esa-sceva/satcom-chunk-collection huggingface dataset:
# Bit Independence Criterion Extended to Stream Ciphers
###### Abstract  
The bit independence criterion was proposed to evaluate the security of the S-boxes used in block ciphers. This paper proposes an algorithm that extends this criterion to evaluate the degree of independence between the bits of inputs and outputs of the stream ciphers. The effectiveness of the algorithm is experimentally confirmed in two scenarios: random outputs independent of the input, in which it does not detect dependence, and in the RC4 ciphers, where it detects significant dependencies related to some known weaknesses. The complexity of the algorithm is estimated based on the number of inputs \(l\), and the dimensions, \(n\) and \(m\), of the inputs and outputs, respectively.  
bit independence criterion; bit independence; RC4; stream cipher; complexity Article
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 6 Simulation Examples
### Channel Estimation Techniques for the IEEE 802.11p Standard
#### 6.1.1 Least Squares Estimator  
The LS channel estimation over the \(k\)-th OFDM symbol and the \(m\)-th subcarrier is given by:  
\[\langle\mathbf{\hat{H}}_{zz}^{\mathrm{d}}\rangle_{m,k}=\frac{\langle\mathbf{ Y}^{\mathrm{e}}\rangle_{m,1}+\langle\mathbf{Y}^{\mathrm{e}}\rangle_{m,2}}{2 \times\langle\mathbf{X}^{\mathrm{e}}\rangle_{m,1}} \tag{15}\]  
for \(m=1,2,\ldots,N_{p}\) and \(k=1,2,\ldots,F\), where \(\mathbf{\hat{H}}_{zz}^{\mathrm{d}}\) represents the estimated channel matrix. From (15), we can see that the LS estimator is used when we assume a time-invariant channel for the transmission of the whole frame, so that the estimated channel \(\langle\mathbf{\hat{H}}_{zz}^{\mathrm{d}}\rangle_{m,k}\) is the same for all \(k\), i.e., for all OFDM information data symbols. After the channel estimation stage, the received OFDM symbols pass to an equalization process. Considering zero-forcing equalization, the equalized signal is given by:  
\[\langle\mathbf{\hat{Y}}_{zz}^{\mathrm{d}}\rangle_{m,k}=\frac{\langle\mathbf{ Y}^{\mathrm{d}}\rangle_{m,k}}{\langle\mathbf{\hat{H}}_{zz}^{\mathrm{d}} \rangle_{m,k}} \tag{16}\]  
for \(m=1,2,\ldots,N_{p}\) and \(k=1,2,\ldots,F\), where \(\mathbf{\hat{Y}}_{zz}^{\mathrm{d}}\) is the equalized data matrix.
# Model Test Analysis of Groundwater Level Fluctuations on
## 1 Introduction
It is difficult to understand the process of karst collapse, which leads to an inability to fully and deeply cognize the mechanism of collapse formation [28], so it is necessary that the formation process and evolution mechanism of karst collapse can be dynamically observed with the help of indoor physical model tests. At the same time, indoor model tests are also one of the effective methods to verify the existence of the proposed collapse theory [29]. For example, [PERSON] et al. [30] established a cylindrical soil collapse model and derived the calculation method of stability coefficients for four different working conditions based on the limit equilibrium theory. [PERSON] [31] carried out physical model tests on karst collapse with clay overburden and obtained a critical collapse width of 2-3 mm. However, the model device collapse hole was in contact with the atmosphere and in an unpressurized state, which did not match the actual karst collapse opening or collapse pipe in a pressurized state. [PERSON] et al. [32] established a physical model based on the exploration data of a collapse site, and conducted a series of tests mainly for the binary structure (clay in the upper layer and sandy soil in the lower layer). It was concluded that fluctuations in the karst water table were the main cause of the induced karst collapse, but the process of collapse formation could not be quantified. Although the use of physical model tests also verified the validity of some of the collapse theories and reproduced the process of karst collapse, which improved the understanding of the evolution of the karst collapse mechanism, there are still many questions on how to quantify the process of karst collapse, especially how to obtain the extent of cover collapse during the collapse process. Therefore, in this paper, clayey soils are selected as the cover layer, and physical model tests are carried out to simulate the collapse process by vacuum suction and collapse process by positive pressure airburst, and to simulate the cover deformation at the same density and supply and drainage rate for five different cover thicknesses respectively. Therefore, this paper takes the one-dimensional structural overburden in the karst collapse-prone area of Lingui District, Guilin City as the research object. Based on the previous research results, a physical model of the overburden type is constructed, a typical test model of groundwater level fluctuation is established and model tests are carried out. In this study, the effects of rainfall and other recharge and cavity supply and drainage on the settlement process of the overlying karst cover are simulated. The effect of groundwater level fluctuations on the cover deformation was then analyzed by means of the cover displacement. This study provides a theoretical and technical basis for practical engineering activities in covered karst areas.
