esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Particle Swarm Optimization (PSO) Algorithm Employing Quasi-Random Numbers
## 4 Discussion  
We have augmented the PSO algorithm by integrating Sobol and Halton random number samplings to achieve superior results. Our rationale behind this enhancement is rooted in Sobol and Halton random numbers, which are quasi-random number types. These numbers are generated in such a manner that they are uniformly distributed across multidimensional space, thus mitigating clustering or bias in particle movement. Consequently, this facilitates more extensive exploration, thereby increasing the likelihood of avoiding local optima and accelerating the discovery of the global optimum, as a more significant portion of the space can be explored when biases or clusters are circumvented.  
The outcomes indicate that the enhancement applied to the standard PSO through the utilization of quasi-random numbers has consistently improved the number of iterations required for the algorithm to produce the optimal function value across all three benchmark functions. The efficiency gains for continuous functions are more pronounced than those for mixed variable functions.  
Figure 8: TSP with 20 cities—convergence plot.  
Figure 7: TSP with 15 cities—convergence plot.  
Additionally, including more decision variables in the problem correlates with more significant improvements in general. As evident from the data, the application of Sobol or Halton sequences to the standard PSO algorithm demonstrates efficiency improvements, suggesting the potential benefits of these sequences to researchers in various optimization fields. Notably, this technique is algorithm-agnostic, as indicated in the introduction, and can thus be employed with other optimization algorithms such as the Genetic Algorithm, Ant Colony Optimization (ACO) algorithm, and other established optimization algorithms.
## 3 Results
### Water Immersion Response in Pulse-Echo Mode
#### 3.2.2 Time Domain Response  
All measurements presented in this section were performed as described in Section 2.3.2 of methods for transducer characterization.  
* Flat and focused transducers.  
The received echoes for the HS03 flat and focused transducers corresponding to the experimental set-up shown in Figure 3 are shown in Figure 9. For the flat transducer it is possible to see the first reflection from the water/steel surface (echo #1 in Figure 5) that appears at \(\sim\)57 \(\upmu\)s (Figure 9, top), and the first reverberation in this cavity (transducer-reflector), at \(\sim\)115 \(\upmu\)s (Figure 9, top). In addition, echoes from: (i) the back surface of the steel block (echo #2 in Figure 5), at \(\sim\)75 \(\upmu\)s (Figure 9, top), (ii) the bottom surface of the water tank (echo #3 in Figure 5), at \(\sim\)87 \(\upmu\)s (Figure 9, top), and (iii) reverberations  
Figure 8: \(SNS\) vs. frequency for flat HS03 transducers in pulse-echo mode in water with different coatings.  
Figure 7: \(SNS\) vs. frequency for HS03 transducers in pulse-echo mode in water: flat and spherically focused (two focal distances: 22 and 35 mm).  
in those cavities can also be appreciated. For the focused transducers (focal distances of 22 and 35 mm) only the reflections from the front steel reflector surface (and reverberations in the transducer-reflector cavity) are observed as the other interfaces mentioned above are well out of focus. For the 22 mm radius of curvature transducer, the first reflection from the steel reflector front face (at -29 us) plus two reverberations in the cavity between the transducer and the reflector can be observed (at -57 us and -85 us).  
* Transducers with coating.  
The received echoes corresponding to the experimental set-up shown in Figure 5 for the HS03 flat transducers with different coatings are shown in Figure 10.  
Figure 10: The response in the time domain in pulse-echo mode in water for HS03 transducers with different coatings (varnish, PTFE, lacquer and PMMA).  
Figure 9: Response in the time domain in pulse-echo mode in water for the HS03 flat and focused transducers, radii of curvature of 22 and 35 mm.
