.060 & 0.14 \\  & & & & & & & \\ \multirow{4}{*}{\(\mathrm{P_{min}^{\mathrm{d_{g,crit}}}}\)} & \multirow{2}{*}{1.76} & 3.0 & 0.14 & 10.65 & 0.060 & 0.14 \\  & & & & & & & \\ \multirow{4}{*}{\(\mathrm{P_{min}^{\mathrm{d_{g,crit}}}}\)} & \multirow{2}{*}{1.76} & 3.0 & 0.09 & 0.42 & 0.060 & 0.09 \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Minimum acceptable structural pounding risk \(\mathrm{P_{min}^{\mathrm{d_{g,t}}}}\) at a targeted value of separation distance \(\mathrm{d_{g,t}}\). Figure 8: Acceptable structural bounding POs-\(\delta_{\text{max}}\) and POs-IDR\({}_{\text{max}}\) at Immediate Occupancy (IO) performance level. DHCs-IDR\({}_{\text{max}}\) at separation distances of d\({}_{\text{g,t}}\) = 3.0 cm, d\({}_{\text{g,t}}\) = 8.00 cm, and d\({}_{\text{g,crit}}\) = 4.99 cm and without pounding. Indeed, as can be observed in Figure 8c, the predicted IDR\({}_{\rm max}\) demand is 1.58 (%\(\delta_{\rm hst}\)) at P\({}_{\rm dg,t}^{\rm F_{\rm max}}\), which is greater than the capacity level of 1 %\(\delta_{\rm hst}\) and also greater than the corresponding demand in the case where bounding is not considered. However, the P\({}_{\rm t,crit}\) is an acceptable level of structural bounding risk at the capacity level of IO. Thus, for the separation gap distance d\({}_{\rm g,t}\) = 8.0 cm, the acceptable levels of structural bounding risk are within the range of P\({}_{\rm dg,t}^{\rm PO}-{\rm P}_{\rm t,crit}\) (\(1.76\cdot 10^{-3}-1.98\cdot 10^{-3}\)) (Figure 8c, Table 2). In a similar way, the minimum acceptable bounding risk P\({}_{\rm min}^{\rm d_{g,t}}\) is equal to \(0.68\cdot 10^{-3}\) at d\({}_{\rm g,t}\) = 3.0 cm (Table 2) when the LS performance level of the IDR\({}_{\rm max}\) is considered, and the corresponding P\({}_{\rm min}^{\rm d_{g,t}}\) is equal to \(0.49\cdot 10^{-3}\) at d\({}_{\rm g,t}\) = 8.0 cm (\(>\) d\({}_{\rm g,crit}\) = 7.59 cm). Finally, the performance of the eight-story RC frame in structural bounding conditions at the CP capacity level (IDR\({}_{\rm max}\) = 2.5 %\(\delta_{\rm hst}\)) is satisfied for P\({}_{\rm min}^{\rm d_{g,t}}\) = \(0.14\cdot 10^{-3}\) at d\({}_{\rm g,t}\) = 3.0 cm and for P\({}_{\rm min}^{\rm d_{g,t}}\)= \(0.09\cdot 10^{-3}\) in the case of d\({}_{\rm g,t}\) = 8.0 cm (Table 2).
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 5 Experimental Results
### Normalized Frame Level Scheduler (FLS) Algorithm  
* Approximate RBFNN Mode-FLS-Algorithm  
The normalized frame level scheduler (FLS) algorithm is a scheduling algorithm used in LTE networks. Figure 13 shows the comparison between the proposed algorithm that applies an approximate RBFNN model on the LTE FLS algorithm and the FLS algorithm; the graph indicates that the approximate RBFNN model is more accurate than the FLS schedule method by approximately 0.11%. However, the approximate RBFNN model is still less accurate than the FLS's raw data. The users in this experiment were 9, 21, 24, 27, and 30. The highest value of the users was listed as 3, 6, 12, 15, and 18, which is different from the raw data.