## 5 Experiment
### Evaluation

Table 4: Performance comparison under different TCP congestion control algorithms. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**DelACK** & **QoS Algorithm** & **Sta1** & **Sta2** & **Sta3** & **Total Throughput** \\ \hline
1 & Default & 6.86 & 6.8 & 6.9 & 20.57 \\
1 & General Airtime Fairness & 13.98 & 4.45 & 3.09 & 21.61 \\
1 & Our Airtime Fairness & 8.46 & 7.37 & 5.32 & 21.16 \\
2 & Default & 11.76 & 6.83 & 7.52 & 26.11 \\
2 & General Airtime Fairness & 14.45 & 7.32 & 5.09 & 26.87 \\
2 & Our Airtime Fairness & 11.33 & 8.83 & 6.45 & 26.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Throughput comparison of QoS algorithms in UL scenario. to other scenarios. In contrast, our airtime fairness algorithm consistently achieves high fairness across different scenarios, owing to its precise calculation of each station's airtime. In summary, the results of all the experiments reveal that the general airtime fairness algorithm calculates transmission airtime based on frame size and wireless rate, which is then used in the scheduling algorithm. This approach allows stations with higher wireless rates to have a greater opportunity to occupy the wireless channel, leading to a positive correlation between the throughput of each node and its wireless rate, thereby increasing total throughput. However, this approach presents two main issues:  
**Inaccurate Airtime Calculation:** As the modulation and coding scheme increases under the same wireless protocol, the wireless transmission rate speeds up, reducing the proportion of time needed for data transmission. When the wireless rate is high, the general airtime fairness algorithm's airtime calculation only reflects a fraction of the total time cost. This method significantly underestimates the actual airtime for nodes with higher rates, erroneously allocating them more transmission opportunities, which results in a poor airtime fairness index. **TCP ACK Considerations:** TCP data packets require the return of TCP ACKs. Given that the wireless channel operates in half-duplex mode, the airtime cost associated with ACKs should be included in the total airtime calculation for the corresponding TCP data frame. The airtime for a single TCP data frame should therefore include both the forward data packet and the reverse ACK. This total airtime should be pre-calculated during AP scheduling to inform the scheduling logic. Our algorithm better reflects the concept of airtime fairness by incorporating these considerations.
# Graphene-Based Membranes for CO2/CH4 Separation: Key Challenges and Perspectives
## 2 Nanoporous Singe-Layer Graphene  
A defect-free single-layer graphene nanosheet is impermeable to gas molecules [19; 20]. To enable single-layer graphene as a membrane, there is a need to generate nanopores of optimized pore size for separation by a molecular sieving mechanism. The kinetic diameters of CO2 and CH4 molecules are 3.30 and 3.82 A, respectively [5]. In this context, therefore, it implies that the ideal pore size of single-layer graphene membrane should be between these kinetic diameters so that CO2 molecules are allowed to permeate through, while the CH4 molecules are retained. There are several etching techniques used to  
Figure 1: Three mainstream graphene-based membrane designs, namely, (**a**) nanoporous single-layer graphene, (**b**) few- to multi-layer stacked laminate, and (**c**) mixed-matrix membrane, which is the focus of this article.  
