esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Family** & **Prec.** & **Rec.** & **F1** & **Family** & **Prec.** & **Rec.** & **F1** \\ \hline bamital & 99.29\% & 89.25\% & 93.81\% & orchard & 93.47\% & 94.41\% & 93.84\% \\ banjori & 95.05\% & 99.97\% & 97.45\% & padcrypt & 73.10\% & 82.20\% & 76.39\% \\ bazzlorader & 99.99\% & 99.95\% & 99.97\% & phorpiex & 18.18\% & 0.48\% & 0.94\% \\ chinad & 98.99\% & 98.81\% & 98.89\% & pitou & 87.62\% & 51.62\% & 63.14\% \\ conficker & 28.38\% & 16.63\% & 20.76\% & proslikefan & 3.70\% & 0.06\% & 0.11\% \\ corebot & 95.37\% & 93.57\% & 94.44\% & pseudoman & 34.73\% & 72.96\% & 45.78\% \\ cryptlocker & 48.64\% & 37.84\% & 42.26\% & pushdo & 76.40\% & 83.82\% & 79.76\% \\ darknessl & 91.02\% & 82.52\% & 86.53\% & pushdotid & 48.33\% & 66.89\% & 55.98\% \\ darkwatchman & 99.39\% & 99.32\% & 99.35\% & pykspa2s & 67.89\% & 73.99\% & 70.35\% \\ dircrypt & 0.00\% & 0.00\% & 0.00\% & qadars & 90.96\% & 80.37\% & 85.19\% \\ dyre & 100.00\% & 100.00\% & 100.00\% & qakbot & 70.31\% & 68.79\% & 69.50\% \\ ebury & 96.02\% & 95.53\% & 95.76\% & qsnatch & 96.98\% & 97.47\% & 97.21\% \\ emotet & 80.78\% & 97.59\% & 88.31\% & ramdo & 93.79\% & 99.59\% & 96.57\% \\ flubot & 87.28\% & 95.22\% & 91.05\% & ramnit & 46.68\% & 56.95\% & 51.14\% \\ fobber & 12.12\% & 0.21\% & 0.42\% & ranbyus & 77.53\% & 91.03\% & 83.74\% \\ gamecover & 99.92\% & 99.94\% & 99.93\% & rovnix & 97.52\% & 95.96\% & 96.72\% \\ gamecover\_p2p & 92.45\% & 43.45\% & 58.92\% & shifu & 63.18\% & 60.61\% & 60.68\% \\ gozi & 77.80\% & 33.69\% & 45.86\% & simda & 84.84\% & 97.62\% & 90.77\% \\ grandoreiro & 96.68\% & 98.14\% & 97.39\% & sphinx & 34.96\% & 30.48\% & 30.74\% \\ locky & 35.11\% & 3.77\% & 6.79\% & suppobox & 73.71\% & 87.09\% & 79.82\% \\ mbyvtdd & 50.82\% & 29.90\% & 37.29\% & symmi & 98.04\% & 99.83\% & 98.92\% \\ metastaeler & 99.88\% & 99.99\% & 99.93\% & tinba & 92.46\% & 99.26\% & 95.74\% \\ monerominer & 99.85\% & 99.94\% & 99.90\% & tinyfluff & 98.82\% & 99.57\% & 99.19\% \\ murtofet & 78.21\% & 92.46\% & 84.68\% & tinynuke & 92.25\% & 99.51\% & 95.65\% \\ murtofetweekly & 99.96\% & 99.70\% & 99.83\% & tufik & 0.00\% & 0.00\% & 0.00\% \\ mydoom & 0.00\% & 0.00\% & 0.00\% & urlzone & 95.39\% & 90.20\% & 92.71\% \\ necro & 99.78\% & 96.62\% & 98.18\% & vawtrak & 22.94\% & 4.66\% & 7.40\% \\ necurs & 89.64\% & 86.30\% & 87.94\% & virut & 99.66\% & 100.00\% & 99.83\% \\ ngioweb & 96.56\% & 67.51\% & 79.46\% & xhex & 99.47\% & 99.73\% & 99.60\% \\ nymaim & 32.12\% & 1.62\% & 3.03\% & zloader & 94.24\% & 99.59\% & 96.84\% \\ nymaim2 & 95.15\% & 42.99\% & 59.22\% & Average & 73.83\% & 70.28\% & 70.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: CNN+LSTM on classic training method precision, recall, and F1 score of each of the 61 DGA family.
# Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
## 2 Materials and Methods
### Concept of a QA System for CXR  
The workflow outline of the QA system is shown in Figure 1. The outline of the datasets is summarized in Table 1. The QA system consisted of four models: (1) correction of orientation, (2) correction of angle, (3) correction of left-right reversal, and (4) judging the patient's position. We used the regression method for the correction of the angle, and another correction was performed using the classification methods.  
In this QA system, each image is programmed to undergo correction at each step according to the judgment of each DL model. Finally, the QA system adds the marker in the free space of the CXR to inform readers of the patient's position according to the judgement of the DL model. Makers were added at the right upper corner of the images.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Task** & **Method** & **Source** & **Train Images** & **Test Images** & **Remarks** & **Pre-Processing** \\ \hline Orientation & Classification & & & & Including 1/4 each of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) rotations. & 15 years old and inappropriate images by visual review \\ \hline Angle & Regression & Chest-14 & 16,000 & 4000 & \begin{tabular}{c} Randomly rotation from \\ \(-\)25 degrees to 25 degrees \\ \end{tabular} & The first exam of each patient \\ \hline \begin{tabular}{c} Left-Right \\ reversal \\ \end{tabular} & Classification & & & & Half of the images \\ \hline \begin{tabular}{c} Patient’s \\ position \\ \end{tabular} & Classification & \begin{tabular}{c} Clinical \\ image \\ \end{tabular} & 7800 & 1200 & \begin{tabular}{c} Including 3000 images of each \\ position, which were standing, \\ sitting and lying. \\ \end{tabular} & \\ \hline \begin{tabular}{c} Overall \\ test \\ \end{tabular} & & & & 120 & \
\begin{tabular}{c} 30\% of the images underwent \\ horizontal flips. \\ \end{tabular} &
\begin{tabular}{c} Make was masked. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of datasets for each training and the overall test.  
Figure 1: Concept of the QA system.  
The QA system consisted of four steps. Images were corrected in their orientation, angle, and left-right reversal, then the patient's position was estimated through the QA system. The QA system was constructed by combining the classification method and the regression method. QA, quality assurance.
## 5 Simulation and Analysis
### Distributed Jammers in Different Topologies  
In order to verify the feasibility of optimising the interference effect, the interference of distributed interference sources in different topologies should be analysed. Two groups of three randomly generated and uniformly distributed topologies are selected, denoted as  
Figure 10: Curve of BER over time.  
Figure 9: Curve of average coverage over time.  
topology 1, topology 2, and topology 3, and the number of LEO satellites to be interfered with is 7.  
As shown in Figure 11, the boxplots record the BERs for 100 experiments for three different topologies, with jamming implemented for 5 s, 10 s, 15 s, and 20 s. The red, green, and blue boxes represent data for topologies 1, 2, and 3, respectively. The tight distribution of the upper and lower edges of the box-and-line plots shows that the jamming system maintains decision stability even when the distributed jammers are presented with different topologies. This provides some technical guarantee for the future realisation of highly dynamic airborne jammers. The nonlinear growth of the median line in the box with the times indicates that the system has a certain perception capability. Under the premise of ensuring interference stability, the system implements progressive interference regulation through dynamic parameter optimisation and strategy iteration mechanisms, which ultimately achieve a stepwise increase in interference effect. This adaptive regulation not only maintains the basic stability of the suppression effect but also achieves a cumulative enhancement in the interference gains through continuous strategy evolution. The hollow circles are the singular values appearing in the box plots, and the axes are on the right side, indicating the number of unsuccessful interference realisations. It can be seen that as the jamming time increases, the singular values become less and the jamming success rate increases.  
As shown in Figure 12, with the accumulation of the number of iterations of the game, the one-weight coverage for the jamming region shows a gradual increase and eventually tends to converge stably. With the deepening of the game process, the jamming system gradually optimises its effectiveness, and finally achieves a stable one-weight coverage state.  