* Exact RBFNN Model Mode-FLS-Algorithm  
Figure 14 shows that the exact RBFNN (radial basis function neural network) model that was proposed for the FLS algorithm LTE network outperforms the FLS schedule algorithm by 0.011% in terms of accuracy. This is because the exact RBFNN model was able to predict optimal scheduling policies for all users in the experiment, and the results were the same when compared to the raw data in the FLS. It is important to note that the accuracy difference between the two algorithms may seem small (0.011%), but in practical applications, even small improvements in accuracy can lead to significant improvements in network performance and user experience.
* GRNN Mode-FLS-Algorithm  
Figure 15 shows that the GRNN-RBFNN (general regression neural network radial basis function neural network) model that was implemented for the FLS algorithm is more accurate than the FLS schedule algorithm by 0.030%. This indicates that the GRNN-RBFNN model is better than the FLS algorithm at predicting optimal scheduling policies for different users. It is also noted that the numbers of users involved in the experiment were 3, 6, 18, and 21, and their corresponding highest values were listed as 9, 12, 15, 24,  
Figure 12: Comparison of performing the EXP-PF model and GRNN model vs. the number of users.  
and 30, respectively. The results indicate that the fairness index for some users was lower compared to others. This suggests that the scheduling algorithm used may not be able to provide equal QoS to all users, and further optimization may be required to improve fairness. It is important to note that the results presented in Figure 15 are specific to the experiment or study being referred to and can be applied to other scenarios or settings.  
Figure 14: Comparison of performing the FLS model and exact RBFNN model.  
Figure 13: Comparison of performing the FLS model and approximate RBFNN model.
## 3 Results and Discussion
### Component Reliability  
It is necessary to calculate the reliability index of each slab segment under different damage cases of hinge joints before evaluating the system reliability. Then, the bending limit state function is established by Equation (14):  
\[Z=R-\lambda_{G}(S_{G1}+S_{G2})-\lambda_{Q}S_{Q} \tag{14}\]  
where \(Z\) is the limit state function; \(R\), \(S_{G1}\), \(S_{G2}\) and \(S_{Q}\) are random variables, represent the resistance, dead load, secondary dead load and live load, respectively; \(\lambda_{G}\) and \(\lambda_{Q}\) represent partial coefficients of dead load and live load, respectively, \(\lambda_{G}=1.2\), \(\lambda_{Q}=1.4\).  
The design values of these random variables are calculated separately according to Equations (15)-(18):  
\[R_{d}=f_{cd}b_{f}x(h_{0}-x/2) \tag{15}\]  
where \(f_{cd}\) is the compressive strength of concrete; \(b_{f}\) is the effective width of concrete flange; \(x\) represents the depth of compression; \(h_{0}\) is the effective height.  
\[S_{G1 kd}=\frac{g_{1}L^{2}}{8}, \tag{16}\]  
\[S_{G1 kd}=\frac{g_{1}L^{2}}{8}, \tag{17}\]  
where \(L\) is the calculated span; \(g_{1}\) and \(g_{2}\) represent the load intensity of dead load and secondary dead load, respectively.  
\[S_{Qkl}=(1+\mu)m(q_{k}Q_{k}+P_{k}y_{k}), \tag{18}\]  
where \(\mu\) is the impact coefficient; \(m\) is the load lateral distribution factor (LLDF); \(q_{k}\) and \(P_{k}\) represent the uniform load and the concentrated load, respectively; \(y_{k}=\frac{l}{4}\), \(\Omega_{k}=\frac{l^{2}}{8}\).  
Figure 4: Range analysis of the load lateral distribution factor (LLDF).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Case Number**} & \multirow{2}{*}{**Slab Segment Number**} & \multicolumn{6}{c}{**Dense Degree**} & \multirow{2}{*}{**Case Number**} & \multirow{2}{*}{**Slab Segment Number**} & \multicolumn{6}{c}{**Dense Degree**} \\ \cline{6-14}  & & & **Initial** & 20\%** & 40\%** & 40\%** & 80\%** & 10\%** & 10\% & \multicolumn{1}{c}  
Comparing Figure 4 with Figure 5, it can be seen that the variation tendency of the range analysis values of the reliability indexes and LLDF under different damage cases of hinge joints is basically the same. Different damaged hinge joints have different effects on the reliability index.
