esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Formulation and Flowchart
### Flow Chart  
Figure 8 shows the flow chart of the process. In short, the flow chart shows how the geometry of the arch is obtained iteratively as a function of its self-weight \(g_{A}\) and the concentrated loads transmitted by the hangers, \(T_{i}\). The actions at the hangers can be obtained in two alternative ways depending whether non-linearity is considered or not (steps \(a\) and \(b\) of the flowchart). In the simplest case described in Figure 9, the deck is assumed to behave as a simply supported continuous girder. An auxiliary FEM model of only the deck is needed, which is supported at the locations of the anchorages of the hangers. The axial loads at the hangers \(T_{i}\) can be calculated because their vertical components are equal to the vertical reactions \(Rz_{i}\) at the supported nodes of the auxiliary FEM model. The calculations of \(Rz_{i}\), i.e., the output of the FEM model, is needed only in the first iteration because they remain unchanged during the iterative process. However, when non-linearity is considered, the auxiliary FEM model must include the whole structure (deck, arch, and hangers) and the axial loads must be obtained from the output of the FEM calculation at every iteration because this model must be redefined at each iteration as the geometry of the arch changes.  
Figure 7: Effect of \(Fx_{i}\) on \(N_{x,i}\)  
Figure 8: Simplified Forced Density Method: Flow chart.  
Figure 9: Actions upon the arch transferred by hangers.  
The additional effect of the self-weight of the arch is easily considered by assigning an additional load \(Fz_{i}\) at every point \(i\) of the arch equal to the weight of its tributary length of arch.  
Once the actions are determined, the form-finding formulation defined in Section 3.1 is applied. The form-finding algorithm (step c of flowchart) is iterative because the value of \(H\) (horizontal reaction at one springing of the arch) must be successively corrected in order to make the centroidal line of the arch pass through a given point (\(x_{C},z_{C}\)) with an convergence error smaller than \(\varepsilon\).  
The process ends (i.e., the convergence is achieved, step d of the flowchart) when the difference between all the coordinates of the nodes of two successive iterations of the arch's geometry (Shapes \(j\) and \(j-1\)) is smaller than a tolerance \(tol\) previously defined. Figure 10 illustrates this iterative process including only the hangers corresponding to the last iteration.
## 2 Materials and Methods
### Carbon and Nitrogen Removal Study  
#### 2.2.1 Experimental Design  
Four parallel bench-scale wetland systems, each with three columns, were used in the Carbon and Nitrogen Removal Study. Each wetland system simulated an SVFCW, and each column represented a cell. The first cell was the Roughing Cell, the second was the Denitrification Cell, and the third was the Polishing Cell. Generally, the final cell is not required for acceptable treatment in a SVFCW, but it is included to provide operational flexibility in a full-scale system and remove the residual carbon and nitrogen. Only the surface area of the cells was scaled down, resulting in 1.2-m (four-foot) tall columns with the inlet 0.45 m (1.5 feet) below the top of the column (representing ground level). The diameter of the columns was scaled down to 10.16-cm (four-inches); prior studies have shown that column to particle diameter ratios should be at least 10 to minimize the wall effects [59].  
Influent winery wastewater from a settling tank was pumped to an elevated reservoir and then flowed via gravity into the first column of each system through a barbed inlet fitting, 0.45 m (1.5 feet) below the top of the column. This column was open to the atmosphere, passively aerated, and assumed to be aerobic [60; 61; 62]. The effluent wastewater from the first column was then either pumped back into the inlet of the first column (recycled) or into the bottom of the second column. The recycling ratio was maintained at 3:1, with three times the hydraulic load going into the first column as the second. Filling the second column from the bottom resulted in water saturation; therefore, it was assumed that an anoxic environment developed within the second column due to aerobic microbial activity near the inlet. Effluent wastewater from the second column was then pumped to the top inlet of the third column. This column served as a polishing column and it was also open to the atmosphere, passively aerated, and assumed to be aerobic. Treated effluent wastewater flowed out of the bottom of the third column into a capped collection bottle to minimize the potential re-aeration of the effluent before analysis. Figure 1 shows a single wetland system.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 2 Data Collection I: Far-Field Beampattern  
In this section, two key features of the FDA beampattern, namely, temporal periodicity at a fixed location and angular mainbeam auto-scanning with increasing fast-time, are experimentally demonstrated and shown to closely follow the theoretical and experimental results in [24]. The experimental setup for this data collection is shown in Figure 1, and a photograph of the general system setup is shown in Figure 2.
