esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Modification of Glass Textile for the Reinforcement of a Cement-Based Composite: A Review
## 4 Modification Techniques of Glass Fibers
### Inorganic Coatings
[PERSON] compared the effects of nano-silica and micro-silica coatings on the tensile strength properties of a lime-cement matrix reinforced with an open-square-mesh biaxial ARG fabric [72]. The nano-silica coating was prepared using the sol-gel method, where tetraethyl orthosilicate (TEOS) was employed as SiO\({}_{2}\) precursor and the fabrics were impregnated by bathing the fibers for 5 min in the colloidal solution obtained by acid-catalyzed condensation of TEOS in a isopropanol/water solution. The wet fabric was left to dry in oven at 105 \({}^{\circ}\)C for 15 min. For the coating with micro-silica particles, the reinforcement was immersed in an aqueous suspension obtained by stirring silica fumes with particle size of 0.15 \(\upmu\)m for one hour in distilled water and successive sonicated for 10 min. In this case, after dipping the fabric in the suspension, it was left to dry at room temperature. For both coatings, SEM images show the presence of diffuse mortar patches attached to the fabric after the failure of the composite, which were not detected on the untreated reinforcement. The strong coupling effects of the silica coating with the reinforcement interphase are confirmed by the increase of the tensile strength of 60.3% and 96.7% for the fabric with nano-silica and micro-silica, respectively, with respect to dry systems. [PERSON] and [PERSON] report an increase of the maximum tensile stress of the composite by coating the reinforcement with 200 nm silica fume particles [63]. However, in the same study, when using fillers with smaller particles dimension (50 nm) no increase in the composite mechanical properties was observed. According to the authors, this is partially due to the wash-out that small particles undergo when the reinforcement is immersed in the fresh cementitious paste. The authors also report that large silica fume particles have a beneficial effect on the durability of the composite because of the pozzolanic reaction between the amorphous silica and the portlandite. Instead of silica fume particles [PERSON] et al., report the use of a potassium silicate dispersion for the impregnation of AR-glass fabric [71]. The inorganic coating was applied on the AR-glass fabric using a coating machine with roll-to-roll technique. Specimens with the potassium silicate reinforcement showed an increase of the tensile strength of 18%, compared to samples with the uncoated fabric. Moreover, also the flexural strength of the composite subjected to four-point bending test increased by coating the fabric with potassium silicate. According to the authors, the inorganic coating reacts with the cementitious matrix, allowing better penetration of the matrix itself within the reinforcement and improving the adhesion at the fibers-to-matrix interface.
## 3 System Building Blocks
### Modeling
#### 3.2.1 Reconstruction  
In the reconstruction from the initial image acquired by a camera, an RGB-D structure is generated. Information is stored regarding the 2D image (color at each point in the point cloud), as well as a point cloud according to the captured 3D scene (spatial position x, y, z). This information is used to construct the model.  
The reconstruction of the face is based on the point cloud P obtained from the input device (camera). These points with a spatial position of x, y, z need to be aligned and organized into a mesh structure that can be easily manipulated and visualized to obtain the head views. The first step of reconstruction consists of a preprocessing step that is conducted to eliminate the outliers. These outliers are generated due to noises or lighting problems at the time of acquisition. The removal of these outliers is an approach based on the distance of the face to the camera and its depth. In order to obtain the position of the face and verify which points belong to it or are considered outliers, we use the facial landmark detector proposed by [31]. It estimates the location of 68 (x, y) coordinate pairs that map to facial structures on the face. These landmarks can be visualized in Figure 6.  
After estimating the landmarks, we calculate a circular region \(Cn\) around the face's nose (red circle in Figure 6 (left)). The \(Cn\) center is given by the \(x\) and \(y\) coordinates of the \(31^{\circ}\) landmark calculated according to [31], and the \(Cn\) radius corresponds to the distance between the \(30^{\circ}\) and \(31^{\circ}\) points.  
\[P_{i}=\begin{cases}0&d_{i}>mD\wedge d_{i}<mD+\tau\\ 1&otherwise\end{cases} \tag{1}\]  
where \(d_{i}\) is the depth of \(P_{i}\), and \(\tau\) is the threshold calculated based on the depth of a human face. After eliminating the outliers, the remaining points \(P\) are rearranged to a regular mesh. Firstly, we compute a triangular mesh using a Delaunay triangulation. Hence, we  
Figure 4: Lighting/background variations (**above**: natural light, **bellow**: artificial light).  
recreate the depth map by matching a surface of the form \(Z=F(X,Y)\). The inputs \(X\) and \(Y\) are 2D grid coordinates based on the coordinates contained in the vectors \(x\) and \(y\) from \(P\). The grid is represented by the coordinates \(X\) and \(Y\), with \(length(y)\) rows and \(length(x)\) columns. \(X\), \(Y\) and \(Z\) are the new coordinates of a regular mesh that represents the 3D surface of the face.  