## 5 Simulation Results
### Simulation Results
#### 5.2.2 Performance Comparisons of Different Algorithms
Therefore, the approximation effect of MODECP is better than that of the genetic algorithm. Figure 3: Performance evaluation of different time periods after 0%, 5%, 10%, and 15% link failures in Bellcanada topology. (**a**) Performance of different link failures in terms of delay. (**b**) Performance of different link failures in terms of load rate. (**c**) Performance of different link failures in terms of link overhead.
# Numerical Simulation Analysis Method for Rockburst Control in Deeply Buried Caverns
## 3 Numerical Analysis Method for Rockburst Control with Anchor Rod Reinforcement
### Analysis of the Rockburst Control Effect of Anchor Rod Support
In turn, the support reaction limits the deformation of the surrounding rock, effectively alleviating the concentration of stress, reducing the release of elastic energy, and preventing the occurrence of rockburst.
## 3 Underlying Terminology and Concepts
### Fiducial Reference Measurements  
The need for a traceable uncertainty for satellite measurements is becoming a pressing requirement for the climate community to derive temporally consistent Climate Data Record (CDR) over multi-decade temporal frame [54; 55]. Within this context, the legacy methods for satellite products validation are not _fit-for-purpose_, owing to the lack of traceability, the disparity of protocols and procedures and the inadequacy of uncertainty budget estimation. For this purpose, the concept of Fiducial Reference Measurements (FRM), initially proposed for in situ radiometers measurements of sea surface skin temperatures [56], was generalised within the ESA S3 Validation Team as a set of guidelines to be followed in order to provide to the users the required confidence in satellite products validation. The FRM are defined as: \"The suite of independent ground measurements that provide the maximum Return on Investment (ROI) for a satellite mission by delivering, to users, the required confidence in data products, in the form of independent validation results and satellite measurement uncertainty estimation, over the entire end-to-end duration of a satellite mission\".  
The FRM is a generic concept, which applies to the validation of any satellite-based data product. The defining mandatory characteristics for FRM are the following (see Figure 1):  
* FRM measurements have documented SI traceability (e.g., via round-robin characterisation and regular (pre- and post-deployment) calibration of instruments using metrology standards (e.g., [56; 57; 58]).
* FRM measurements are independent from the satellite geophysical retrieval process.
* An uncertainty budget for all FRM instruments and derived measurements is available and maintained.
* FRM measurement protocols, procedures and community-wide management practices (measurement, processing, archive, documents, etc.) are defined, published openly and adhered to by FRM instrument deployments.
* FRM are accessible to other researchers, allowing for the independent verification of processing systems.  
The full compliancy to FRM requirements may be difficult for some terrestrial ECVs, owing to the lack of SI standards for the derived geophysical parameters and the limited knowledge on calibration uncertainties for some of the commonly used Cal/Val sensors. On the other hand, inter-comparison field campaigns and the reinforced focus on pre- and post-deployment calibration and detailed analysis of the error budget shall be addressed to move towards the FRM concept.
## 3 Experimental Results
### Development of the Statistical Model  
MINITAB 17 software was used to analyze the outcomes of the experiments. Statistical models were formulated to establish the connection between the process variables and their outcomes in terms of YS, UTS and % E using regression analysis. The formulated regression models are shown as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Tool Rotation** & **Weld Speed** & **YS** & **UTS** & **Facture** \\
**No.** & **Speed (RPM)** & **(mm/min)** & **(MPa)** & **(MPa)** & **Location** \\ \hline
6 & 1400 & 65 & 99.4 & 110.9 & 11 & SZ/TMAZ \\
1 & 1400 & 40 & 95.1 & 110 & 9 & SZ/TMAZ \\
5 & 1400 & 25 & 90.7 & 108.8 & 8.5 & SZ/TMAZ \\
7 & 1000 & 65 & 90.1 & 108 & 8.8 & SZ/TMAZ \\
2 & 1000 & 40 & 88.7 & 107.4 & 7.4 & HAZ \\
4 & 1000 & 25 & 86.5 & 105.2 & 7 & HAZ \\
9 & 710 & 65 & 87.2 & 106 & 7.2 & HAZ \\
3 & 710 & 40 & 87 & 104.8 & 6.3 & HAZ \\
8 & 710 & 25 & 85.4 & 102.1 & 5.4 & HAZ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental data of the tensile properties (YS, UTS, % E).  
