esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Network Admission Control Mechanism Using Anonymous Identity-Based Cryptography
## 3 Proposed Mechanism
### Ulser Admisson Control
#### 3.4.2 IPC Process  
The verifying port uses inter-process communication (IPC) to transmit information to the communication port.  
We use a list mechanism to record node admission information. Both a blacklist and a whitelist have been set up, considering the following scenarios. If only a blacklist is used to record blocked IPs and IDs without a whitelist, the result will be that all IPs and IDs not on the blacklist are allowed, which differs from our intended configuration. On the other hand, if only a whitelist is used, active attacker IPs cannot be recorded, limiting its function solely to IP and ID admission control without additional capabilities. The process is detailed in Algorithm 1.  
```
Input: Verification results \(V\), whitelist \(W\), blacklist \(B\), network layer information \(ID_{B}\), IP, whitelist validity period \(wtime\), blacklist validity period \(btime\) Output: Modified \(W\) and \(B\)
1. If IP exists in B:
2. /* If this IP address is already in the blacklist, update the blacklist time and filter the packet */
3. For each item in B:
4. If each.ip == IP:
5. //Update the blacklist time for this IP
6. each.time = updateTime()
7. Filter packet
8. Return
9. If V is True:
10. //Verification successful, add or update entry in whitelist
11. Create a new element w in W
12. w.ip = IP
13. w.id = ID_B
14. w.time = setTime(getTime() + wtime)//Set whitelist countdown
15. W.add(w)
16. Else:
17. //Verification failed, add entry to blacklist
18. Create a new element b in B
19. b.ip = IP
20. b.id = ID_B
21. b.time = setTime(getTime() + btime)//Set blacklist countdown
22. B.add(b)
```  
**Algorithm 1** IPC Process
###### Abstract  
The subject of this study is steganographic systems that are able to hide not only the content of the transmitted information but also the fact of its transmission. In this paper, we present a novel data transmission model within such systems, combining a modified [PERSON]'s algorithm with the dynamic utilization of multiple transmission routes. Consequently, this allows the simultaneous transmission of messages through parallel groups of routes with the dynamic alternation of these groups during the information exchange process between the sender and the receiver in the steganographic network. Thus, the speed, reliability, and security of message transmission are improved. Moreover, to increase the security level, the proposed model uses a route-time matrix that is available only to the sender and receiver. Additionally, the data transmission model has been tested on synthetic data and data obtained from real devices. The evaluation of the effectiveness of the proposed model on the example of various structured steganographic systems shows the significant effectiveness of our model. As a result, in comparison with existing similar methods, the secrecy index was increased five times and the violation index was decreased four times.  
Keywords:steganography; steganographic system; steganographic network; hidden data transmission; information concealment; [PERSON]'s algorithm; routing +
Footnote †: journal: Electronics
## 2 Circuit Description
### Proposed TIA with Concurrent AGC  
In this work, we propose a novel AGC circuit that can eliminate the inevitable need for delayed feedback loops. Instead, the proposed 'concurrent AGC' circuit employs dual on-chip APDs at both input nodes of the m-TIA and the replica TIA, thereby enabling the control of the 3-bit PMOS switches in the m-TIA by the DC output voltages of the replica TIA, as described in Figure 2. This dual on-chip APD configuration helps the proposed OTA to not only achieve circuit symmetry, but also to guarantee a fast AGC function within a narrow single pulse width.  
Figure 3 shows a schematic diagrams of an m-TIA equipped with the proposed concurrent AGC circuit and the replica TIA. Here, the m-TIA comprises an on-chip APD, a voltage-mode common-source amplifier (M\({}_{1}\)) with a feedback resistor array, and a PMOS transistor load (M\({}_{2}\)) with a cross-coupled NMOS source follower (M\({}_{3}\) and R\({}_{S}\)). Therefore, the incoming optical pulses are converted into electrical currents (\(i_{pd}\)) by the on-chip APD, and these signals are then fed into the m-TIA. Particularly, the cross-coupled NMOS source  
Figure 2: Block diagram of proposed optoelectronic transimpedance amplifier (OTA).  