generate nanopores on single-layer graphene nanosheet to date, including ion bombardment followed by chemical oxidation [21], focus-ion beam (FIB) patterning [10], gold nanoparticle deposition followed by oxidation [22], oxygen plasma [23] with ozone etching [24], and ultraviolet-induced oxidative treatment [25]. Each method utilizes etching to first create defects on the pristine single-layer graphene which can then be aggravated into nanopores. There appears no systematic control over the pores sizes by using different techniques. For example, [PERSON] et al. generated subnanometer pores of size 3.4 to 5 A using ultraviolet-induced oxidative treatment to obtain high ideal CO2/CH4 selectivity at \(9.0\times 10^{3}\)[25]. [PERSON] et al. used an FIB patterning method to create pores that were in the range of 7.6 to 1000 nm, which showed the highest H2/CO2 mixed gas selectivity of 3.4 using the graphene membrane containing 7.6 nm pores [10].
## 3 Results
### Establishment of Optimal Extraction and Partitioning Conditions  
The QuEChERS extraction methodology predominantly employs acetonitrile (MeCN) as its extraction solvent [21]. MeCN, a polar organic solvent, exhibits excellent miscibility with pure water, and recent studies have investigated enhancing the extraction efficacy of pesticides by incorporating EA, a solvent with lower polarity, into the extracts [22; 23]. To determine the most effective extraction and partitioning conditions for the residue of tricyclazole in brown rice, this study compared the pesticide recovery rates using three different extraction solvents: MeCN, a 1:1, \(v/v\) mixture of MeCN/EA and EA, and two types of QuEChERS salts (Original and EN 15662), as depicted in Table 1.  
MeCN yielded the highest recovery rates, especially when used with Original QuEChERS salts. EA alone resulted in the lowest recovery rates. The type of partitioning salt also impacted the results, with Original QuEChERS salts generally outperforming EN 15662 salts across all solvent combinations. This could indicate that the composition of the EN 15662 salts may not be as effective for tricyclazole recovery in the conditions tested. The observed trends in brown rice align with findings from biological matrices such as serum and urine [24; 25], where salts without pH buffers have been reported to deliver better recovery rates than buffered salts in multiresidue analysis. This consistency across different matrices underscores the reliability of original QuEChERS salts in diverse analytical contexts. When comparing solvents, a mix of MeCN/EA yielded higher recovery rates than using EA alone, suggesting that the polarity of the solvent may play a significant role in the efficiency of tricyclazole extraction.  
In all conditions, recovery rates fell within the acceptable range of 70-120%. Utilizing analysis of variance (ANOVA), the significance of differences among treatments was confirmed. It indicated that Methods No. 1-3 showed a significant increase in recovery rates compared to Methods No. 4-6 (\(p<0.05\)), prompting further comparative analyzes of the extraction efficiencies of methods No. 1-3.
## 4 Optimization Methods
### Objective Functions and Constraints  
In present work, \(j\) factor, \(f\) factor, \(JF\) factor, entropy generation rate \(\dot{S}_{\text{gen}}\) and total annual cost \(C_{\text{total}}\) are optimization goals and presented as follows:  
Figure 2: Flowchart of the particle swarm optimization (PSO) algorithm.  