## 3 Chemical Composition of _Hyssopus officinalis_ L. and _Agastache foeniculum_ (Pursh) Kuntze EOs
### Antimicrobial Activity of H. officinalis EO
#### 4.1.2 Antibacterial Activity  
[PERSON] et al., 2018 [7] demonstrated moderate activity of _H. officinalis_ EO against _Streptococcus pyogenes_, _Streptococcus pneumoniae_, and _Streptococcus mutans_, and weak activity against _Escherichia coli_, Klebsiella pneumoniae, Pseudomonas aeruginosa, _Proteus mirabilis_, _Staphylococcus aureus_, _Staphylococcus epidermidis_, _Bacillus subtilis_, and _Microococcus luteus_. [PERSON] et al., 2023 [47] demonstrated a slightly better activity against _E. coli_ and _S. aureus_. [PERSON] et al., 2009 [27] tested _H. officinalis_ EO against several _Lactobacillus_ strains (_Lactobacillus acidophilus_, _Lactobacillus casei_, _Lactobacillus bulgarcius_, _Lactobacillus sakei_, and _Lactobacillus rhamnosus_) and found no antibacterial activity, suggesting its suitability for food applications. Also, they showed slight antibacterial activity against _Bacillus cereus_, _E. coli_, _Enterococcus faecalis_, and _S. aureus_. [PERSON] et al., 2013 [14] showed moderate antibacterial activity of _H. officinalis_ EO against _E. coli_ and _S. aureus_ and weak antibacterial effect against Proteus vulgaris and S. aureus resistant strains. [PERSON] et al., 2011 [31] demonstrated that the oil showed considerable activity against _S. aureus_, _B. cereus_, and _Staphylococcus saprophyticus_. Gram-negative bacteria were less susceptible than Gram-positive bacteria. Additionally, the oil displayed bactericidal effects against the tested bacteria. [PERSON] et al., 2018, [34] demonstrated antibacterial activity against B. cereus, B. subtilis, _S. epidermidis_, _Acinetobacter baumanni_, _E. coli_, and most of the _S. aureus_ strains, standard strains, and clinical isolates. [PERSON] et al., 2016 [36] showed strong inhibitory and bactericidal effects against the strain of _P. aeruginosa_ from sputum, while the weakest effect was against the strains _S. pyogenes_ and _Klebsiella_ sp. from wound swabs. [PERSON] et al., 2016 [26] showed that the _H. officinalis_ EO is effective against _S. aureus_, _S. typhimurium_, _E. coli_, and _P. aeruginosa_. The data are comprehensively detailed in Tables 5 and 6.
## 3 Results of the Field Experiment
### Dynamic Behaviour  
Figure 9 illustrates the structural dynamic response. Figure 9a-c relay the various loading cases, indicating the structural dynamic response. Measurements were recorded from cases 1-3 with an average speed of 3-20 km/h practicable to prompt the dynamic structural effect, with 3 km/h considered the crawling speed and 20 km/h considered the average speed [19; 23; 30; 31]. The deformation response ranged from 0-0.031, 1.30-1.932, and 11.249-15.844 mm from the loading cases, respectively, as indicated in Figure 9d. The responses were random due to their dynamic nature. Still, it was observed that the structural characteristic was linear from individual responses concerning the loading, and unloading departure from the respective negative amplitude from Figure 9a-c. Each negative peak indicated the maximum deflection and deviation from it during unloading, corresponding to a linear elastic response.  
Figure 8: Load vs. strain relationship at (**a**) Downstream. (**b**) Up-stream. (**c**) Stress vs. strain relationship.  
The maximum deflection is supposedly more prominent than the static maximum response by approximately 1.4 mm. As a result, the structure would seem nonlinear compared to the static response of 14.43 mm [30]. From Equation (3), the DAF was derived as 1.09 with a structural span of 31.81 mm of a one-lane bridge [23]. The parameter is well within the given threshold by ASSHTO 1997, indicating that the deterioration is significantly low and within maintenance margins [31]. Since DAF is a modal parameter that can reflect the dynamic performance of the bridge, it has proved to be a good indicator of the pavement condition. Consequently, it lays the foundation for a maintenance schedule concerning a well-conditioned pavement's safe margins of 1.0-1.4 DAF: Structural failure constitutes numerous aspects, such as fatigue failure, surface wear, and corrosion, that manifest [19; 20; 22; 32] under dynamic loading [30].