follower is exploited together with the PMOS load, hence enabling the output peak voltages to reach the supply voltage (V\({}_{\text{DD}}\)) [12]. Also, the feedback resistor array (\(R_{F}\) and \(R_{F(i\text{in-}1\text{-}3)}\)) is connected in parallel and operated by the PMOS switch transistors. For this purpose, the gate voltage (V\({}_{\text{C}}\)) is generated from the output of the replica TIA (M\({}_{4}\)-M\({}_{6}\)), followed by the cascaded common-source amplifiers (M\({}_{7}\), M\({}_{8}\)) before entering the gates of the PMOS switches in the m-TIA. When substantial input currents enter the m-TIA, the V\({}_{\text{C}}\) increases and effectively adjusts the equivalent feedback resistance by varying the parallel combination of resistors. Namely, this mechanism enables AGC functioning by modulating the feedback resistance in response to the amplitudes of the input currents.
## 3 Model Test Verification
### Influence of Bearing Disk Diameter  
When the horizontal load at the top of the pile is 50 N, the diameter of the bearing plate gradually decreases. Decreases of 55 mm, 45 mm, and 35 mm of the bearing plates are taken, respectively, and then the comparison of pile displacement and bending moment is shown in Figures 8 and 9.  
The horizontal displacement at the ground increases with the decrease in plate diameter. When the bearing plate diameter is 55 mm, 45 mm, and 35 mm, the horizontal displacement is 4.62 mm, 4.89 mm, and 5.03 mm, respectively. Compared with the ZPJ-35 pile with the smallest plate diameter, the deformation resistance of the ZPJ-45 pile and  
Figure 8: Displacement of pile body with different bearing plate diameters.  
the ZPI-55 pile increased by 8% and 3%, respectively. With the increase in pile depth, the horizontal displacement of the pile body decreases first and then increases and remains unchanged gradually. For piles with different plate diameters and the same plate position, the horizontal displacement is concentrated on the bearing plate, and the displacement of the pile body under the bearing plate is small and stable with depth. It can be inferred that the existence of the bearing disc plays a certain role in \"embedment,\" resulting in a sharp reduction in pile displacement and pile rotation angle. As shown in Figure 9, the diameter of the bearing plate decreases from 55 to 35 mm, and the maximum bending moment values of the pile body are 4.813 N-m, 5.271 N-m, and 5.504 N-m, respectively. Compared with the ZPI-35 pile, the resistance capacity increased by 13% and 4%, respectively. It can be seen that increasing the bearing plate diameter can balance the load and effectively reduce the maximum bending moment of the pile body. Therefore, in the actual project, the bearing plate diameter should be appropriately increased to achieve good bearing capacity. The bearing plate is set at 0.1 m from the ground. According to the figures, the bending moment of the pile body changes sharply near 0.1 m. At the same time, increasing the diameter of the bearing plate greatly reduces the maximum bending moment of the pile body and the bending moment under the plate.
# The Removal of Crystal Violet from Textile Wastewater Using Palm Kernel Shell-Derived Biochar
## 3 Results and Discussion
### Effect of Competitive Cations  
Wastewater from industries contains various contaminants, such as, acids, bases, salts, metal ions, and other inorganic compounds. The existence of cations in the wastewater contributes to a high ionic strength solution [99] that may significantly impact the adsorption capacity of BC-PKS [100]. Therefore, the effect of cations including monovalent (Na\({}^{+}\)), divalent (Ca\({}^{2+}\)), and trivalent (Al\({}^{3+}\)) ions on the adsorption of CV by BC-PKS were studied. The results as illustrated in Figure 5 demonstrates that BC-PKS adsorption of CV decreased in the presence of Na\({}^{+}\), Ca\({}^{2+}\), and Al\({}^{3+}\), indicating the impact of the cations on the adsorption of CV by BC-PKS through a competitive sorption mechanism caused by the reduction in the attraction between active sorption sites and CV molecules [101]. However, the differences in the adsorption amount were more significant with the rise of cationic concentrations because of increased competition for adsorption sites [102]. Additionally, the impact of the co-existing cations on the CV adsorption followed the decreasing order: Al\({}^{3+}\) - \(>\) Ca\({}^{2+}\) - Na\({}^{+}\) because Al\({}^{3+}\) has a higher valence charge than the monovalent (Na\({}^{+}\)) and divalent (Ca\({}^{2+}\)) cations [103].  
