esa-sceva/satcom-chunk-collection huggingface dataset:
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### Overview of Representative Volume Elements  
The smallest replicated unit or the representative volume element (RVE) alludes to the smallest unit that is replicated throughout the design domain or in a sub-region of a design domain; such RVEs are also referred to as unit cells. Below are the different existing types of RVEs or unit cells.
# Correlation Between Co-Design and Psychological Need Satisfaction Based on fNIRs Hyperscanning
###### Abstract  
With the increasing attention paid to user-oriented design and services, the term co-design, a form of user-participatory design, is increasingly mentioned. Previous studies have shown that co-design could better satisfy user needs in process and results, but there are few studies that have been empirically validated through neuroscience methods. Therefore, this study used fNIRS (functional near-infrared spectroscopy) to measure the brain blood oxygen data (HbO\({}_{2}\) & Hb) of both \"designer\" and \"user\" participants from design and non-design majors during traditional and co-design tasks. The IBS (inter-brain synchronization) between participants was calculated using a wavelet coherence package in Matlab. Research has found significant consistency of DLPFC (dorsolateral prefrontal cortex) and BROCA (pars triangularis Broca's area) was observed in both tasks, with more significance observed in co-design tasks, and IBS activation was significantly positively correlated with need satisfaction rate. The results showed that co-design could better activate DLPFC and BROCA brain regions compared to traditional design, which further supports previous research and expands the possibility of collaborative task paradigms. The increase in co-design IBS compared to traditional design also represents better collaborative performance, and could provide empirical evidence for participatory design based on neuroimaging.  
co-design; fNIRS; hyperscanning; IBS; wavelet coherence +
Footnote †: journal: Applied sciences
## 4 A Communication Scheme Based on Self-Organizing Patterns
Dots in four groups are respectively 15, 10, 5, 3 pixels away from each. Figure 8 illustrates difference images at different values of parameter \(\delta\). It can be seen that depending on the cell probability to be excited (parameter \(\delta\)) the required minimum distance from dots (in the secret image) varies. Better information visibility of the decoded image is ensured by higher values of \(\delta\). However, denser initially exited cells lead to less resting cells--and this results in a less number of iterations required for a fully developed pattern. On the other hand, a smaller number of iterations results into smaller changes in the initial image. The secret visual communication scheme based on SOP in the AF model can be illustrated in Figure 9 (\(\
u=0.2\), \(\tau=n=20\), \(\delta=0.08\)). Before the transmission takes place, the original secret image is transformed into a dot-skeleton equivalent. The system parameters \(\
u\), \(\tau\), \(\delta\), \(L_{x}\), \(L_{y}\) must be predetermined beforehand. Finally the pseudo-random number seed \(a_{0}\) and the number of iterations \(n\) is transmitted by using the secure communications channels. The original secret image is shown in Figure 9a. Its dot-skeleton equivalent is illustrated in Figure 9b. Using the parameter \(a_{0}\) (as well as the parameter \(\delta\)) the Sender generates the initially excited cells  
Figure 8: The initial dot-skeleton image (**a**) and SOP at different \(\delta\): the first row represents initial conditions; the second row—patterns; the third row—difference images. (**a**) \(\delta=0.001\) (**b**) \(\delta=0.01\) (**c**) \(\delta=0.1\) (**d**) \(\delta=0.2\). (Figure 9c). The initial states of cells are inverted accordingly to the dot-skeleton representation (Figure 9d). The resulting self-organizing pattern is shown in Figure 9f. This pattern can be placed on top of a standard cover image (Figure 9e) by using the least significant bit (LSB) steganographic technique [16]. This is a standard spatial domain steganographic technique which manipulates LSB of pixels to embed the secret information into the cover image. The final cover image ready to be sent over to the Receiver via an open communication channel is shown in Figure 9g. The Received obtains the cover image (Figure 9g) and reproduces the pattern (Figure 9f) by using a clean copy of the cover image (Figure 9e). The remaining procedure for the Receiver is to simply re-generate the initial array of pseudo-randomly excited cells (as shown in Figure 9c) and to generate the pattern (the parameters of the AF model are privately known in advance)--the alternative pattern is illustrated in Figure 9h. Finally, the difference image between the two (Figure 9f,h) reveals the secret message as shown in Figure 9i.