Figure 11: Boxplot of BER for three distributed interference topologies.
## 3 Similarity Transformations  
In order to obtain ODEs for Equations (3)-(7), the following non dimensional parameters are used [48,49]:  
\[\psi=\
u xf(\zeta),\zeta=\frac{Ur^{2}}{\
u x},\theta(\zeta)=\frac{T-T_{0}}{T_{w}- T_{\infty}},g(\zeta)=\frac{a^{*}}{a_{0}},m(\zeta)=\frac{b^{*}}{a_{0}}. \tag{10}\]  
Equation (3) is satisfied by utilizing Equations (8) and (9). Equations (4)-(7) are converted into the following ODEs:  
\[2f^{\prime\prime}+2\zeta f^{\prime\prime\prime}+ff^{\prime\prime}-F_{r}{f^{ \prime}}^{2}=0, \tag{11}\]  
\[4\theta^{\prime}+4\zeta\theta^{\prime\prime}+2\text{Pr}(f\theta^{\prime}-f^{ \prime}\theta-f^{\prime}S)+\lambda(Cf^{\prime}(\zeta)+D\theta)(\zeta)=0, \tag{12}\]  
\[\frac{1}{Sc}(2g^{\prime}+\zeta g^{\prime\prime})+fg^{\prime}-Kgm^{2}=0, \tag{13}\]  
\[\frac{\beta}{Sc}(2m^{\prime}+\zeta m^{\prime\prime})+fm^{\prime}+Kgm^{2}=0. \tag{14}\]  
The non-dimensional form of these parameters is [50,51]:  
\[F_{r}=\frac{C_{b}x}{\sqrt{k}},\text{Pr}=\frac{\
u}{\alpha},S=\frac{f}{e},Sc= \frac{\
u}{D_{A}},\beta=\frac{D_{B}}{D_{A}},K=\frac{k_{1}a_{0}^{2}x}{u_{w}},K_ {s}=\frac{k_{s}}{D_{A}}\sqrt{\frac{\
u x}{U_{0}}}. \tag{15}\]  
The modified boundary conditions are given as:  
\[f(\zeta)=\frac{\lambda}{2}\zeta,f^{\prime}(\zeta)=\frac{\lambda}{2},\theta( \zeta)=1-S,g^{\prime}(\zeta)=K_{s}g(\zeta),\beta m^{\prime}(n)=-K_{s}g(\zeta),\]  
\[f^{\prime}(\zeta)\rightarrow\frac{1-\lambda}{2},\theta(\zeta)\to 0,g(\zeta) \to 1,m(\zeta)\to 0\;\;\text{as}\;\zeta\rightarrow\infty \tag{16}\]  
Since \(U=u_{w}+u_{\infty}\
eq 0\), \(\lambda=0\) and corresponds to a needle that behaves to be static in a flowing nanofluid. On the other hand, \(\lambda=1\) indicates the approach of dynamic needle in a deskbound ambient fluid. When \(\lambda\) varies between zero and one, i.e., \((0<\lambda<1)\), the movement of the needle is similar to the direction of fluid. For \(\lambda<0\), the needle moves toward the negative \(x\)-axis, and the free stream velocity moves towards positive \(x\)-axis; for \(\lambda>1\), it is vice-versa.  
For simplicity, a comparison can be drawn between \(A^{*}\) and \(B^{*}\) in terms of size of diffusion coefficients. Thus, the diffusion coefficients \(D_{A}\) and \(D_{B}\) are equal [52], i.e., \(\beta=1\).  