Figure 4: Effect of BC-PKS dosage (0.1–1.0 g) on the adsorbed CV per unit mass of BC-PKS (mg/g) and the removal percentage of CV by BC-PKS (%) (initial concentration: 400 mg/L; volume: 30 mL; adsorbent dosage: 0.1–1.0 g; reaction time: 24 h; temperature: 25 \({}^{\circ}\)C).  
Figure 5: Effect of competing cations on CV adsorption on BC-PKS (initial concentration: 400 mg/L; volume: 30 mL; adsorbent dosage: 0.5 g; reaction time: 24 h; temperature: 25 \({}^{\circ}\)C).
# Investigation of IEEE 802.16e LDPC Code Application in PM-DQPSK System
## 2 IEEE 802.16e Standard LDPC Code Construction
### LDPC Encoding Algorithm  
Leveraging the sparsity of the check matrix \(H\) for LDPC coding is an effective approach. This approach capitalizes on the fact that the majority of elements in the check matrix \(H\) are zero, leading to efficient encoding and decoding processes. By exploiting this sparsity, LDPC codes can achieve high performance with relatively low computational complexity. Furthermore, this characteristic enables the development of fast iterative coding algorithms that efficiently operate on the sparse structure of the matrix, facilitating rapid encoding and decoding of data. A fast iterative coding algorithm that takes advantage of this sparsity is described below [16].  
Encoding is the process of obtaining a check digit \(v\) from a given set of information code elements \(u\). According to the conventions of the LDPC code of the IEEE 802.16e standard, the information code element can be regarded as consisting of \(k_{b}(k_{b}=n_{b}-m_{b})\) vectors of \(z\times 1\), and the information code element is defined as \(u\). Then  
\[u=[u(0)\quad u(1)\quad\ldots\quad u(k_{b}-1)] \tag{4}\]  
where each element in \(u\) is a \(z\times 1\) column vector.  
Check code element \(v\) is composed of \(m_{b}\) vectors of \(z\times 1\).  
\[v=[v(0)\quad v(1)\quad\ldots\quad v(m_{b}-1)] \tag{5}\]  
The process of encoding can be divided into two steps:  
1. Initialize and compute \(v\) (0);
2. Recursively compute \(v\) (\(i+1\)) from \(v\) (\(i\)), where \(0\leq i\leq m_{b}-2\).  
Based on the above basic matrix \(H\), each row of the basic matrix is summed and re-accumulated to obtain \(v\) (0), as shown below.  
\[P_{p(x,k_{b})}v(0)=\sum_{j=0}^{k_{b}-1}\sum_{i=0}^{m_{b}-1}P_{p(i,j)}u(j) \tag{6}\]  
where \(x\), \(1\leq x\leq m_{b}-2\) is the number of rows of non-zero term of the basic matrix \(H\). \(P_{i}\) denotes a cyclic right shift of \(i\) bits for a unit matrix of \(z\times z\). There is  
\[v(0)=P_{p(x,k_{b})}^{-1}\left(\sum_{j=0}^{k_{b}-1}\sum_{i=0}^{m_{b}-1}P_{p(i,j )}u(j)\right) \tag{7}\]  
And because \(P_{i}\) denotes the cyclic right shift of the unit matrix of \(z\times z\) by \(i\) bits, \(P_{p(x,k_{b})}^{-1}=P_{z-p(x,k_{b})}\).  