## 4 Results
### Process Management
The analysis of the results obtained shows that the highest percentage of enterprises achieving the third and fourth levels of digital maturity still concerns large companies. Noteworthy, however, is the fact that small enterprises are still in second place, whose percentage of enterprises reaching the third level of maturity is only three percentage points lower. The worst performers in this set are medium-sized enterprises, among which less than 50 percent reach the next level of digital maturity, and the fourth level is only reached by around 28 percent of respondents. The third level of digital maturity in the process management dimension is achieved primarily by manufacturing companies--almost 64% of respondents from this group of companies. The group of trading companies recorded the smallest share of respondents achieving both maturity levels. However, it is worth noting that in this group, the percentage of companies reaching both levels of digital maturity is balanced (there was no enterprise recorded whose development was behind at level 3). The highest level of digital maturity is reached by enterprises implementing best process management practices in their logistics service, including interdepartmental and interdisciplinary collaboration and integrating external stakeholders. The share of such organizations in the surveyed groups of enterprises is shown in Figure 11. Notably, the highest rate of reaching the fifth level of maturity was recorded for small enterprises. A comparison of the responses given to the last two levels indicates that small enterprises that have reached maturity level 4 understand the importance of digital transformation in the process management dimension and are, therefore, taking steps to implement best practices in this area. The smallest percentage was found in the group of medium-sized enterprises. Only around 17% of the companies in this group reached level 5 of digital development in the analyzed dimension. The analysis of the degree of achievement of the highest level of maturity by type of business conducted by the respondents shows significant similarity. In all groups of audited enterprises, the share of companies reaching level 5 of maturity was around 20-25%. A summary of the maturity levels in the distinguished groups of enterprises is presented in Tables 1 and 2. A noteworthy phenomenon observed in the group of small and medium-sized enterprises is that a higher percentage of enterprises reached level 3 of digital maturity than level 2. An analysis of the other answers given by this group of respondents indicates that some enterprises are implementing integration, digitalization, and automation, but only concerning core processes. Supporting processes are still implemented in a traditional way and represent the potential for further improvement. However, it is worth pointing out that this phenomenon primarily concerns manufacturing companies. Figure 11: Percentage of companies reaching level 5 of digital maturity in the area of process management.
## 5 Simulation Results and Discussion  
The proposed FQA is simulated using MATLAB 2022a software because its Fuzzy Toolbox examines all fuzzy membership functions, hence making it suitable for use. The performance is compared with the existing protocols, including FRNSEER [13], FC-RBAT [14], BOA-ACO [22], and OAFS-IMFO [32]. The simulation parameters are presented in Table 3.  
The positioning of the BS and the number of nodes can influence the experimental outcomes. Thus, we established four different scenarios to evaluate the proposed approach. In scenario 1, we randomly deployed 70 nodes within a network area measuring \(100\) m \(\times\)\(100\) m, and the BS was positioned at the center of the network. In the second scenario, the BS is switched to the edge of the network. Then, we expanded the network to \(200\) m \(\times\)\(200\) m and increased the number of nodes to \(150\), which can further verify the stability of the proposed algorithm. Finally, we placed the BS at the corner of the network. The scenario parameters are shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Initial energy & 1 J \\ \(E_{dec}\) & 50 (n/bit) \\ \(E_{pdb}\) & 5 (n/bit) \\ \(\varepsilon_{fs}\) & 10 (pJ/bit/m\({}^{2}\)) \\ \(\varepsilon_{mp}\) & 0.0013 (pJ/bit/m\({}^{4}\)) \\ \(d_{0}\) & 87.7 m \\ Data packet size & 4000 bits \\ Control packet size & 200 bits \\ \(M_{c}\) & 10\({}^{2}\)\(\sim\)10\({}^{6}\) \\ _Maxsteps_ & 10\({}^{6}\) \\ \(T\) & 0.04 \\ \(\Gamma_{0}\) & 1.6 \\ \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
The proposed protocol was compared with the FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of alive nodes, network lifetime, energy consumption, and throughput. The detailed test results are shown and discussed as follows.