Figure 6: Tensile test specimen (sample no. 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample No.** & **Tool Rotation Speed (RPM)** & **Weld Speed (mm/min)** \\ \hline
1 & 1400 & 40 \\
2 & 1000 & 40 \\
3 & 710 & 40 \\
4 & 1000 & 25 \\
5 & 1400 & 25 \\
6 & 1400 & 65 \\
7 & 1000 & 65 \\
8 & 710 & 25 \\
9 & 710 & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Design of experiments.  
* Yield strength (YS): \[\text{YS}=72.0+0.0126\ \times\ \text{Rotation Speed}+0.114\ \times\ \text{Weld Speed}\] (1) (b) Ultimate tensile strength (UTS): \[\text{UTS}=95.6+0.00808\ \times\ \text{Rotation Speed}+0.0695\ \times\ \text{Weld Speed}\] (2) (c) % Elongation (% E): \[\text{\% E}=0.819+0.00462\ \times\ \text{Rotation Speed}+0.0515\ \times\ \text{Weld Speed}\] (3)
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 4 Single-Modality Data Representation and Event Detection
### Text Based Data Representation
#### 4.1.2 Topic Model Based Methods  
To avoid the above problems caused by VSM representation, topic modeling, such as Latent Dirichlet Allocation (LDA) [32], Latent Semantic Indexing (LSI) [33] and Probabilistic Latent Semantic Indexing (PLSI) [34], is widely used in the next generation for topic (event) detection. In topic modeling, a document is represented as a probability distribution of a set of topics, and each topic is represented as a distribution of a set of topic words. The idea of the topic model is shown in Figure 4, in which \(\alpha\) and \(\beta\) are the distribution parameters of Dirichlet, \(\mathsf{w}\) is the word. \(\theta\), \(\mathsf{z}\), \(\varphi\) are the latent variables. K, M, N are the number of topics, number of documents and the number of words in a document, respectively. Topic models is an unsupervised model and it can extract semantic information from text effectively. Topic model had achieved great success on text representation and many researchers had applied topic model on topic detection task. For example, [PERSON] and [PERSON] [35] proposed a supervised LDA model that leveraged the documents information to obtain better learning process and achieved better representation of documents. [PERSON] et al. [36] proposed to use Dirichlet forest priors to incorporate domain knowledge into topic modeling. In addition, [PERSON] et al. [37] used hierarchical topic model to mine topic hierarchies and finer grained topics. [PERSON] et al. [38] proposed a multifaceted news analysis methods for the online news events search. [PERSON] et al. [39] provided a new representation of news articles and news events by using probability model that combined content and time information. It can be seen from above researches that the probabilistic topic model method has achieved excellent performance on event detection tasks.
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 6 Discussion
### Analysis of Results When Only Video Traffic Is Exchanged  
The obtained results confirmed the results of the past study when Asterisk FreePBX was attacked with various TCP DoS attacks. From the current study, it was found that the UDP DoS attacks do not affect the parameters of the VoIP video streams. During the attack, the packet loss increased to 0.06% (with an allowable 1% packet loss), which is more compared to the study when only voice streams were exchanged. This is normal because when video traffic is exchanged, the amount of information that is exchanged is much more than when only voice traffic is exchanged. The average jitter is also very far from the allowable 30 ms.  
During this study, the UDP DoS attack did not result in the blocking of the Asterisk. Despite the server being flooded with UDP packets, the system functioned normally. Figure 40 represents what the CPU utilization of Asterisk was for the day when only video streams were exchanged--for normal mode and during the UDP DoS attack. As can be seen from the graph, the average CPU utilization is low--about 13%.  
Figure 38: Asterisk’s CPU load for the whole voice call study period.  
Figure 39: Asterisk’s memory load for the whole voice call study period.  