Figure 5: Representation of the modeling process for an RGB-D image. The process starts with the RGB image, passing by the depth map, 3D reconstruction and mesh.  
Figure 6: Estimated facial landmarks (**left**), landmarks on the face (**center**) and the Voronoi Diagram (**right**).  
The texture of the model is mapped using \(UV\) mapping that projects the 2D image obtained from the photo to the 3D surface.
## 4 AC Steady-State Analysis  
Let \(P=v_{\rm pv}i_{\rm pv}\) be the DC power generated by the PV generator and \(p_{\rm ac}\) the instantaneous power injected to the grid by the inverter. By neglecting the losses, one has  
\[p_{\rm ac}(t)=V_{g,\rm rms}I_{g,\rm rms}(1-\cos(2\omega_{g}t))=P(1-\cos(2 \omega_{g}t)) \tag{32}\]  
The instantaneous power of the capacitor at the output of the PV generator is the difference between the produced power \(P\) and the one absorbed by the inverter. Therefore, it can be expressed as follows  
\[p_{C}=P-P(1-\cos(2\omega_{g}t))=P\cos(2\omega_{g}t) \tag{33}\]  
This capacitor power oscillates at the double of the grid frequency. It is positive during one half period and therefore the capacitor is charged and it is negative during the other half period and therefore the capacitor is discharged. During the charging period, the energy supplied to the capacitor when \(p_{C}>0\) is  
\[\Delta E_{C}=\int_{-\frac{\pi}{\omega_{g}}}^{\frac{\pi}{\omega_{g}}}P\cos(2 \omega_{g}t)dt=\frac{p}{\omega_{g}} \tag{34}\]  
In steady state, this energy is equal to the energy stored in the capacitor when its voltage changes from its minimum value \(v_{dc,\rm min}\) to its maximum value \(v_{dc,\rm max}\)  
\[\Delta E_{C}=\frac{1}{2}C_{\rm dc}\overline{v}_{\rm dc,max}^{2}-\frac{1}{2}C _{\rm dc}\overline{v}_{\rm dc,min}^{2}\approx C_{\rm dc}V_{\rm mpp}\Delta \overline{v}_{\rm dc} \tag{35}\]  
where \(\Delta\overline{v}_{\rm pv}=\overline{v}_{\rm dc,max}-\overline{v}_{\rm dc, min}\) is the peak-to-peak AC ripple of the PV capacitor voltage in steady-state operation. Using (34) and (35), \(\Delta v_{\rm pv}\) can be expressed as follows  
\[\Delta\overline{v}_{\rm dc}=\frac{P}{C_{p}V_{\rm mpp}\omega_{g}} \tag{36}\]This AC PV voltage ripple of the averaged variable \(\overline{v}_{\rm dc}\) should not be confused by the switching ripple of the state variable \(\overline{v}_{\rm dc}\) which is much smaller and can be neglected. Therefore, in steady-state operation, the time varying averaged input PV voltage \(v_{\rm dc}(t)\) is composed by a DC component \(V_{\rm mpp}\) imposed by the MPPT controller and an AC ripple that can be approximated by \(\Delta v_{\rm dc}\) given in (36) and finally \(v_{\rm dc}(t)\) can be accurately described by the following expression in steady-state operation  
\[\overline{v}_{\rm dc}(t)\approx V_{\rm mpp}+\frac{1}{2}\Delta\overline{v}_{\rm dc }\sin(2\omega_{g}t) \tag{37}\]  
For a well designed inverter, the amplitude of the AC component of the PV voltage must be selected much smaller than its DC component, i.e, \(\Delta v_{\rm dc}\ll V_{\rm mpp}\). Let \(\Delta v_{\rm dc,max}\) be the maximum value of the allowed ripple in the DC PV voltage, then the value of capacitor must be selected such that  
\[C_{\rm dc}\geq\frac{P}{\Delta\overline{v}_{\rm dc,max}V_{\rm mpp}\omega_{g}} \tag{38}\]  
These theoretical predictions will be validated below by using the full-order switched model of the differential boost inverter implemented in PSIM\({}^{\copyright\copyright\) software.
# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 3 Proposed Method
### Process Node Recommendation
The following principles should be followed when recommending k nodes:  
1. Recommend sequential rules with a high process-matching degree first. 2. Recommend sequential rules with a high lift under the same conditions first. 3. Recommend sequential rules with high confidence under the same conditions first. 4. Recommend sequential rules with high support under the same conditions first. The process-matching degree is calculated as a weighted combination of similarity and node recommendation. When the similarity equals 1, it indicates that the edge-disjoint path is the same as the CPSP, and thus, has no recommendation significance and it will not be recommended. The recommended top k node set is the last node of the CPSP in the recommended top k sequential rule. The implementation of the process node recommendation algorithm is performed in the following steps shown in Figure 7.  
The input for Algorithm 3 consists of a reference model and a table of sequential rules, and the output is a set of recommended nodes. ```
Input: reference model \(F\), sequential rules table \(T^{*}\) Output: recommended node set Node  Initialize candidate recommendation sequential rules dictionary \(P\) Call Algorithm1 to get edge-disjoint path \(R\) of \(F\) FOR (each edge-disjoint path \(L\) in \(R\) where the last node is the current node) do{ FOR (each sequential rule \(T\) in \(T^{*}\)) do{ Sequential pattern \(S\) and its corresponding _Lift_, _Conf_, _Sup_ = T[0], T[1], T[2], T[3] Call LCS(L,S) to get the longest common subsequence owned by \(L\) and \(S\) Calculate LCS_Sim(\(L\),\(S\)) according to Equation (4) Calculate P_m(\(L\),\(S\)) according to Equations (5) and (6) P+ = {\(S\):[\(P\_m\), _Lift_, _Conf_, _Sup_]]} Get top k \(P\) sorted by \(P\_m\); if the same, then sorted by _Lift_; and so on Get Node\(\rightarrow\)the last node of \(S\) in top k \(P\) Return Node
```  
**Algorithm 3** Process node recommendation algorithm
Taking the real datasets as an example, suppose the reference model's edge-disjoint path entered by the modeler is <OpenAPI Message, Wiretap1>. There are two sequential rules in the sequential rule base, 1: <OpenAPIMessage, Wiretap1, Wiretap, 1.22, 1.0, 49> and 2: <OpenAPIMessage, Wiretap1, Terminal, 1.43, 1.0, 42>. First, the process-matching degree of both CPSPs was calculated to be 0.9. Then, the lift degree was compared, and it was found that the lift degree of sequential rule 2 was greater than that of sequential rule 1. Therefore, the recommended next node for \"Wiretap1\" was the last node of the CPSP in sequential rule 2. The top five recommendation results of the edge-disjoint path in the whole real datasets are shown in Table 2.
## 3 Results
### Estimation Models and Validation  
The MR model is presented in Equation (9). The variables, including AL, BU, and area, were selected from the feature selection method. The MR model provided the lowest accuracy compared to DT and SVR (see Table 4).  
\[TEC=0.44\ (AL)+0.37(BU)+0.04(Year)+0 \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Multiple Regression** & **Decision Tree (DT)** &
\begin{tabular}{c} **Support Vector** \\ **Regression (SVM)** \\ \end{tabular} \\ \hline MAE & 0.33 & 0.12 & 0.20 \\ RMSE & 0.51 & 0.24 & 0.39 \\ R2 & 0.81 & 0.95 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance of prediction models.  
Figure 4: The scatter plot and correlation between predictors and prediction variable (sample of the year 2012 and 2018); (**a**) TEC and BU and (**b**) TEC and SL.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variables** & **Year** & **TP** & **TH** & **SL** & **AL** & **BU** & **GDP** & **PD** & **Area** \\ \hline Multiple Regression (MR) & 0.12 & & & 0.86 & 0.49 & & & \\ Decision Tree (DT) & & 0.51 & & & 0.20 & & 0.004 & 0.09 \\ Support Vector Regression (SVR) & & 0.03 & 0.04 & 0.08 & & 0.06 & 0.05 & 0.06 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Electricity consumption estimation predictors and their weight in each model.  