The expression for \(v(i)\) can be obtained by summing each row of the basic matrix, where \(1\leq i\leq m_{b}-1\).  
\[v(1)=\sum_{j=0}^{k_{b}-1}P_{p(i,j)}u(j)+P_{p(i,k_{b})}v(0),i=0 \tag{8}\]  
\[v(i+1)=v(i)+\sum_{j=0}^{k_{b}-1}P_{p(i,j)}u(j)+P_{p(i,k_{b})}v(0),i=1,m_{b}-2 \tag{9}\]  
where, when \(i=-1\), \(P_{i}=0_{z\times z}\).
# Investigation of Failures during Commissioning and Operation in Photovoltaic Power Systems
## 3 Results and Discussion  
In this section, the outputs of the faults created in the MATLAB Simulink program are given, and the results obtained are compared with other studies.  
The modeled photovoltaic power plant is connected to the busbar of the facility at the 34.5 kV medium voltage level within a real-time facility and is subsequently connected to the grid via the grid connection point. It includes a central inverter with an installed power of 1.8 MW connected to the grid.  
Photovoltaic cells are used in monocrystalline cell types. For each photovoltaic panel used in the photovoltaic power plant; the maximum power is 450 W, the maximum power point voltage is 41.4 V, the maximum power point current is 10.87 A, the open circuit voltage is 49.79 V, and the short circuit current is 11.44 A. There are 26 series-connected photovoltaic modules in each array and 15 parallel-connected arrays in each combiner box. A total of 10 combiner boxes are used. In this configuration, the maximum input voltage of the inverter is 1500 V DC, and the rated voltage is 1100 V DC. The output voltage of the inverter is 500 V AC. A step-up transformer with a ratio of 0.5/34.5 kV is used at the grid connection point of the inverter to transmit the inverter output voltage to the grid. While modeling fault situations, the air temperature is set to 25 \({}^{\circ}\)C and the radiation is set to 1000 W/m\({}^{2}\) to keep the environmental effects constant in every failure situation and to identify the situation where power production is at maximum efficiency.  
Details about photovoltaic panels are shown in Figure 3.  
As a result of modelling the faults on MATLAB Simulink, the graphs in Figures 4 and 5 were obtained.  
Figure 3: PV panel details.  
Figure 4: DC measurements. (**a**) Grid-sourced phase–phase short-circuit fault, (**b**) grid-sourced open-circuit fault, (**c**) inverter open-circuit fault, (**d**) DC open-circuit fault, (**e**) DC ground fault, (**f**) DC line-to-line short-circuit fault, and (**g**) PV module fault.  
Figure 5: AC measurements. (**a**) Grid-sourced phase–phase short-circuit fault, (**b**) grid-sourced open-circuit fault, (**c**) inverter open-circuit fault, (**d**) DC open-circuit fault, (**e**) DC ground fault, (**f**) DC line-to-line short-circuit fault, and (**g**) PV module fault.
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 1 Introduction  
Global and local contrasts are often imperfect in images. The available dynamic range of the display is either not utilized, or, on the contrary, the range is wider than the low dynamic range display.  
Medical images, particularly Computed Tomography (CT) images, are challenging to visualize. First, artefacts could lead to inferior diagnostic performance [1]. Second, a lack of good local contrast could also limit diagnostic performance; local contrast enhancement can improve diagnostic efficiency [2] or significantly reduce interpretation times [3]. Third, CT images have low soft tissue contrast, a relatively high noise level compared to magnetic resonance imaging (MRI), and they have a high dynamic range (approximately 12 bits) [4].  
CT images represent absolute tissue densities using CT numbers that are measured in Hounsfield units (HU) [5]. This calibrated nature of the CT imaging makes it appealing to use global, monotonic tone mapping operators (TMO) which assign the same color to pixels representing the same tissue density, regardless of the location of the pixels.  
Specialized protocols are developed for diagnosing specific pathologies. However, the need for image post-processing in order to obtain better local contrast [6; 7; 8] dates back decades.  
In the following, we briefly overview the most important approaches for global and local contrast enhancement approaches for CT images before we present our proposed local method.