Figure 41 shows what the memory load was for the day. As can be seen from the graph, only 14% of the memory is in use, the rest of the memory is free. As can be seen from the graph, there is no significant difference in the memory load when Asterisk is attacked and when it is not attacked--always only 14% of the memory is in use.  
Again, during this study, the ping command is successful. Asterisk's computational capabilities are not blocked.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 4 Experimental Setup
### Indicator Extraction
#### 4.4.4 ECG Data  
The ECG signal processing workflow is illustrated in Figure 4. After preprocessing to remove noise, the process begins with a first-order difference using Formula 1 to identify all inflection points of the ECG signal. Then, a second-order difference is applied using Formula 2 to calculate all the peaks of the ECG signal. Finally, the peak threshold is determined using Formula 3 to identify the peak points of the R-waves. Thus, the RR intervals are obtained, allowing for the calculation of the average heart rate every 10 s. Statistical results indicate that the average heart rate (denoted as HR) of each group has a strong positive correlation with the scores of subjective scales (r > 0.8), suggesting that it can effectively indicate the magnitude of workload in flight tasks.
###### Abstract  
The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used.  
Keywords:spatial dimensional push-broom hyperspectral imaging system; hyperspectral brain tumor data; image denoising; non-uniformity correction +
Footnote †: journal: Applied sciences
## 3 The Proposed Method
### Feature Fusion
#### 3.2.2 Late Fusion  
In late fusion, text and image features are extracted and then combined for classification. This is also known as decision-level fusion [49]. It integrates different model predictors by a fusion mechanism to come up with the final decision. Late fusion can be expressed as follows: \(output=\text{g}(f_{1}\ (x_{1}),\ \dots,f_{n}(x_{n}))\), where functions \(f_{1},\ \dots\ f_{n}\) are applied to individual features and function \(g\) is used to aggregate the individual decisions by \(f_{1}\),... \(f_{n}\). The main disadvantage of late fusion is that it cannot learn the correlation among features. Compared to early fusion, late fusion tends to be more robust to features that have negative influence [36]. The architecture is shown in Figure 4.  
As shown in Figure 4, after embedding texts and images into vectors by Word2 Vec and RGB models, respectively, they are input to two separate CNN models for feature extraction as described in Section 3.1. The corresponding feature map and text/image category can be obtained. Then, to fuse text and image features, in addition to using a fully connected layer, we also try to combine the two feature maps by inner product since they are of the same size. The idea is to increase the correlation and reduce the dimension through the inner product.
## 2 Materials and Methods
### Evaluation Setup
We evaluated the performance of the proposed method using four performance metrics: sensitivity (Se), which represents the ability of the algorithm to detect true R peaks; positive predictivity rate (PPR), which represents the ability of the algorithm to discriminate between true and false R peaks; detection error rate (DER), which is used to evaluate the total detection error over all R peaks; and accuracy (Ac), which is calculated as the proportion of correctly identified R peaks over all cases. \[\text{Se}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\% \tag{4}\]  
\[\text{PPR}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\% \tag{5}\]  
\[\text{DER}=\frac{\text{FP}+\text{FN}}{\text{TotalQRS}}\times 100\% \tag{6}\]  
\[\text{Ac}=\frac{\text{TP}}{\text{TP}+\text{FN}+\text{FP}}\times 100\% \tag{7}\]  
A true-positive (TP) occurred when an R peak was correctly detected by the proposed algorithm, whereas a false-negative (FN) occurred when an R peak was missed, and a false-positive (FP) occurred when noise was detected as an R peak. Figure 6: Occurrence of the signal noted as an “isolated QRS-like artefact” in record #I6272 [12; 13; 15]. Tables 5 and 6 present a comparison of the results obtained by the proposed method and other methods [16; 17; 18], from which the proposed method obtained the highest performance. The average elapsed time for the R peak detection on the MIT-BIH NSR database was approximately 385 ms per 10,000 s at 1000 samples per second, resulting in a processing time per sample of approximately 38.5 ns. When tested using the Fantasia database, the total time for R peak detection over 249,746 s of recording was 12,672 ms at 1000 samples per second, resulting in a processing time per sample of approximately 36.2 ns.