## 3 Machine Learning Models
### Decision Trees and Random Forests
#### 3.2.2 Utilities  
In the utilities field, the work of [PERSON] et al. [36] focuses on the prediction of annual rice production in Bangladesh, comparing the forecasting accuracy of an optimized ARIMA method (based on the AIC criterion) to the XGBoost algorithm. Based on the MAE, MPE, RMSE, and MAPE error metrics, the XGBoost model's performance in the test set was found to be superior to the performance of the ARIMA model.  
In a slightly different field of applications, the work of [PERSON] et al. [37] aims to predict retail sales volume utilizing an XGBoost model and benchmarking its results against the classic ARIMA approach (without preprocessing due to the stationarity of the data set), a classic Gradient Boosting Decision Tree (GBDT) algorithm, an LSTM deep learning model, and the forecasting tool Prophet. The results of this study indicated that the tree-based approaches, trained with weather and temperature features, give the best forecasting performance among the five models, while the ARIMA model has the worst performance. Furthermore, it is interesting to note that while the tree-based models' results are similar, the XGBoost model requires significantly fewer training iterations than the GBDT approach, while both tree-based models require fewer data and fewer features in contrast to the deep learning models.  
Finally, an interesting ARIMA comparison to decision tree models was found in the scientific literature regarding anomaly detection in a smart home IoT by [PERSON] et al. [38]. The dataset was comprised of energy consumption data from multiple home appliances and the machine learning models were trained using additional weather information. It was shown that the ARIMA model outperforms every other model considered for this study, followed by SARIMA, LightGBM, Prophet, VAR, and LSTM, respectively. The comparison was based on the MAE, MSE, and RMSE error metrics.
## 2 Materials and Methods
### Statistical Evaluation  
The statistical indicators used in this study include root mean square error (\(RMSE\)), coefficient of determination (\(R^{2}\)), mean absolute error (\(MAE\)), mean bias error (\(MBE\)), and global relative indicator (\(GRI\)), which are expressed as:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(Y_{i,\rho}-Y_{i,p}\right)^{2}}{n}} \tag{8}\]\[R^{2}=\frac{\left[\sum_{i=1}^{n}\left(Y_{i,\rho}-\overline{Y}_{o}\right)\left(Y_{i, p}-\overline{Y}_{p}\right)\right]^{2}}{\sum_{i=1}^{n}\left(Y_{i,\rho}-\overline{Y}_{o} \right)^{2}\sum_{i=1}^{n}\left(Y_{i,p}-\overline{Y}_{p}\right)^{2}} \tag{9}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|Y_{i,\rho}-Y_{i,p}\right| \tag{10}\]  
\[MBE=\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i,\rho}-Y_{i,p}\right) \tag{11}\]  
\[GRI=\sum_{j=1}^{4}\alpha_{j}\frac{\left(\left|Y_{i,j}\right|-\left|g_{j,min} \right|\right)}{\left(\left|g_{j,max}\right|-\left|g_{j,min}\right|\right)} \tag{12}\]  
where \(Y_{i,\rho}\), \(Y_{i,p}\), \(\overline{Y}_{o}\), and \(\overline{Y}_{p}\) are the observed and predicted factors, and the mean values of the observed and predicted factors, respectively; \(n\) is the number of observations; \(\alpha_{j}\) is a coefficient, which equals to 1 for _RMSE_, _MAE_, and _MBE_, and \(-1\) for \(R^{2}\); \(g_{j,max}\) and \(g_{j,min}\) represents the maximum and minimum of \(j\), respectively; \(Y_{i,j}\) is the scaled value of \(j\). The models with a lower _GRI_ value indicated higher accuracy.