## 6 Vision System for Therapeutic Adaptation: Definition of Integration Strategies
### Integration Testing and Verification  
The integration testing and verification phase aimed to ensure the correct functioning of the overall system, ensuring software and hardware compatibility and efficiency. The main objectives of the tests included the verification of computational performance, to ensure that the system can process large volumes of data without delays, perform synchronization between modules, avoid latencies in the data flow and resistance to failures, and test the system's ability to autonomously recover from simulated errors.  
Before testing the complete system, each module was individually validated through unit tests that allowed one to evaluate each module's functionality, robustness, and interoperability with the rest of the system. The tests on the individual modules included the accuracy of motion detection, the adaptability to lighting variations, the accuracy of biomechanical parameter extraction, and the reliability of the generated recommendations. The system was also tested for its ability to adapt to the simulated progress of the patient, the safety of the recommendations to avoid dangerous conditions, the precision of motor assistance, and the system response in the case of failures.  
After the individual modules were validated, integration tests were performed to verify that the modules interacted correctly with each other. These tests included the validation of communication between modules, data synchronization, motor response verification, and latency testing to ensure that the biomechanical parameters processed by the vision module were received without delay by the recommendation module. To optimize data transmission, tools such as ZeroMQ and MQTT were considered, as well as multithreading and parallel computing techniques to improve efficiency.  
The system was also subjected to stress tests to simulate adverse operating conditions, such as high request loads, and scalability tests to assess the ability to add new modules without compromising the stability of the system. Hardware and software failures were also simulated to verify the system's resilience and its ability to autonomously recover from failures.  
Finally, continuous monitoring tools, such as advanced logging and predictive maintenance based on machine learning algorithms, were implemented to detect anomalies and prevent future failures.
## 4 Experimental Results
Furthermore, the proposed method achieves a higher overall error rate of 14.3% and a lower relative distance error of 1.52 than the other compared methods. The evaluation demonstrates that the proposed method generally outperforms the traditional methods. The performance of the proposed method on each type of WBC was evaluated. Table 2 shows the segmentation accuracy for each type of WBC. The performance of five kinds of segmentation methods was also compared. For each kind of method, a typical algorithm mentioned in the Introduction was evaluated as a representation. [PERSON]'s work [6] was adopted for the threshold-based method; [PERSON]'s work [9] was adopted for the learning-based method; [PERSON]'s work was adopted [7] for the active-contour-based method; [PERSON]'s work [13] was adopted for the metaheuristic-based method; [PERSON]'s work [14] was adopted for the saliency-based method. All of these methods were compared with the proposed method in Table 3, and the results show the proposed method achieved higher segmentation accuracy than those other methods. In addition, owing to the color variety of blood smear images some segmentation methods that are sensitive to color space may fail to correctly segment WBCs. Most of the WBC segmentation methods focus mainly on the segmentation part, but few of them pay attention to color adjustment. However, the blood smear images captured in the clinic do not always have good color consistency. Thus, color adjustment is a necessary step in practical application. A color adjustment method based on k-means \begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Region Growing** & **Snake** & **Watershed** & **Proposed Method** \\ \hline OR & 0.093 & 0.084 & 0.066 & 0.063 \\ UR & 0.059 & 0.081 & 0.068 & 0.067 \\ ER & 0.232 & 0.195 & 0.186 & 0.143 \\ RDE & 9.34 & 6.46 & 4.54 & 1.52 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation via average error measure of OR, UR, ER and RDE. clustering and color transfer was adopted in this paper. The color adjustment was treated equally as the segmentation part. Experiments show that the color adjustment can improve the accuracy of WBC segmentation. Table 4 shows the difference between the proposed method with color adjustment and without color adjustment.