\[j=F_{1}(L_{\rm f},L_{\rm e},\theta,H,\delta_{\rm plate},L_{\rm h})=F_{1}(x_{1},x _{2},x_{3},x_{4},x_{5},x_{6})=F_{1}(X) \tag{29}\]  
\[f=F_{2}(L_{\rm f},L_{\rm e},\theta,H,\delta_{\rm plate},L_{\rm h})=F_{2}(x_{1},x _{2},x_{3},x_{4},x_{5},x_{6})=F_{2}(X) \tag{30}\]  
\[JF=F_{3}(L_{\rm f},L_{\rm e},\theta H,\delta_{\rm plate},L_{\rm h})=F_{3}(x_{1}, x_{2},x_{3},x_{4},x_{5},x_{6})=F_{3}(X) \tag{31}\]  
\[\dot{S}_{\rm gen}=F_{4}(L_{\rm f},L_{\rm e},\theta,H,\delta_{\rm plate},L_{\rm h })=F_{4}(x_{1},x_{2},x_{3},x_{4},x_{5},x_{6})=F_{4}(X) \tag{32}\]  
\[C_{\rm total}=F_{5}(L_{\rm f},L_{\rm e},\theta,H,\delta_{\rm plate},L_{\rm h })=F_{5}(x_{1},x_{2},x_{3},x_{4},x_{5},x_{6})=F_{5}(X) \tag{33}\]  
The optimization objective in present work is to obtain the \(j_{\rm max},f_{\rm min},\) and \(J\)\(F_{\rm max},\) minimum \(\dot{S}_{\rm gen},\) and minimum \(C_{\rm total}.\) The values \(x_{1},\)\(x_{2},\)\(x_{3},\)\(x_{4},\)\(x_{5}\) and \(x_{6}\) in Equation (29) to Equation (33) are the optimized variables in the objective functions, and correspond to \(L_{\rm f},L_{\rm e},\theta,H,\delta_{\rm plate}\) and \(L_{\rm h}.\)  
The design variables and the constraint conditions are listed in Table 3.  
In present study, to take in to account the effect of constraint violation during the optimization process, an arbitrarily large value (known as penalty function) is also added. The fitness function is defined as Equation (34) [37]:  
\[fitness=\Phi(X)=\left\{\begin{array}{ll}F_{t}(X)&x\,\,\mbox{is in the feasible region}\\ F_{t}(X)+(-1)^{\Omega}M&\sum\limits_{u=1}^{12}[g_{u}(X)]^{2}&x\,\,\mbox{is out of the feasible region}\end{array}\right. \tag{34}\]  
where the penalty coefficient \(M=500,\)\(g_{u}(X)\) is penalty function. \(\Omega\) equal to 0 or 1 depend on the objective function \(F_{t}.\) In this work, when the objective function aim at achieving \(f_{\rm min},\) minimum \(\dot{S}_{\rm gen}\) and minimum \(C_{\rm total},\)\(\Omega=0,\) and \(\Omega=1\) when \(F_{t}\) is to obtain \(j_{\rm max}\) and \(J\)\(F_{\rm max}.\)
## Calibration Process
### Camera Calibration  
In fact, the camera calibration implies that the robot is rotating around its axis and taking pictures of all the viewable chessboards in turn. In this case, the ability to make several \"passes\" during the shooting process should be provided for, to control which of the boards the robot is currently observing and in which direction it should turn.  
As a result, the algorithm can be represented as a sequence of actions: \"get a frame from the camera\" and \"turn\". The important points to consider are:  
1. Which board is currently being viewed;.
2. Which direction to turn;
3. When to stop;
4. What to do if the chessboard is not found in the picture.  
Thus, it was decided to represent the robot's state during the camera calibration with the following set of values.  
1. The number of the board being observed (the boards are numbered starting from 0 counterclockwise). The initial value is determined by the robot's position;
2. The direction of the robot's rotation. The initial value can be arbitrary; for definiteness, it is assumed to be clockwise;
3. Is there a chessboard in the frame? The initial value is \"true\", since the initial position of the robot suggests that the board is in the camera's field of view;
4. Is the robot in recovery mode? The recovery mode refers to a situation where the robot does not observe the chessboard, but knows where to move in order to find it. This can be either in a situation of transition from board to board or when the robot has turned so that it is not already observing the extreme board.  
The algorithm of transition from state to state algorithm is visualized in Figure 2.  