## 4 Simulation Results
### Comparative Results  
Table 3 summarizes the main figures of merit for the DMTJ and SMTJ SIMPLY and sFALSE schemes operating at 300 K and 77 K. The DMTJ-based (SMTJ-based) SIMPLY operation at 77 K presents RM and RM\({}_{3\sigma}\) improvements by a factor of 2.3\(\times\) and 2.4\(\times\) (2.5\(\times\) and 2.6\(\times\)), respectively, as compared to the room temperature operation. As for the sFALSE operation, the DMTJ-based (SMTJ-based) design presents 2.2\(\times\) (2.3\(\times\)) higher RM\({}_{3\sigma}\) at 77 K, as compared to the counterpart operating at 300 K. These results highlight the considerable improvement in terms of the reliability of SIMPLY LIM at cryogenic temperatures.  
The higher RM comes at the cost of increased switching currents (see Table 1). This leads to a reduction in performance and an increase in consumed energy. The read energy (E\({}_{\text{read}}\)) of the DMTJ-based (SMTJ-based) scheme for the SIMPLY and sFALSE operations increases by about 1.7\(\times\) and 1.4\(\times\) (4.5\(\times\) and 1.3\(\times\)), respectively, as compared to 300 K operation. As for the write energy (E\({}_{\text{write}}\)), when operating at 77 K, the DMTJ-based scheme  
Figure 6: \(V_{\text{G}}\) statistical distributions for the sFALSE scheme based on (**a**) SMTJ and (**b**) DMTJ devices. The results are obtained when the comparator detects **Q** = '0’ (green and blue lines) and **Q** = '1’ (orange and red lines) referred to the preliminary read operation under process variations at 300 K and 77 K. Tail transistor \(Mn\) size: **Wn**/**Ln** = 1 \(\upmu\)m/8 \(\upmu\)m. \(V_{\text{READ}}\) = 1.1 V and Vbias = 1 V. \(\upmu\) represent the mean values for each case, with \(\sigma\) being the standard deviation of \(V_{\text{G}}\) distributions.  
consumes only 4% more energy than at 300 K, while its SMTJ-based counterpart is more than twice as energy-hungry.  
Figure 7 compares the average energy consumption (averaged on all input combinations) of DMTJ- and SMTJ-based schemes. The DMTJ-based scheme is always the preferable option, mainly due to the lower write energy.
# Adapative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection
## 2 Related Work
The authors start by applying a feature selection algorithm to obtain the most relevant features for the distinction between benign network traffic and each cyber-attack. Then, the values of the remaining features, which are considered irrelevant for the classification, are perturbed by a Wasserstein generative adversarial network (WGAN) [34]. On the condition that there are no class-specific constraints for the remaining features, this approach could improve the coherence of an example with its class. Nonetheless, the unconstrained perturbations created by WGAN disregard the domain structure, which inevitably leads to invalid examples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Setting** & \begin{tabular}{c} **Supported** \\ **Models** \\ \end{tabular} & \
\begin{tabular}{c} **Domain** \\ **Constraints** \\ \end{tabular} &
\begin{tabular}{c} **Class-Specific** \\ **Constraints** \\ \end{tabular} \\ \hline FGSM [3] & White-box & ANN & ✗ & ✗ \\ C\&W [22] & White-box & ANN & ✗ & ✗ \\ DeepFool [23] & White-box & ANN & ✗ & ✗ \\ Houdini [24] & White-box & ANN & ✗ & ✗ \\ StrAttack [25] & White-box & ANN & ✗ & ✗ \\ ZOO [26] & White-box & ANN & ✗ & ✗ \\ JSMA [27] & White-box & ANN & ✓ & ✗ \\ Polymorphic [28] & Gray-box & ANN & ✗ & ✓ \\ Reconstruction [29] & Gray-box & ANN & ✗ & ✗ \\ OnePixel [30] & Black-box & CP & ✓ & ✗ \\ RL-S2V [31] & Black-box & CP & ✗ & ✗ \\ BMI-FGSM [32] & Black-box & Any & ✗ & ✗ \\ GAN [33] & Black-box & Any & ✗ & ✗ \\ WGAN [34] & Black-box & Any & ✗ & ✗ \\ Boundary [35] & Black-box & Any & ✗ & ✗ \\ Query-Efficient [36] & Black-box & Any & ✗ & ✗ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of relevant methods and addressed constraint levels. On the other hand, both the Jacobian-based saliency map attack (JSMA) [27] and the OnePixel attack [30] could potentially preserve a domain structure. The former was developed to minimize the number of modified pixels in an image, requiring full access to the internal gradients of an ANN, whereas the latter only modifies a single pixel, based on the class probabilities predicted by a model. These methods perturb the most appropriate features without affecting the remaining features, which could be beneficial for tabular data. However, neither validity nor coherence can be ensured because they do not account for any constraint when creating the perturbations. To the best of our knowledge, no previous work has introduced a method capable of complying with the fundamental constraints of domains with tabular data, which hinders the development of realistic attack and defense strategies. This is the gap in the current literature addressed by the proposed method.