# Respiratory Muscle Strength as a Predictor of VO2 max and Aerobic Endurance in Competitive Athletes
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline
**Characteristics** & **X** & **SD** & **B** & **SE** & **B** & **T** & \(p\) & **Zero-Order** & **Partial** \\ \hline (Constant) & & & 123.729 & 40.390 & & 3.063 & 0.005 & & \\ MIP (cm/H\({}_{2}\)O) & 99.08 & 29.70 & \(-\)0.111 & 0.151 & \(-\)0.124 & \(-\)0.737 & 0.467 & 0.364 & \(-\)0.133 \\ MEP (cm/H\({}_{2}\)O) & 130.95 & 42.82 & 0.338 & 0.111 & 0.543 & 3.061 & **0.005** & 0.565 & 0.488 \\ MAC (cm) & 91.40 & 10.720 & \(-\)4.127 & 3.790 & \(-\)1.658 & \(-\)1.089 & 0.285 & 0.510 & \(-\)0.195 \\ ACI (cm) & 95.20 & 10.216 & \(-\)0.233 & 2.152 & \(-\)0.089 & \(-\)0.108 & 0.915 & 0.492 & \(-\)0.020 \\ ACE (cm) & 89.13 & 10.827 & 3.552 & 3.031 & 1.441 & 1.172 & 0.251 & 0.515 & 0.209 \\ MSC (cm) & 78.82 & 10.454 & 2.796 & 2.475 & 1.095 & 1.130 & 0.267 & 0.509 & 0.202 \\ SCI (cm) & 82.95 & 9.462 & \(-\)3.838 & 1.571 & \(-\)1.361 & \(-\)2.443 & **0.021** & 0.419 & \(-\)0.407 \\ SCE (cm) & 76.63 & 10.505 & 1.849 & 2.168 & 0.728 & 0.853 & 0.400 & 0.524 & 0.154 \\ MVV & 135.63 & 38.39 & 0.124 & 0.115 & 0.178 & 1.073 & 0.292 & 0.534 & 0.192 \\ \hline \multicolumn{10}{l}{**F\({}_{(9-30)}\) = 4.552**} & \multicolumn{6}{c}{P = 0.001} \\
**R = 0.760** & \multicolumn{6}{c}{R\({}^{2}\) = 0.577} \\ \hline \multicolumn{10}{l}{MIP: Maximum inspiratory pressure, MEP: Maximum expiratory pressure, MVV: Maximum voluntary ventilation, MAC: Mid-Avilla Circumference, ACI: Axilla Circumference On Inspiration, ACE: Axilla Circumference On Expiration, MSC: Mid-Subcostal Circumference, SCI: Subcostal Circumference On Inspiration, SCE: Subcostal Circumference On Expiration; X: Mean, SD: Standard Deviation, SE: Standard Error, B: Regression Coefficient, 6: Standardized Regression Coefficient, T: t-Statistic, \(p\) : _p_-Value, Zero-order: Bivariate Correlation Values, Partial: Partial Correlation Values.  
\end{table} 
Table 3: Regression analysis results regarding VE (Mean \(\pm\) S.D. = 134.80 \(\pm\) 26.69 mL) prediction of the parameters. Figure 1: Regression analysis results predicting VO2 max/kg from various parameters. Table 4 demonstrates that the parameters have a strong and significant relationship with VO\({}_{2}\) (R = 0.814, R\({}^{2}\) = 0.663, \(p\) < 0.05). The mentioned variables together explain 66% of the total variance in VO2 (Figure 3).According to the standardized regression coefficient (\(\beta\)), the values of MEP and subcostal circumference on inspiration have a significant effect on VO\({}_{2}\), while the other predictor variables do not seem to have a significant effect on VO\({}_{2}\). When the bilateral and partial correlations between predictor variables and VO\({}_{2}\) were examined, low-level correlations were found between MIP and VO\({}_{2}\), and moderate and positive correlations with other parameters.