\[g(\zeta)+m(\zeta)=1. \tag{17}\]  
Now, Equations (13) and (14) yield:  
\[\frac{1}{Sc}(2\zeta g^{\prime\prime}+2g^{\prime})+fg^{\prime}-Kgm^{2}=0, \tag{18}\]  
with the boundary conditions:  
\[g^{\prime}(\zeta)=K_{s}g(\zeta),\,\text{as}\;\zeta=n\]  
\[g(\zeta)\to 1\;\;\text{as}\;\zeta\rightarrow\infty \tag{19}\]  
The physical quantity of noteworthy attention are the drag force coefficient \(C_{f}\) and the rate of heat transfer \(Nu_{x}\), which are specified as follows:  
\[C_{f}=\frac{\tau_{w}}{\rho U^{2}},\tau_{w}=\mu\left(\frac{\partial u}{ \partial\tau}\right)_{r=R(x)}, \tag{20}\]\[Nu_{x}=\frac{xq_{w}}{k(T_{w}-T_{\infty})}q_{w}=-k\bigg{(}\frac{\partial T}{ \partial r}\bigg{)}_{r=R(x)}. \tag{21}\]  
By using Equation (8), Equations (20) and (21) can be transmuted as:  
\[C_{f}\text{Re}_{x}^{1/2}=4n^{1/2}f^{\prime\prime}(n),Nu_{x}\text{Re}_{x}^{-1/2 }=-2n^{1/2}\theta^{\prime}(n). \tag{22}\]
## 2 Biomedical Image Segmentation
### Introduction of Probabilistic Models in Biomedical Image Segmentation
#### 2.3.3 Integration with Deep Learning  
Recent advancements in biomedical image segmentation involve the integration of probabilistic models with deep learning architectures. This synergistic approach combines the representational power of deep neural networks with the probabilistic reasoning of models like GMMs and HMMs, leading to more accurate and robust segmentation outcomes [31; 35; 41].  
In conclusion, probabilistic models, including Gaussian Mixture Models and Hidden Markov Models, offer a principled and versatile framework for addressing the challenges of biomedical image segmentation. By explicitly considering uncertainty and variability, these models contribute to more accurate delineation of structures in complex imaging data. Their integration with advanced computational methods, such as deep learning, represents a promising avenue for future developments in biomedical image segmentation, catering to the evolving needs of the field.
## 3 Applications of the Tactile Internet
### Other TI Applications  
Another TI application includes the unmanned autonomous framework. For instance, industrial manufacturing products lined up on assembly line-based production requires mobile robots to help and deliver the assembly parts on demand. This process requires a highly reliable tactile communication framework to carry out the operation. These frameworks are now extensively used in hard-to-reach situations and provide support to humans in a dangerous environment. For example, a drone with a high-precision remote control and without minimised RTT operated by a human can be significantly used as a TI application.
# Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation
## 3 Control Scheme  
The cascading control approach is utilized in every DGR with two control loops. In the grid-connected mode, the outer control loop regulates the real and imaginary powers (P, Q). At the same time, the direct and quadrature current components (I\({}_{\text{conv\_d}}\), I\({}_{\text{conv\_q}}\)) are tuned by the inner loop to regulate the voltages of the PCCs. In the off-grid mode, both terminal voltage components (V\({}_{\text{q}}\), V\({}_{\text{d}}\)) are regulated using the outer loop, while the Iconv_d and Iconv_q are tuned utilizing the inner control loop. The references voltages (V\({}_{\text{conv\_a}}\)*, V\({}_{\text{conv\_b}}\)*, V\({}_{\text{conv\_c}}\)*) are obtained via the transformation of the d-q references voltages (V\({}_{\text{conv\_d}}\)*, V\({}_{\text{conv\_q}}\)*). V\({}_{\text{conv\_d}}\)*and V\({}_{\text{conv\_q}}\)* are calculated using the four PIs presented in Figure 2. Finally, the inverter switches pulses using a comparator that matches a triangle signal at 1980 Hz (60 Hz pairs) to the reference voltages (V\({}_{\text{conv\_a}}\)*, V\({}_{\text{conv\_b}}\)*, V\({}_{\text{conv\_c}}\)*).  
Figure 1: MGD diagram.  
The EBSABA method and other approaches are used to calculate the gains of the four PI controllers. Section 4 delves more into this.  