The robot's rotation is performed by giving appropriate commands to its wheels. Since the wheels of the robot are not supposed to be calibrated at a given moment, it is impossible to apply speed V to the left wheel or V to the right wheel and be sure that the robot will rotate relative to its center. Therefore, it was decided to give commands only to one of the wheels. This ensures that the robot rotates relative to the point of contact of the second wheel with the floor surface. To make the image from the camera sharp, the robot moves in small steps. To do this, the minimum speed possible for the robot to move for a certain quantum of time (0.1 s) is fed to the wheel, and after that the robot stops, and only after that receives an image.  
The final algorithm comprises the following sequence of actions:  
1. Obtain frame from the camera;
2. Find a chessboard on the camera frame;
3. Save information about board corners found in the image;
4. Determine the direction of rotation according to the schedule;
5. Make a step;
6. Either repeat the steps described above, or complete the data collection and proceed with the camera calibration using OpenCV.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 3 Analytical Procedure
### Random Communications Ranges
#### 3.2.3 Weibull Fading Channel Effect  
The range-based distribution in a Weibull fading channel follows with CDF given as;  
\[F_{R}^{\text{Weib}}(r)=1-\exp\left(-\frac{\Gamma\big{(}1+\frac{2}{\bar{\zeta}} \big{)}\gamma_{th}P_{N}r^{\alpha}}{P_{0}P_{t}}\right)^{\frac{\zeta}{2}}, \tag{14}\]where \(C\) is Weibull fading parameter [22] in the range \([0,\infty]\), \(\alpha\) is the path-loss exponent in the range \(2,6\). \(C=2\) corresponds to a Rayleigh fading channel, \(P_{0}\) is path-loss constant, and \(\Gamma(.)\) is the gamma function. As before, we find the CDF as;  
\[F_{R}^{\text{Weib}}(r)=\begin{cases}1-\exp(-\Lambda r^{\hat{\alpha}}),\text{ w}/\text{ BS}\\ 1-\exp(-\Lambda r^{\hat{\alpha}}),\text{ BS}\end{cases}\] (15a) where \[\hat{\alpha}=\frac{\alpha C}{2}\], \[\Lambda_{c}=\left(\Lambda\Gamma(1+\frac{2}{C})\right)^{\frac{C}{2}}\], and \[\hat{\Lambda}_{c}=\left(\dot{\Lambda}\Gamma(1+\frac{2}{C})\right)^{\frac{C}{2}}\]. Then the PDF, given by \[f_{R}^{\text{Weib}}(r)=\frac{\text{d}F_{R}^{\text{Weib}}(r)}{\text{d}r}\] is; \[f_{R}^{\text{Weib}}(r)=\begin{cases}\hat{\alpha}\Lambda_{c}r^{\hat{\alpha}-1} \exp(-\Lambda_{c}r^{\hat{\alpha}}),\text{ w}/\text{BS}\\ \hat{\alpha}\hat{\Lambda}_{c}r^{\hat{\alpha}-1}\exp(-\hat{\Lambda}_{c}r^{\hat{ \alpha}}),\text{ BS}\end{cases}\] (16a) (16b)  
(16a) for w/BS and (16b) with bus shadowing effects, respectively. The expectation and variance respectively are;  
\[\mathbb{E}[R]=\frac{\Gamma\big{(}\frac{1}{\hat{\alpha}}\big{)}}{\hat{\alpha} \Lambda_{c}^{1/\hat{\alpha}}}\text{ and }\mathbb{V}ar[R]=\frac{\Gamma\big{(}\frac{1}{\hat{ \alpha}}\big{)}\big{(}\hat{\alpha}2^{2/\hat{\alpha}}\Gamma\big{(}\frac{1}{2}+ \frac{1}{\hat{\alpha}}\big{)}-\sqrt{\pi}\Gamma\big{(}\frac{1}{\hat{\alpha}} \big{)}\big{)}}{\sqrt{\pi}\hat{\alpha}^{2}\Lambda_{c}^{2/\hat{\alpha}}}.\]  
By using the corresponding big vehicle shadowed channel scenario, we swap \(\hat{\Lambda_{c}}\) instead of \(\Lambda_{c}\).