## 4 Experimental Results and Analysis  
### Key Point Detection Experiment  
#### 4.1.1 Training Strategy and Evaluation Index  
The Pytorch-1.7.1 deep learning framework was used; the operating system was Windows 10, the GPU was NVIDIA GTX 3090, and the processor was a 12 th Intel(R) Core (TM) i9-12900 KF CPU @2.40 GHz to train and test the HRnet network, and the training process used pre-trained models. The size of the dataset image is 256 \(\times\) 192, the batch size is 8, and the epoch is 150. The initial learning rate (learning rate) is 0.01 using Adam optimization. The experiment used Average Precision (mAP) and Average Recall (AR) scores based on OKS (Object Key point Similarity, OKS) [21] as evaluation metrics.
## 3 ROP Prediction Based on the Random Forest Machine Learning Model  
### Principles of the Random Forest Algorithm  
Random forest belongs to the Bagging class of ensemble machine learning algorithms. As shown in Figure 3, ensemble learning involves training multiple weak learners to form a strong learner. In the Random Forest, the weak model chosen is the CART decision tree. The Random Forest utilizes bootstrap sampling to collect multiple different subsets of the original samples for training the decision trees. The final result is obtained by averaging the predictions of all the decision trees.  
Figure 3: Random Forest model structure diagram.  
Figure 2: ROP prediction results of well Fu39 based on the mechanical-specific energy model.  
When training an individual CRAT decision tree model, consider a scenario as depicted in Figure 3. In this scenario, the primary factors influencing mechanical drilling rate are whether the drilling tool's rotation speed exceeds 50 r/min. The decision tree divides the dataset into two groups based on this criterion, grouping data points with rotation speeds below 50 r/min together. The average of this group represents the tree's prediction for the mechanical drilling rate under this condition.  
As the decision tree model's other feature variables, such as drilling pressure, vary, the mechanical drilling rate normally increases with the increase in the drilling pressure. However, during the drilling process, situations like those illustrated in Stage 3 of Figure 1, such as bit mud packing, bottom-hole mud packing, and tool vibrations, can lead to increased drilling pressure and a decrease in the mechanical drilling rate. However, a mechanical drilling rate model driven by a mechanistic model cannot reflect this anomalous change in the mechanical drilling rate caused by complex situations. Continuing to use a traditional mechanical drilling rate model would yield a trend opposite to the actual values. On the other hand, a data-driven approach, which considers only the mapping relationship between data points, can capture this anomalous trend in the mechanical drilling rate and effectively addresses the problem of deep well mechanical drilling rate prediction in complex situations.  
For the sake of facilitating comprehension of this model, further detailed formulaic explanations regarding the Random Forest can be found in Appendix A.
## 5 Results and Discussions
From the above discussion, it can be concluded that the MU HCDP when attached to the outer wall of the WTS ring (opposite to the excitation point) is the most effective location of the HCDP on the WTS test specimen for reducing the vibration amplitude in comparison to the other setup. Furthermore, it has been also observed that the position of the HCDP on the WTS test specimen plays a very crucial role for vibration attenuation.
# Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection
###### Abstract  
Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI) plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS) contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing unit (GPU) were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method.  
network security; pattern matching algorithm; deep packet inspection; intrusion detection system; general-purpose graphics processing unit; compute unified device architecture +
Footnote †: journal:
# Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring
## 2 Methods
### Comparative Analysis  
By placing the sensors on advanced medical mannequins, Euler angles were measured while executing several typical supine body movements. These maneuvers include a range of postural transitions, such as rolling from the back to the stomach, slipping from the bed surface onto the floor below, turning towards one's side, experiencing a seizure, and succumbing to head droopiness. By performing a Procrustes shape analysis, dissimilarity values are obtained and utilized to distinguish between various movements by comparing them with reference shape data sets. In case an individual executes multiple rollovers (i.e., transitioning from a back to a stomach position), then the dissimilarity values for such transitions (compared with reference shape data set) must be significantly different in comparison to rolling over towards either side, so as to accurately differentiate among these distinct maneuvers.  