## 4 Data Collection and Result Discussion
### Conclusions and Future Work  
After the absolute antenna phase center calibration algorithms were investigated, an absolute antenna phase center calibration procedure has been implemented and followed by a calibration platform based on a robot. Making use of the robot's rotation and tilting, a homogenous observations distribution with regard to the antenna hemisphere are achievable, which is critical for high precision antenna phase center parameter estimation. The calibration results of GPS antennas presented in this paper show good repeatability for calibrations with different antenna types on different days. Our preliminary calibration results validate the absolute calibration methods of the robot and IGS type mean values by agreement of the estimated parameters at the 1 mm level for the most part of elevations above 15\({}^{\circ}\), if an identical antenna is used.  
Future work includes several refinements that must be made to enable calibration of all GNSS antennas. First, the calibration for BeiDou Navigation Satellite System (BDS) should be feasible in the near future. With the rapid development of BDS, the BDS antenna PCC model is a prerequisite for high accuracy BDS applications, such as, precise point positioning (PPP) and interoperability with GPS and GLONASS and other GNSS systems. Second, our calibration strategies for low elevation need to be further improved as to maintain a consistent precision level with IGS type mean values.
## 3 The FQS Algorithm  
Faster quick search (FQS) is an improved version of the quick search algorithm. QS calculates a shift table (array) using Equation (2). In addition to the same shift table in the QS algorithm, FQS calculates two more elements: one is the maximal expected shift position (called \(pos\)); the other is a new shift table for the prefix \(P[0,...,pos-1]\) using the QS algorithm. The expected shift (ES) is the sum of shifts when a mismatch occurs in the pattern current position. In our algorithm, the shift is calculated by the bad character rule, which shifts to right when matching the symbol of the text. In the uniform distribution of symbols, the maximal expected shift position is the left most position of the pattern that has the maximal expected shift value in all positions of the pattern. When the mismatching occurs in this position, it will have the largest shift value in the average case. Equation (3) calculates the expected shift distance for each position in pattern \(P\). The maximal expected position, \(pos\), is calculated in pattern \(P\) by using Equation (4). Finally, the algorithm identifies a maximal location, \(pos\), which has the maximal expected shift position.  
Before we introduce Equation (3), we first need to consider the array, \(prepos_{j}(c)\). Given the current position, \(j\), in pattern \(P\) and a symbol, \(c\in\Sigma\), \(prepos_{j}(c)\) records the most recent occurrence position of symbol \(c\). For example, given a pattern \(P\) = \"GCAG\", let us examine the \(prepos_{j}(c)\) array. First, the size of array \(prepos_{j}(c)\) is the same as the alphabet size. Array \(prepos_{j}(c)\) is calculated by scanning pattern \(P\) from left to right. The initial value of \(prepos_{j}(c)\) is set to \"\(-1\)\". After scanning \(j=0\), \(prepos_{0}(G)\) is changed to zero, and all of the other symbols of \(prepos_{j}(c)\) are still \"\(-1\)\" (the initial value). After scanning \(j=1\), \(prepos_{1}(C)\) is updated to one, because the second character is C; all of the other corresponding \(prepos_{1}(c)\) remain the same as \(prepos_{0}(c)\). After \(j=2\), \(prepos_{2}(A)\) is updated to two; the other elements are unchanged. After \(j=3\), \(prepos_{3}(G)\) is updated to three, the other elements remain unchanged: \(prepos_{3}(A)=prepos_{2}(A)=2,prepos_{3}(C)=prepos_{1}(C)=1\) and \(prepos_{3}(T)=-1\).  
Now, consider Equation (3). \(ES_{j}\) is the sum of the shifts in the current position, \(j\), of pattern \(P\) if the bad character rule is applied. For each position, \(j\) in \(P\), where \(m-1\geq j\geq 0\), \(ES_{j}\) is calculated by using Equation (3), which indicates the sum of shift values for each symbol, \(c\in\Sigma\).  
\[ES_{j}=\sum_{c}(j-prepos_{j}(c)),0\leq j\leq m-1,c\in\Sigma \tag{3}\]  
The maximal expected shift position (\(pos\)) for pattern \(P\) is computed using Equation (4). \(pos\) is defined as the first position in pattern \(P\) where the maximal \(ES_{j}\) occur.  