## 6 Results
### Comparison with a Hybrid Optimization Approach
It is worth motioning that even if the DSCA-SOCP reach the same optimal solution of the proposed MI-SOCP approach, these methodologies are not comparable due to:  
* The DSCA-SOCP approach being a metaheuristic approach that requires a statistical test based on multiple evaluations to determine its capability of finding the global optimum. In this sense, after 100 consecutive evaluations, the DSCA-SOCP methodol \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Nodes** & **Active Power (MW)** & \(\mathbf{E_{\text{loss}}}\) **(kWh/day)** \\ \hline Base case & — & — & 2666.2860 \\ GAMS-BONMIN & \{27, 61, 64\} & \{0.4366, 1.6744, 0.3253\} & 2046.0656 \\ GAMS-COUENNE & \{12, 18, 61\} & \{0.4990, 0.3808, 1.9254\} & 2030.5272 \\ GAMS-DICOPT & \{11, 17, 64\} & \{0.6900, 0.4194, 1.6626\} & 2074.0086 \\ DSCA-SOCP & \{11, 18, 61\} & \{0.5655, 0.4197, 1.9079\} & 2028.1673 \\ MI-SOCP & \{11, 18, 61\} & \{0.5655, 0.4197, 1.9079\} & 2028.1673 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal placement and sizing of renewables considering daily curves in the IEEE 69-node test feeder. Figure 5: Behavior of the photovoltaic (PV) generation and the demand during a typically operation day [21]. ogy reach the solution reported in Table 4 only 5 times, i.e., an efficiency of 5%, while the proposed MI-SOCP finds the same solution at each running, due to the convex structure of the solution space that guarantees the global optimum finding without recurring to statistical tests; * Each evaluation of the DSCA-SOCP approach (i.e., one complete evaluation) takes about 16,747.35 s with the main problem being that it is not possible to ensure the finding of the optimal solution while the proposed MI-SOCP only takes 315.60 s to determine the global optimal solution. These processing times show that our proposal is about 53 times more efficient than the DSCA-SOCP approach.
# Differential Analysis of a Cryptographic Hashing Algorithm HBC-256
## 3 Materials and Methods of Research  
### HBC-256 Hash Function  
More details about the HBC-256 hashing algorithm described in [15]. The general scheme of message hashing \(M(M_{0},M_{1},\ldots,M_{t-1})\) at k = 3 is shown in Figure 1, where k is the number of parts, \(M_{r}\left(m^{0},m^{1},\ldots,m^{k-1}\right)\) is a message block M consisting of 128 \(\times\) k bits, \(r=0,1,\ldots,t-1\). The proposed algorithm HBC-256 takes \(M_{r}\) from the message M as a master key \(rk_{0}^{j}\), and the encrypted text-the previous intermediate hash value \(h_{i-1}^{j}\), \(j=0,\ldots,k-1\). At the very beginning of the process of hashing the message \(M(M_{0},M_{1},\ldots,M_{t-1})\) the initial hash value is taken as \(h_{0}^{j}=0^{128}\), i.e., null value. Based on message blocks \(M_{r}\), round keys \(rk_{i}^{j}\), \(i=1,2,\ldots,R_{1}\), are generated, where \(R\) is the number of rounds for hashing message block \(M_{r}\).  
The HBC-256 data hashing algorithm is based on the proposed CF block cipher (Figure 2). CF has two inputs-a 128-bit \(h_{i-1}^{j}\) and a 128-bit round key \(rk_{i}^{j}\) and one output-an intermediate 128-bit hash value \(h_{i}^{j}\). The nonlinear bijective transformation S is determined using the SBOX procedure. Four \"golden\" S-blocks were selected according to Table 1.  
Figure 1: The general hashing scheme of the HBC-256 algorithm.  
Based on the Wide-pipe scheme, in a single hash loop, the CF algorithm is simultaneously executed k times for different \(m^{j},\ j=0,\ldots,k-1..\). The length of the intermediate hash value is 128 k bits. The Davis-Meyer CF is computed by \(p^{j}\) as the result of summing \(h_{i-1}^{j}\) and \(h_{1}^{j}\) modulo 2. The PerF procedure then rearranges the values of all three \(p^{j}\), which are further taken as \(h_{i-1}^{j}\). The final hash value is determined through ComF procedures.
## 4 Experiments and Analysis of Results
### The Analysis of Uplink NOMA-VLC  
In the uplink NOMA-VLC system, the BER performance for different bias currents, attenuation values, and power allocations were analyzed. Figure 10 represents the BER performance of User 1 and User 2 with different bias current values. It can be seen that the BER performance of all users with Material-2 was below the forward error correction (FEC) limit criterion when the bias current was 0.6 A. As the bias current increased from 0.1 to 0.6 A, the BERs of all users with Material-1 and Material-2 showed decay. The BERs of all users with both materials increased when the bias current increased from 0.6 A to 1 A. Therefore, in our following experiment, the bias current value was set to 0.6 A to obtain the best BER performance.  
Figure 11 shows the BER performance of User 1 and User 2 for uplink NOMA-VLC with Material-1 and Material-2 considering different attenuation values. As the attenuation increased, the BERs of all users showcased a decay. We executed various experimental trials to achieve an optimized attenuation of 10 dB to find out the best conceivable BER  
Figure 10: BER performance of NOMA-VLC under different bias current values.  