To compare different movement types, it is expected that we will find low dissimilarity values when each movement type is compared to another of its own kind. Conversely, higher dissimilarity values should emerge through comparisons between a reference data  
Figure 2: Procrustes shape analysis applied to a set of landmarks. The shapes have been aligned, after the translation, rotation, and scaling, revealing underlying patterns of shape variation. Statistical analysis can now be performed to explore shape differences and relationships.  
set (e.g., Rollover 3) and other kinds of bodily movements. Therefore, in analyzing a specific movement's curve shape data set, it is juxtaposed with standard sets of body motion data (Figure 3); this comparison allows for finding the smallest dissimilarity value--significantly lower than others--which then represents the given action associated with that reference motion. Hence, the body movement is identified.  
To summarize, sensors placed on advanced medical mannequins were used to measure the Euler angles during various bodily movements. These measurements were then compared using a Procrustes shape analysis to obtain dissimilarity values. These values are pivotal in differentiating between various movements when compared with a reference shape data set. For instance, if a patient performs multiple rollovers (e.g., transitioning from a back to a stomach position), the dissimilarity values for such transitions (compared with a reference shape data set) must be significantly lower in comparison to rolling over towards either side. This will help accurately differentiate among these different maneuvers. It is anticipated that one can identify body movements by comparing a specific movement's curve shape data set against standard sets of body motion data. This comparison aims to identify the dissimilarity value that distinctly represents the given action associated with that reference motion, thereby identifying the body movement.
## 4 An Efficient Convolutional Denoising Autoencoder Method for BDS NLOS Detection
### Proposed Convolutional Denoising Autoencoder Algorithm  
We developed the CDAENet method to detect LOS/NLOS categories in real urban forest datasets. First, the DAE can remove the noise while downgrading the dimensions. Second, MLP aim at capturing nonlinear relationships by superimposing multiple neural networks. Therefore, we used multiple fully connected layers to learn the nonlinear trend of the NLOS signal. Finally, test samples are fed into the trained CDAENet classifier to detect NLOS. See Algorithm 1 for details.  
```
0: The raw dataset D1, D2, D3, and D4. (D1, D2, D3 and D4 correspond to the four urban forest environments and more details can be found in Section 5.2)
0: The type of the BDS signal. \(h_{j}^{l}=[h_{1},h_{2},...h_{M}]\in M^{l}\), where \(h_{i}\) is the number of hidden units in layer \(i\) and \(l\) is the number of hidden layers. \(\hat{x}=[\hat{x}_{1},\hat{x}_{2},...\hat{x}_{z}]\in Z^{l}\), where \(\hat{x}_{i}\) is the number of output units in layer \(i\) and \(l\) is the number of output layers.
1:for iterator=1,2,3,...,T do
2: Generate latent variables matrix \(H\) through Equation (11) \(h=f_{\theta}(\hat{x})=s(W\hat{x}+b)\)
3: Let the loss \(l\) be defined as (using Equation (14)) \(L(W,W^{l},b,b^{l})\longleftarrow\min_{\omega}\frac{1}{2N}\sum_{t=1}^{N}\|\hat {x}_{t}-x_{t}\|^{2}\)
4: Compute stochastic gradient of the loss w.r.t each \(\omega\) \(\bigtriangledown\vec{\mathcal{L}}=\bigtriangledown_{\theta\
otin\mathcal{L}} \Big{(}\frac{1}{|\mathcal{M}\sum L_{t}|}\Big{)}\)
5: Update network parameters by backpropagation \((\theta,\phi)\longleftarrow\textit{Stochastic gradient descent optimizer}\)
6:endfor
7:for\(h_{j}^{l}=[h_{1},h_{2},...h_{M}]\in M^{l}\)do
8: For detection on the data \(h_{j}^{l}\) using MLP
9:endfor
10:return LOS or NLOS
```  
**Algorithm 1** CDAENet algorithm for NLOS recognition in the urban forest environment.