## 5 Numerical Examples
Subsequently, the correlations between any two macro elastic mechanical parameters are evaluated, where the results are listed in Table 3.  
By comparing the correlation coefficients listed in Table 3, it can be found that the results obtained by the non-probabilistic [PERSON]'s model can achieve the same correlation relationships as the probabilistic [PERSON]'s model referenced by [PERSON] [29] and the values are almost identical. Here, the symbol '+' denotes the positive correlation and the symbol '-' denotes the negative correlation. However, for the non-probabilistic combined quantification model, the results show that the correlation relationships between any two macro elastic parameters are closely identical, except that the correlation relationship between the transverse tension modulus \(E_{2}\) and the Poisson's ratio \(v_{12}\) is contrary to [PERSON]'s model. As a result, we will make good use of some simulated sample points obtained by the non-probabilistic [PERSON]'s model to model macro-mechanical parameters as a closed and bounded convex polyhedral model, which can be plotted in Figure 4. Here, the blue boundary line is the convex polyhedral model based on the developed grey mathematical theory via some sample points. The red solid points are simulated sample points by Monte Carlo simulation methods through the non-probabilistic [PERSON]'s model and the number of simulated points is 30,000. It is easily found that almost all simulated points can be \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Reference [29]**} & \multicolumn{4}{c}{**Proposed in This Paper**} \\ \cline{3-6}  & & **Series Model** & **Parallel Model** & **Combined Model** & **Huang’s Model** \\ \hline \(E_{1}(\text{GPa})\) & 142.250/0.052 & 142.510/0.090 & 142.510/0.090 & 142.510/0.090 & 142.510/0.090 \\ \(E_{2}(\text{GPa})\) & 8.690/0.040 & 7.100/0.073 & 9.650/0.083 & 8.430/0.066 & 8.670/0.070 \\ \(v_{12}\) & 0.240/0.037 & 0.244/0.063 & 0.201/0.085 & 0.236/0.064 & 0.244/0.063 \\ \(G_{12}(\text{GPa})\) & 4.38/0.050 & 3.59/0.093 & 9.512/0.088 & 4.46/0.076 & 4.37/0.088 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The quantification results under different models. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Correlation Coefficients**} \\ \cline{2-4}  & **Reference [29]** & **Combined Model** & **Huang’s Model** \\ \hline \(E_{1},E_{2}\) & +0.172 & +0.428 & +0.168 \\ \(E_{1},v_{12}\) & \(-0.087\) & \(-0.099\) & \(-0.085\) \\ \(E_{1},G_{12}\) & +0.241 & +0.226 & +0.241 \\ \(E_{2},v_{12}\) & +0.011 & \(-0.114\) & +0.013 \\ \(E_{2}  
enveloped in the convex polyhedral model and the enveloping rate can be up to 99.9%. In other words, the proposed quantification method on the basis of grey mathematical theory and the micro-macro analysis model can give a very credible and reasonable evaluation domain for macro elastic mechanical parameters of composites. It can be a powerful and efficient tool for uncertainty quantification of composite laminates.