\[pos=\min(k|ES_{k}=\max(ES_{j}),0\leq j\leq m-1) \tag{4}\]
###### Abstract  
This paper proposes a new sliding surface for controlling a Semi-Bridgeless Boost Converter (SBBC) which simultaneously performs Power Factor Correction (PFC) and DC bus regulation. The proposed sliding surface is composed of three terms: First, a normalized DC voltage error term controls the DC bus and rejects DC voltage disturbances. In this case, the normalization was performed for increasing system robustness during start-up and large disturbances. Second, an AC current error term implements a PFC scheme and guarantees fast current stabilization during disturbances. Third, an integral of the AC current error term increases stability of the overall system. In addition, an Adaptive Hysteresis Band (AHB) is implemented for keeping the switching frequency constant and reducing the distortion in zero crossings. Previous papers usually include the first and/or the second terms of the proposed sliding surface, and none consider the AHB. To be best of the author's knowledge, the proposed Sliding Mode Control (SMC) is the first control strategy for SBCs that does not require a cascade PI or a hybrid PI-Sliding Mode Control (PI-SMC) for simultaneously controlling AC voltage and DC current, which gives the best dynamic behavior removing DC overvoltages and responding fast to DC voltage changes or DC load current perturbations. Several simulations were carried out to compare the performance of the proposed surface with a cascade PI control, a hybrid PI-SMC and the proposed SMC. Furthermore, a stability analysis of the proposed surface in start-up and under large perturbations was performed. Experimental results for PI-SMC and SMC implemented in a SBBC prototype are also presented.  
sliding surface; sliding mode control; semi-bridgeless boost converter; adaptive hysteresis band; power factor correction; non-linear control +
Footnote †: journal: Applied Sciences
# Plant Growth Regulators Improve the Production of Volatile Organic Compounds in Two Rose Varieties
## Results and Discussion
### Analysis of VOCs after BA and NAA Application
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**RT.**} & \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**RI**} & \multicolumn{3}{c}{**Mean of Peaks at BA (mg/L)**} & \multicolumn{3}{c}{**Mean of Peaks at NAA (mg/L)**} \\ \cline{3-8}  & & & **0** & **100** & **200** & **0** & **100** & **200** \\ \hline
10.11 & Prenyl acetate * & 932 & 2.28 & 2.09 & 2.69 & 1.98 & 12.95 & 17.01 * \\
12.77 & Myrcene & 979 & 12.25 & 16.60 & 13.20 & 14.12 & 17.48 & 30.44 * \\
19.19 & Cuminal & 1214 & 1.24 & 1.88 & 4.38 * & 1.52 & 1.84 & 1.04 \\
21.29 & Geranial & 1249 & 37.57 & 51.06 & 70.71 * & ND & ND & ND \\
22.03 & Methyl geranate & 1299 & 2.26 & 3.25 & 5.21 * & 4.46 & 2.23 & 6.86 \\
24.19 & Geranyl acetate & 1360 & 2.58 & 10.84 & 11.09 * & 9.01 & 12.42 & 15.01 \\
25.44 & Caryophyllene & 1428 & 2.26 & 3.25 & 5.21 & 4.04 & 6.18 & 7.39 * \\
25.61 & \(\alpha\),\(\beta\)-Dihydro-\(\beta\)-ionone & 1437 & 45.47 & 49.27 & 47.49 & 42.86 & 69.98 & 71.17 * \\
25.62 & Dihydro-\(\beta\)-ionone & 1439 & 44.82 & 56.10 & 76.17 * & 25.22 & 24.75 & 38.47 \\
25.88 & cis-Muurola-(41),5-diene * & 145 & 7.61 & 10.83 & 33.45 * & 5.07 & 10.43 & 13.17 \\
30.90 & trans-3-Cuddeeene & 1695 & 1.07 & 1.26 & 2.20 * & 4.28 & 6.99 & 7.04 \\
34.27 & Z-5-Nonadecene & 1818 & 0.87 & 1.03 & 1.16 * & 13.07 & 13.17 & 19.09 \\
35.15 & 9-Nonadecene & 1880 & 16.39 & 15.82 & 15.93 & 13.57 & 39.87 & 43.62 * \\
35.23 & \(\gamma\)-Cadinene * & 1905 & 0.13 & 0.16 & 4.13 * & 1.27 & 0.79 & 2.06 \\ \hline \hline \end{tabular}  
* (*) represents significant differences between different concentrations of BA and NAA (0 (control), 100 and 200 mg/L); RT—retention time; RI—retention index based on alkane series; ND—not detected; \({}^{\dagger}\)—compounds that increased significantly with LSD \(p\leq 0.05\) in Hybrid Tea rose with increasing concentrations of BA and NAA compared to the control.  