The DT model was created from five attributes (one target attribute, TEC, and four predictor attributes, BU, TH, PD, and area) and their decision label value (see Appendix B Table A1). Total household (0.393) was the highest weight attribute, followed by an area of lit (0.192) and population (0.005). Figure 5b illustrates the DT prediction chart, which led to the best performance in electricity prediction among three models (see Table 4) with an RMSE of 0.24, MAE of 0.12, MSE of 0.06, and the highest R\({}^{2}\) of 0.95. The 210 training datapoints trained the final SVR prediction model with four attributes: TP, TH, SL, BU, and PD (see Figure 5c). The kernel function of each variable was present by w(x\({}_{i}\)) in Figure 5c, and the offset is 0.655. As shown in Table 4, the SVR prediction model had the second-best prediction performance.  
Figure 5: The scatter plots of predicted values versus actual values from different models, including (**a**) Multiple regression, (**b**) Decision Tree, and (**c**) Support Vector Regression.  
A comparison of the prediction models' performance is illustrated in Table 4, in which DT provided the best performance with the lowest MAE, RMSE, and highest R\({}^{2}\), followed by SVR and MR. Therefore, DT achieved the highest accuracy and was chosen to estimate the district-level electricity consumption in Thailand's EEC.
## 5 Conclusions and Future Scope  
The blockchain-based intrusion detection system addresses resource constraints in 5G SDN by merging blockchain security and advanced neural networks. This robust solution fortifies mobile users' information security, heightening the challenge for attackers. The decentralized blockchain ensures resilience by avoiding a single vulnerability point, promoting trust with its transparent, tamper-resistant design. The blockchain-based intelligent intrusion detection and prevention system overcomes the challenges of resource constraints faced by SDN controllers and switches in the 5G software-defined architecture. It enhances information security and broadens threat detection capabilities, shaping the future of 5G network security. The proposed BSHNN algorithm shows significant enhanced performance with respect to parameters such as accuracy, precision, recall, detection rate, and authentication time. The results also reveal that our proposal provides a substantial increase in throughput with a minimum packet loss ratio.  
Further research in this field involves exploring potential challenges and limitations in integrating blockchain technology into 5G networks. It also includes investigating ways to minimize the overhead introduced by blockchain while maintaining its security benefits. Additionally, there is a need for ongoing research into more advanced AI techniques to achieve even more accurate packet classification and intrusion detection. These avenues of exploration will contribute to the continued enhancement of network security and the adoption of innovative technologies.  
Conceptualization, N.K.S.N.; Methodology, N.K.S.N.; Software, N.K.S.N.; Formal analysis, B.B.; Writing--Original draft, N.K.S.N.; Writing--Review & Editing, B.B.; Supervision, B.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
### The Packing Types  
The packing is the core component of the packed scrubber, the configuration of which is directly related to the gas-liquid mass transfer efficiency. The surface of packing is the basis of gas-liquid mass transfer in the scrubbing tower [32]. During the experiment, the simulated fuel gas was preoxidized by the DBD reactor and the discharge power was fixed at 19.5 W. The effects of different packing on desulfurization and denitration efficiency were investigated and the results are shown in Figure 5. The experimental results show that the denitration efficiencies of the D\({}_{\text{g}}\) 10 mm Pall ring, D\({}_{\text{g}}\) 6 mm Pall ring, D\({}_{\text{g}}\) 6 mm Dixon ring, and D\({}_{\text{g}}\) 6 mm Cannon ring are 62.6%, 66.6%, 70.8%, and 65.6%, respectively, while the desulfurization efficiencies are more than 99.3% for all packings.  
The main absorption process for the packed scrubber occurs in the part filled with packing. For the micro tower height \(dh\), the mass of the solute transferred between gas and liquid phases in this section per unit time can be calculated using the following formula [32]:  
\[X=N_{A}aSdh \tag{5}\]  
where \(X\) is the mass of solute transferred between gas and liquid phases in the micro tower section per unit time in kmol/s; \(N_{A}\) is the gas-liquid mass transfer rate in kmol/(s\(\cdot\) m\({}^{2}\)); \(a\) is the gas-liquid mass transfer area per unit volume in m\({}^{2}\)/m\({}^{3}\); \(S\) is the cross-sectional area of the scrubber in m\({}^{2}\).  
The packings with a larger specific surface area can provide a larger gas-liquid mass transfer area \((a)\) and achieve a greater \(X\). The specific surface areas of the four types of packings used in the experiment are shown in Table 2. Due to the larger specific surface area of the D\({}_{\text{g}}\) 6 mm Dixon ring, it obtains a higher gas-liquid mass transfer efficiency and denitration efficiency. SO\({}_{2}\) is easily absorbed by aqueous solution, while its removal efficiency depends more on the SO\({}_{2}\) absorption capacity of the scrubbing solution; therefore, the packing types have little effect on the desulfurization efficiency.  