Figure 9: P-I and V-I measurement results.  
performance. As we increased the attenuation above 10 dB, all the BERs indicated a rise in each associated curve. Next, we used the LS-SIC algorithm to further improve the BER performance. It is worth noting that the BER performance of both users considering both materials was below the FEC limit criterion while using the LS-SIC algorithm when the attenuation was 10 dB.  
In order to secure successful signal decoding, there must be a sufficient power difference between received signals. We used the optimized values of bias current and attenuation and the control power allocation ratio in the NOMA system to obtain multiplexing transmission. Figure 12 shows the BER performance for two users considering different power allocation ratios. For experimental demonstration, we considered power allocation ratios to be 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, and 0.97. The decreasing exponential curves showed that a higher power allocation ratio was suitable for better BER performance. We noticed that the best BER performance was obtained at the power allocation ratio of 0.96. Moreover, our findings indicated that the MISO-NOMA-VLC system could support an aggregate data rate of 40 Mbps within the FEC criterion in the uplink NOMA-VLC system.
# Ferry Scheduling Optimization Considering Arrival Time Uncertainty and In-Place Time Differences
###### Abstract  
The aircraft fery service is an important link in the transfer of travelers between the remote parking stand and the terminal. By analyzing the process of inbound and outbound ferry service, the relationship between the in-place time of each ferry on the same flight is clarified. A ferry service splitting method considering the difference in in-place time is proposed. Based on this, a dynamic programming scheduling model for ferries is innovatively developed. Considering the impact of flight arrival and departure uncertainty on the in-place time of ferry service, the model is transformed into a dynamic programming stochastic model with opportunity constraints. The optimization objective of this model is to minimize the number of ferries used. To describe the model more easily, a sample average approximation technique is introduced to transform the stochastic model into a deterministic model with confidence. A genetic algorithm based on Monte Carlo random sampling is designed to solve the model. The experimental results based on the actual operation data of an airport in South China show that the dynamic splitting method considering the difference in the in-place time of the ferry can improve the resource utilization efficiency; the scheduling scheme based on the dynamic planning stochastic model has better robustness.  
air transportation; ferry scheduling; dynamic programming; stochastic model; genetic algorithm +
Footnote †: journal: Applied sciences
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 4 Materials and Methods
### Model Selection
#### 4.2.2 The Classifiers Used in Our Study  
In our study, we selected a diverse range of classifiers to ensure a balanced and thorough comparative analysis. These included widely adopted models such as logistic regression (LR) [1], support vector machine (SVM) [14], random forest (RF) [15], and K-nearest neighbors (KNN) [15]. Multilayer perceptron (MLP), naive Bayes (NB), and decision tree (DT) [48]. These models are typically used in the context of churn, and they obtained very good prediction results in previous research on churn prediction. The inclusion of LR, SVM, RF, and KNN was particularly motivated by their suitability for high-dimensional datasets. This combination of classifiers allowed us to evaluate both their performance in our context and their ability to capture patterns in a churn dataset. Additionally, we included zero-shot classification to explore whether this more recent approach, which classifies text without requiring extensive training data, could contribute effectively in the context of churn prediction.
## 3 Spaceborne GNSS-R Raw IF Data and Processing
### Raw IF Data Processing
#### 3.2.1 Direct Signal Processing  
The direct signal is processed following a generic GNSS signal processing scheme, from which the code and carrier parameters are estimated through a combination of a delay locked loop (DLL) and a phase locked loop (PLL). These parameters are then fitted to 3 rd degree polynomial functions to get the time series of the code offset \(\Phi^{\rm d}_{\rm code}(t)\), the carrier frequency \(f^{\rm d}_{\rm carrier}(t)\), and the carrier phase \(\phi^{\rm d}_{\rm carrier}(t)\). In addition, the navigation bits or the secondary code \(D_{*}(t)\) are also decoded from the outputs of the tracking loops. By further processing of these navigation bits and code offset measurements from different GNSS satellites, the position, velocity and timing (PVT) information of the receiver can be also estimated, which can be used to calibrate and validate the PVT information provided in the meta data. With the direct signal parameters, the complex waveform of the direct signal \(z^{\rm d}(t_{0},\tau_{i})\) are also generated at the same epoch as the reflected signal (\(t_{0}\)).
