esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In some experiments, the data obtained did not meet the normality assumptions, such as having a heavy tail distribution. This condition can cause unreliable or even misleading results for practitioners in the field of quality improvement [1]. However, some studies have proven that the multivariate t-distribution can handle such data [3; 4; 5; 7; 20]. Therefore, the multivariate t-distribution approach is to be developed for Response Surface Methodology (RSM), especially for the modeling stage.  
The Second-Order Multiresponse Surface (SOMRS) model with multivariate t-distribution approach developed in this research differs from other development carried out in several previous studies [4; 6; 7; 8]. The differences are in the type of input variable (a setting of experimental conditions) and the order model (second-order model).  
The proposed SOMRS model with a multivariate t-distribution approach has been developed along with its parameter estimation and hypothesis testing. The solution of parameter estimation using the Maximum Likelihood Estimation (MLE) is not closed-form such that it is optimized numerically using the Expectation-Maximization (EM) algorithm. Afterward, the Likelihood Ratio Test (LRT) is employed for testing simultaneously the parameters model.  
The use of the MLE method to get the parameter estimation and LRT parameter estimation for testing the parameter hypothesis of the model obtained in this study is the same as the research conducted for the multivariate t-distribution approach [6], and the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Quality Characteristics** & **Mean** & **Std. Dev.** & **Min.** & \(\mathbf{Q_{1}}\) & **Med** & \(\mathbf{Q_{3}}\) & **Maks.** & **Skewness** & **Kurtosis** \\ \hline \(\mathrm{Table^{\prime}sFriability}(Y_{1})\) & 0.52 & 0.21 & 0.12 & 0.41 & 0.51 & 0.65 & 0.93 & 0.18 & \(-\)0.04 \\ \hline \(\mathrm{Table^{\prime}sHardness}(Y_{2})\) & 90.33 & 19.01 & 60.00 & 79.00 & 86.08 & 101.83 & 134.17 & 0.52 & 0.06 \\ \hline \(\mathrm{Table^{\prime}sDisintegrationTime}(Y_{3})\) & 242.98 & 62.01 & 147.00 & 197.62 & 244.49 & 279.79 & 367.50 & 0.39 & \(-\)0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive Statistics of Paracetamol Tablets Quality Characteristics.  
multivariate Gamma distribution approach [8]. Therefore, the results obtained also do not differ from these two studies.  
This paper has applied this approach to determine the SOMRS model and optimal conditions of the physical quality characteristics (friability, hardness, and disintegration time) of Paracetamol tablets. All of these quality characteristics are in accordance with the desired specifications [19].  
The model's goodness of fit is based on the R-square values; further research can use other methods to be more convincing. At the optimization stage, the Dual Response Systems (DRS) technique is used to set conditions to achieve the desired quality characteristics. Other optimization methods can also be used for further research.
###### Abstract  
The applications of Nd-Fe-B-based magnets are experiencing significant diversification to achieve efficiency and miniaturization in different technologies. Metal injection molding (MIM) provides new opportunities to manufacture Nd-Fe-B magnets with high geometrical complexity efficiently. In this study, the impacts of the binder system composition and powder loading on the rheological behavior, contamination, and magnetic properties of the Nd-Fe-B MIM parts were investigated. A high-pressure capillary rheometer was used to measure the apparent viscosity and pressure drops for feedstocks with different binder formulations and powder contents. Also, oxygen and carbon contamination, density, and magnetic properties were measured for different feedstock formulations and powder loadings. From the rheological, density, and magnetic properties points of view, the binder system consisting of 45 vol.% LLDPE as backbone was selected as the optimum formulation. The findings indicated that the sample with this binder system and 55 vol.% powder content had a high density (6.83 g/cm\({}^{3}\)), remanence (0.591 T), and coercivity (74.6 kA/m) compared to other binder compositions. By using 58 vol.% powder loading, the values of density (7.54 g/cm\({}^{3}\)), remanence (0.618 T), and carbon residue (982 ppm) improved, and a suitable rheological behavior was still observed. Thus, a suitable feedstock formulation was developed.  
metal injection molding; binder system; magnetic properties; rheological behavior; carbon residue +
Footnote †: journal:
## 2 Experiments and Methods
### Field Tests in Actual Engineering Projects  
The Taojia River Drainage Hub and Water Supply Project in Yuyao City is located on the west side of the Taojia River estuary in Simen Town, Yuyao City, Ningbo, Zhejiang. This pump station is situated in a high-chloride environment along China's East Sea coast. The service environment of the pump station is determined by its geographical location and the stray currents generated by power transmission lines of the plant and surrounding sea walls. Using this as the experimental background, the study investigates the optimal dosage of a _Xanthinium sibiricum_ corrosion inhibitor in practical engineering and predicts the remaining compressive strength of reinforced concrete after adding this inhibitor.  