# High-Speed Photodetectors for Microwave Photonics
## 4 High-Power Photodiodes on Si
### Heterogenous Photodiodes  
Heterogeneous silicon photonics, i.e the integration of group III-V materials onto silicon, benefits from the mature Si processing technology, while fully exploiting the high-performance of III-V materials. Since bandgap-engineering is available in III-V semiconductors, complex photodiode heterostructures can be designed, which have been shown to enable high-power high-linearity analog applications [5]. To date, three approaches have been reported for the heterogeneous integration of III-V photodiodes on silicon: (i) molecular bonding [36], (ii) adhesive bonding [37], and (iii) III-V material growth on Si [38]. Previously, it has been demonstrated that molecular die and wafer bonding are technologies that combine different materials without compromising their properties. These approaches have produced high-performance photodiodes with a low dark current, high responsivity at 1.55 \(\upmu\)m wavelength, high speed, and high power [39; 40]. In [39], waveguide MUTC PDs were fabricated from an InGaAs/InP die that was wafer-bonded onto silicon-on-insulator (SOI). The PDs had an internal responsivity of 0.95 A/W, with a very low dark current of 10 nA (2.9 mA/cm\({}^{2}\)), a bandwidth of 48 GHz, and high RF output power of 12 dBm at 40 GHz. In ref. [40], this work was extended to waveguide photodiodes with a bandwidth of 65 GHz. Figure 12a,b shows the device structure and the measured frequency responses, respectively. Unlike some earlier work, an inverted photodiode layer stack was used, which resulted in the p-contact being on top after dye bonding. To enable efficient optical coupling from the silicon waveguide through the low-index InP drift layer into the absorber, the Si waveguide was designed to be 300 nm wide (inset of Figure 12b). It follows that the optical mode is no longer confined. Instead, it is pushed upwards into the active photodiode region. It should be noted, that the heterogeneous integration process, which allows changing the widths of the PD mesa and the Si waveguide independently, enabled this design. Responsivity and dark current were 0.84 A/W at 1.55 \(\upmu\)m and 1 nA at \(-\)3 V, respectively. The output power was \(-\)2 dBm at 70 GHz for a PD with an area of 75 \(\upmu\)m\({}^{2}\)[40].
## 3 Results and Discussion
### Tolerance of the Microalgae to Raw Whey (RW) Wastewater
Notably, previous studies have highlighted the metabolic capacity of _Chlorella_ and _Scenedesmus_ species to assimilate lactic acid [45; 46]. Therefore, it is reasonable to infer that a comparable phenomenon of lactic acid assimilation could have taken place in the microalgae cultures, thus providing additional support for the hypothesis. Despite the non-axenic nature of the cultures, microscopic analysis revealed a notable absence of thriving contaminating microorganisms such as bacteria, fungi, or yeasts. The genera _Chlorella_ and _Scenedesmus_ have also proven effective in recovering nutrients from various types of wastewaters rich in compounds containing essential elements such as nitrogen, phosphorus, or sulfur [12]. This is the case of RW, which has concentrations of nitrate, phosphate, and sulfate of 0.3 times, 54.4 times, and 11.7 times those contained in the BG11 medium (see Table 1). Overall, both duckweed and microalgae, as demonstrated in flask culture experiments, exhibit the ability to remediate contaminants present in cheese whey wastewater when diluted up to 30%, eliminating the need for additional nutrients found in the BG11 medium (control). Consequently, both biological systems can effectively treat the effluent independently. The selection between microalgae and duckweed biomass depends on the specific application and economic viability for the intended purpose. Each exhibits unique characteristics that render them valuable biomass sources in distinct contexts [47]. Microalgae, rich in protein, essential fatty acids, vitamins, and antioxidants, are valuable for food, feed, and nutraceutical applications. Similarly, duckweed, also rich in protein, serves as a rapid plant source for animal feed, rapidly doubling its biomass. Cultivation costs vary depending on the system used, with economic assessments showcasing diverse applications for both microalgae and duckweed, including biofuels, animal feed, supplements, bioplastics, and wastewater treatment. The ultimate biomass value depends on market demand.