P is the actual active power of the converter, Q is the actual reactive power of the converter, I\({}_{\text{conv\_d}}\) is the direct axis component current of the inverter, I\({}_{\text{conv\_q}}\) is the quadrature axis component current of the inverter, V\({}_{\text{d}}\) is the grid direct axis voltage, V\({}_{\text{q}}\) is the grid quadrature axis voltage, I\({}_{\text{d}}\) is the grid direct axis current, I\({}_{\text{q}}\) is the grid quadrature axis current, and V\({}_{\text{conv\_2}}\)*, V\({}_{\text{conv\_b}}\)*, and V\({}_{\text{conv\_c}}\)* are the three-phase inverter reference voltages Ia,b,c are the three-phase inverter voltages and currents.
# Shifting from Conventional to Organic Filter Media in Wastewater Biofiltration Treatment: A Review
###### Abstract  
Biofiltration is a promising wastewater treatment green technology employed to remove various types of pollutants. The efficiency of biofililtration relies on biofilm, and its performance is significantly influenced by various factors such as dissolved oxygen concentration, organic loading rate, hydraulic retention time, temperature, and filter media selection. The existing biofilters utilize conventional media such as gravel, sand, anthracite, and many other composite materials. The material cost of these conventional filter materials is usually higher compared to using organic waste materials as the filter media. However, the utilization of organic materials as biofilter media has not been fully explored and their potential in terms of physicochemical properties to promote biofilm growth is lacking in the literature. Therefore, this review critically discusses the potential of shifting conventional filter media to that of organic in biofiltration wastewater treatment, focusing on filtration efficiency-influenced factors, their comparative filtration performance, advantages, and disadvantages, as well as challenges and prospective areas of organic biofilter development.  
biofiltration; biofilm; organic filter media; wastewater treatment; green technology +
Footnote †: journal: Applied Sciences  
**Keywords:** biofilm; biofilm; organic filter media; wastewater treatment; green technology  
Footnote †: journal: Applied Sciences
## 4 Calculation Results
### Homotopy-Pade Approximation  
With application of the homotopy-Pade acceleration technique [26], the convergence region can be substantially enlarged. For example, when \(\mathbb{h}=-30\), \(\varepsilon=0\), \(\mathbb{h}=-1/10\), the \(\text{Pade}[11,11]\) homotopy-Pade approximation of \(\theta_{b}\) in Equation (92) by HAM [44] can be written as:  
\[\theta_{b}=\frac{\alpha}{2}\frac{f(\alpha)}{g(\alpha)}\] (94a) where \[ \begin{array}{c}f(\alpha)=1+0.19449\alpha^{2}+3.2631\times 10^{-2}\alpha^{4}+ 2.2845\times 10^{-3}\alpha^{6}\\ \qquad\qquad\qquad+1.8114\times 10^{-4}\alpha^{8}+1.3556\times 10^{-6}\alpha^{10} \end{array}  \tag{94b}\] \[ \begin{array}{c}g(\alpha)=1+0.28136\alpha^{2}+4.3400\times 10^{-2} \alpha^{4}+4.3884\times 10^{-3}\alpha^{6}\\ \qquad\qquad\qquad+2.8367\times 10^{4}\alpha^{8}+9.1351\times 10^{-6}\alpha^{10} \end{array}  \tag{94c}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(\alpha\)} & \multirow{2}{*}{ \begin{tabular}{c} **Exact** \\ **Solution** \\ \end{tabular}  } & \(\varepsilon=0\) & \multicolumn{2}{c}{\(\varepsilon=8/\pi^{2}\)} \\  & & \(\text{h}=-1/10\) & \multicolumn{2}{c}{\(\text{h}=-2/25\)} \\  & & **(HAM [48])** & & **(IHAM)** & \\ \cline{2-5}  & \(\mathbf{\Theta_{b}}\) & \(\mathbf{\Theta_{b}}\) & \(\mathbf{\Delta}\) & \(\mathbf{\Theta_{b}}\) & \(\mathbf{\Delta}\) \\ \hline
0 & 0 & 0 & 0 & 0 & 0 \\ \hline
0.3 & 0.094719 & 0.094757 & 0.0403\% & 0.094782 & 0.0664\% \\ \hline
0.6 & 0.18509 & 0.18533 & 0.131\% & 0  
When the relative error \(\Delta\leq 1\%\), the convergent regions of the \(Pade[11,11]\) homotopy-Pade approximation by HAM [44] are:  
\[\alpha\in[0,4.8],\ \Theta_{b}\in[0,0.76], \tag{95}\]  
When \(n=30,\varepsilon=8/\pi^{2},\mathrm{i}=-2/25\), the \(Pade[15,15]\) homotopy-Pade approximation of \(\theta_{b}\) in Equation (92) by IHAM is:  