Figure 5: Desulfurization and denitration efficiencies of various packing types (operating conditions: gas flow rate = 5 L/min, scrubbing solution = 1% NaOH solution, spray density = 7.6 m\({}^{3}\)/(m\({}^{2}\cdot\)h), packing height = 80 cm).
## 4 Multicast Routing Approaches in DTN
### Buffer-Efficient Multicast Routing in DTN
#### 4.2.1 Buffer Management
It calculates a rank value to decide the sequence to delete messages after looping through the buffer. The advantage of QBMR is that it does not require any overhead cost to maintain group membership information. The node will exchange and merge the i-list tables on each encounter. During the merging process, the entries with the same multicasting EID (M_EID) are grouped together to look for the maximum value of the number of receipts for a multicast message. This maximum value donates the current size of a multicast group and the average multicast group size can be estimated. Reliable Multicast over Delay tolerant mobile Ad hoc networks: Reliable Multicast over Delay tolerant mobile Ad hoc networks (RMDA) is a flooding-based multicast scheme with a group management module and a transmission management module. It forwards the message bundle to a computed average amount of group members to ensure reliability but induces a higher buffer resource consumption [47]. RMDA achieves a higher delivery ratio by keeping track of the total number of group members. [PERSON] et al. [48] proposed an extended version of RMDA with a buffer management strategy, as depicted in Figure 9. It checks whether the available buffer can accommodate incoming messages. The oldest unicast message is deleted first, followed by the multicast message with a delivery ratio closest to the RMDA value whenever the free buffer is not enough to accommodate new messages. They further introduced a configurable delivery ratio to prioritize multicast messages and reserve a certain amount of buffer space for multicast messages only [49]. The scheme removes buffered messages based on an estimated number of group members, the number of ACKs, and the configured delivery ratio level. The simulation results show that the ratio of message delivery increases even in a larger group size. However, the flooding-based nature of these protocols forwards the message bundles to any encountered node without authentication. Precede Global Knowledge-Based Scheduling and Drop Policy: The precede Global knowledge-Based Scheduling and Drop policy (GBSD) [50] is known as an optimal-based strategy dedicated to unicasting in DTNs. GBSD schedules and drops messages by estimating the delivery probability of each message upon contact. [PERSON] and [PERSON] [51] extended GBSD to formulate the delivery probability to multiple endpoints to support the multicasting service in DTN, and they implemented a new E-GBSD algorithm to schedule and to remove messages. E-GBSD prioritizes source messages in the buffer and stores them until the local buffer is full of source messages to ensure their availability, which is done by the utility function to maximize the value of the change in message delivery rate (MDR). E-GBSD performs better than FIFO and GBSD and achieves the highest MDR among the three of them.
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 2 Discrete Wavelet Transform  
Wavelet transform is often used to analyze instantaneous and time-varying signals. Although a classical Fourier transform can reflect the overall connotation of signals, its expression is usually not intuitive enough. As an alternative to Fourier transform, wavelet transform can decompose signals at different scales. Different decomposition scales can be selected for different processing targets. Moreover, the time and the frequency domains can be simultaneously located. For non-stationary signal analysis, it has great advantages.  
Discrete wavelet transform is realized by passing the signal through a series of low-pass and high-pass filters [21; 22]. Wavelet transform decomposes the signal into detailed components and approximates components of different scales. Figure 1 is a schematic diagram of discrete wavelet transform, where \(x[n]\) is the discrete input signal with length n; \(g[n]\) is a low-pass filter, which can filter out the high-frequency part of the input signal and output the low-frequency part. \(h[n]\) is a high-pass filter. Contrary to the low-pass filter, it filters out the low-frequency part and outputs the high-frequency part. \(\downarrow\) is a down-sampling filter.  
At present, many wavelet bases have been developed, such as Haar, Daubechies (Db), Symlet and so on, for the analysis and synthesis of signals. Correct selection of a wavelet basis function plays an important role in de-noising performance [23]. Table 1 shows the comparison of the signal-to-noise ratio (SNR) and the mean square error (MSE) of the de-noised ECG signals which are de-noised by several typical wavelet basis. It can be seen from the table that SNR of db5 is relatively high and MSE is relatively low. Therefore, we select db5 as a wavelet basis in the process of de-noising ECG signal in the paper.