# A Study on Crowd Evacuation Model Considering Squeezing Equilibrium in Crowded Areas
## 3 Model Optimization Method
### Proof of Optimization Formula  
There is a certain relationship between \(h1\), \(h2\) and \(h3\), which will be demonstrated in detail below. According to the reference [43], the adaptation time \(\tau\) is 0.5 s, the social force intensity between pedestrians \(A_{a}\) is 1.5 KN, the coefficient of action range \(B_{a}\) is 0.8 m, the coefficient \(K\) of human body flexibility is 120,000, the coefficient \(k\) of human body friction is 600,000, the personnel mass \(m\) is 80 kg, and the personnel radius \(r\) is 0.4 m.  
\[A_{a}e^{\frac{R_{ab}-d_{ab}(t)}{R_{a}}}h1n_{ab}(t)=1.5e^{\frac{0.8-d_{ab}(t)}{ 0.8}}h1 \tag{8}\]  
\[A_{a}e^{\frac{R_{ab}-d_{ab}(t)}{R_{a}}}h2n_{ab}(t)+Kg[R_{ab}-d_{ab}(t)]n_{ab}( t)+kg[R_{ab}-d_{ab}]\Delta v_{ba}(t)t_{ab}(t) \tag{9}\]  
\[=1.5e^{\frac{0.8-d_{ab}(t)}{0.8}}h2+M\]  
\[M=Kg[R_{ab}-d_{ab}(t)]n_{ab}(t)+kg[R_{ab}-d_{ab}]\Delta v_{ba}(t)t_{ab}(t) \tag{10}\]  
where, \(M\) is the friction between pedestrians. \(d_{ab}(t)\) is [0, 0.15], in (m), so  
\[1.5e^{\frac{0.8-d_{ab}(t)}{0.8}}h1\leq 1.5e^{\frac{0.8-d_{ab}(t)}{0.8}}h2+M \tag{11}\]  
the critical value (tangent to the influence range) is taken,  
\[d1_{ab}(t)=0.8+0.15=0.95,\ d2_{ab}(t)=0.8,\ R_{ab}-d_{ab}(t)=0 \tag{12}\]  
The friction between pedestrians is 0, so \(M=0\) to get:  
\[1.5e^{\frac{-0.15}{0.8}}h1\leq 1.5e^{0}h2 \tag{13}\]  
So  
\[e^{-0.1875}h1\leq\text{h2} \tag{14}\]  
So, the relationship formula of the acting force between pedestrians is:  
\[h2\geq 0.829h1 \tag{15}\]  
Similarly:  
\[A_{a}e^{\frac{R_{at}-d_{aw}(t)}{R_{a}}}h2n_{aw}(t)=1.5e^{\frac{0.4-d_{aw}(t)}{ 0.8}}h2 \tag{16}\]  
\[A_{a}e^{\frac{R_{at}-d_{aw}(t)}{R_{a}}}h3n_{aw}(t)+Kg[R_{at}-d_{aw}(t)]n_{aw}( t)+kg[R_{at}-d_{aw}(t)]\Delta v_{wa}(t)t_{aw}(t) \tag{17}\]  
\[=1.5e^{\frac{0.4-d_{aw}(t)}{0.8}}h3+M\]  
\[M=Kg[R_{at}-d_{aw}(t)]n_{aw}(t)+kg[R_{at}-d_{aw}(t)]\Delta v_{wa}(t)t_{aw}(t) \tag{18}\]  
where, \(M\) is the friction between pedestrians and obstacles amd \(d_{aw}(t)\) is [0, 0.2], in (m), so  
\[1.5e^{\frac{0.4-d_{1 ap}(t)}{0.8}}h2\leq 1.5e^{\frac{0.4-d_{aw}(t)}{0.8}}h3+M \tag{19}\]  
the critical value (tangent to the influence range) is taken,  
\[d1_{aw}(t)=0.4+0.2=0.6,\ d2_{aw}(t)=0.4 \tag{20}\]  
When the tangent is critical, the friction between pedestrians and obstacles is 0, so \(M=0\) to get:  
\[e^{\frac{-0.02}{0.8}}h2\leq\ h3 \tag{21}\]So, the relational expression of the acting force between pedestrians and obstacles is:  
\[h3\geq 0.607h2 \tag{22}\]  
The relational expression between the two acting forces is further constructed,  
\[h3\geq e^{-0.5}h2\geq e^{-0.5}e^{-0.1875}h1\geq e^{-0.6875}h \tag{23}\]  
so  
\[h3\geq 0.503h1 \tag{24}\]  
To sum up, the relationship between the parameters \(h1\), \(h2\) and \(h3\) to be optimized is (relational expression of acting force)  
\[h2\geq 0.829h1,\ h3\geq 0.503h1 \tag{25}\]