\[\theta_{b}=\frac{\alpha}{2}\frac{f(\alpha)}{g(\alpha)}\] (96a) where \[f(\alpha)=1\&+0.27701\alpha^{2}+4.9969\times 10^{-2}\alpha^{4}+5.2892 \times 10^{-3}\alpha^{6}+4.6124\times 10^{-4}\alpha^{8}+2.2630\times 10^{-5} \alpha^{10}\] \[\qquad\qquad\qquad+8.9473\times 10^{-7}\alpha^{12}+6.2843\times 10^{-9} \alpha^{14} \tag{96b}\] \[g(\alpha)=1+\&0.36091\alpha^{2}+6.7608\times 10^{-2}\alpha^{4}+8.4095 \times 10^{-3}\alpha^{6}+7.4211\times 10^{-4}\alpha^{8}+4.6051\times 10^{-5} \alpha^{10}\] \[\qquad\qquad\qquad+1.8474\times 10^{-6}\alpha^{12}+3.6726\times 10^{-8} \alpha^{14} \tag{96c}\]  
When the relative error \(\Delta\leq 1\%\), the convergent region of the \(Pade[15,15]\) homotopy-Pade approximation by IHAM are:  
\[\alpha\in[0,7.6],\ \Theta_{b}\in[0,0.87], \tag{97}\]  
Table 5 compares the 30 th homotopy-Pade approximation solutions by IHAM and HAM. From Table 5, the improved homotopy analysis method extends the convergence region from \(\alpha\in[0,4.8]\), to \(\Theta_{b}\in[0,0.76]\) for the homotopy-Pade approximate solution against the original homotopy analysis method [44], which is a \(58\%\) extension.
## 6 Security Solutions
### Service Security
#### 6.3.5 Analysis and Comparison  
With the yardsticks provided in Table 7, we compare the performances of authorization schemes and present the results in Table 12. We noticed that centralized authorization approaches experience reduced computation overhead, offer better interoperability, and simplify the associated access-control managements. These approaches reduce computation overhead by empowering resource-constrained devices to offload costly operations including policy assessment and verification of token status (e.g., verification of signature and ticket validity) to outside entities or proxies. Furthermore, as the contained devices are prevented from storing access policies, issuing secret credentials (e.g., keys or certificates), and even access-control lists, centralized schemes are efficient in terms of memory consumption. However, centralized approaches come with more communication overhead resulting from the information exchanges with an external entity of interest; an IoT device forwards the access token to the assigned external entity and gets an authorization decision in return. Moreover, such communications are also responsible for further delaying the overall response time associated with the respective request, which is not a desirable feature in time-sensitive IoT applications. For example, in an IoT-based healthcare system, a physician sends a request to a wearable medical device to access the latest physiological data of patients, and the request must be served immediately.  
The distributed approaches can be suitable for real-time IoT applications because the devices themselves make the authorization decision. While distributed approaches offer a benefit of desirable scalability, it becomes complicated to manage policies in the distributed devices. The interoperability between devices can be affected if the policies cannot be updated on time. The distributed approaches are also labeled underperformers from the perspective of memory efficiency, since the devices themselves need to store the associated policies, secret context, and decision rules. It is important to highlight that integrating CapBAC with a straightforward attribute-based access control approach proves to be effective in Universal Plug-and-Play (UPnP)-enabled IoT systems. This combination facilitates a more nuanced and decentralized solution, enhancing the overall system's efficiency and security [170].  
Our analysis finds that very few of the authorization schemes consider the context information for decision making. However, context information can make the decision process easier and faster, because the decision algorithm can jump to a quick resolution by only looking at the context information, such as time of access, device status, user or device location, and so forth.