\end{table} 
Table 2: Identification of VOCs that differed significantly (\(p\leq 0.05\)) between flowers of Hybrid Tea after being treated with different concentrations of BA and NAA. Figure 3: Volatile organic compounds collected from Floribunda flowers after the application of BA (**A**) and NAA (**B**), the red dots represent the volatile compounds which were significantly (\(p<0.05\)) different to the total collected volatile compounds. PCA analyses were used with one-way ANOVA.
## 4 Simulation Results and Complexity Analysis
### Computational Complexity Analysis
#### 4.2.1 Complexity Formula Analysis
For the second stage, executing the Chebyshev acceleration algorithm, including \(\left[\frac{2y^{(k)}}{\mu^{(k-1)}\cdot\mu}(\mathbf{G}_{HOSR}\bar{\mathbf{x}}^ {(k-1)}+\mathbf{d}_{HISOR})\right]\), \(\left[-\frac{\mu^{(k)}}{\mu^{(k-2)}\bar{\mathbf{x}}^{(k-2)}}\right]\), and their addition operation, requires \(\left[2N_{T}^{2}+N_{T}\right]\), \(N_{T}\), and \(N_{T}\) CMAs, respectively. Because the second stage is an iterative algorithm, the above computational complexity is added and multiplied by the number of iterations \(i\), expressed as \(\left[i(2N_{T}^{2}+3N_{T})\right]\). Therefore, our proposed CHSOR method requires a total of \(\left[11N_{T}^{2}-N_{T}+i(2N_{T}^{2}+3N_{T})\right]\) CMAs. The complexity analysis for the other methods is derived similarly and is not elaborated here. For greater clarity, the algebraic expressions of the computational complexity for different schemes are tabulated in Table 10. It is worth mentioning that it can be seen from the algebraic formulas in Table 10 that the complexity is independent of the number of receiving antennas. Of course, this is an important attraction for the B5G uplink system, which urgently needs M-MIMO. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Detection Methods** & **Complex Multiplications and Additions (CMAs)** \\ \hline \hline SOR & \(\frac{5}{2}N_{T}^{2}+\frac{1}{2}N_{T}+i(2N_{T}^{2}+N_{T})\) \\ \hline MSOR & \(\frac{5}{2}N_{T}^{2}-\frac{3}{2}N_{T}+i(2N_{T}^{2}+N_{T})\) \\ \hline AOR & \(3N_{T}^{2}+i(3N_{T}^{2})\) \\ \hline SOR & \(5N_{T}^{2}+N_{T}+i(4N_{T}^{2}+2N_{T})\) \\ \hline SAOR & \(6N_{T}^{2}+i(6N_{T}^{2})\) \\ \hline CSOR & \(\frac{5}{2}N_{T}^{2}+\frac{1}{2}N_{T}+i(2N_{T}^{2}+3N_{T})\) \\ \hline Chebyshev-MAOR & \(\frac{17}{2}N_{T}^{2}-\frac{3}{2}N_{T}+i(2N_{T}^{2}+3N_{T})\) \\ \hline Chebyshev-RI & \(3N_{T}^{2}-2N_{T}+i(2N_{T}^{2}+3N_{T})\) \\ \hline CHSOR & \(11N_{T}^{2}-N_{T}+i(2N_{T}^{2}+3N_{T})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Algebraic expressions of computational complexity for different detectors.