The experimental plan involved casting 64 concrete specimens, each measuring 150 mm \(\times\) 150 mm \(\times\) 150 mm, with embedded reinforcing steel, and 60 specimens were divided into 4 groups of 15 each. During the casting process, the _Xanthinium sibiricum_ corrosion inhibitor was added to the four groups at concentrations of 0 g/L, 1 g/L, 2 g/L, and 3 g/L, respectively, forming four sample sets with different inhibitor concentrations. The proportion of inhibitor added was determined by the ratio of _Xanthinium sibiricum_ mass to the volume of concrete pore liquid. After casting, all specimens underwent standard curing for 28 days. Following curing, all 60 specimens were placed in seawater under conditions identical to the Taojia River pump station's service environment, simulating the performance changes in concrete during pump station operation. Over the next five months, on the same day each month, three specimens from each of the four concentration groups were removed for uniaxial compression testing [22; 23; 24]. The test was repeated three times for each group, and the average value was used to represent the compressive strength of each concrete specimen under different dosages of rust inhibitor after varying periods of corrosion.  
After allocating the above 4 groups of specimens, the remaining 4 specimens were mixed with the _Xanthinium sibiricum_ corrosion inhibitor at concentrations of 0, 1 g/L, 2 g/L, and 3 g/L, respectively. Following 28 days of standard curing, these 4 specimens underwent uniaxial compression testing. The purpose of this was to obtain the compressive strength of reinforced concrete specimens that had just reached maturity without experiencing corrosion.
## 4 The Proposed Model
### Local Weighted Smoothing and Representation Learning
#### 4.2.2 Weighted Embedding Aggregation  
The smoothed embedding of entity \(h\) under virtual relation group \(k\) is defined as a similarity-weighted average over its neighbors:  
\[\mathbf{e}_{h,k}^{(1)}=\sum_{t\in\mathcal{N}_{k}(h)}\gamma_{ht}\cdot\mathbf{e }_{t}^{(0)}, \tag{15}\]  
where the normalized attention weight \(\gamma_{ht}\) is computed as  
\[\gamma_{ht}=\frac{\exp(\omega_{ht})}{\sum_{t^{\prime}\in\mathcal{N}_{k}(h)} \exp(\omega_{ht^{\prime}})}. \tag{16}\]  
Then, we combine the original embedding with the smoothed result through a residual connection:  
\[\mathbf{u}_{h,k}^{(1)}=\mathbf{e}_{h}^{(0)}+\lambda\cdot\mathbf{e}_{h,k}^{(1)}, \tag{17}\]  
where \(\lambda\) is a smoothing coefficient that controls the influence of neighbors.
## 4 Optical CAM Technology
### Experimental Demonstration of an Optical CAM Cell
For the first logic state, when the FF holds a logic value of \"0\", CW1 emerges at FFOut1 with a low power level and enters the  
Figure 5: **(a)** Proposed architecture of an all-optical CAM cell; **(b)** Experimental setup for the demonstration of the Comparison operation and Write operation. SOA-MZI XOR gate, while Figure 6b shows the time trace of the search bit signal acting as the second control signal of the XOR operation. Figure 6c shows the result of the XOR comparison, where it can be seen that a logic \"_1_\" pulse is only obtained when a \"logic 1\" pulse is present at the search bit, featuring the same data pattern and confirming an XOR operation with the FF state of logic \"_0_\". Equivalently, when the FF holds a logic value of \"_1_\", as shown in the second column of Figure 6a, and is compared with the input search bit trace, the obtained CAM cell output features an inverse logic bit pattern compared to the input search bit, as shown in Figure 6c, verifying the XOR proof-of-principle. The eye diagrams of the obtained XOR output signals of the CAM cell are shown in Figure 6d, featuring an average ER of 9.2 dB. The results for the CAM cell Write mode functionality are presented in Figure 6e-h. Figure 6e,f show the _Set_ and _Reset_ signals and their corresponding eye diagrams, that were sent to the CAM cell in order to control the state of the FF. Figure 6g,h show the two stored bit values of the FF as a recorder at its two outputs, where it can be seen that the FF changes state upon the arrival of a Set (or Reset) pulse with a recovery time of 80 ps and the memory is then maintained until the arrival of the next opposite pulse. The two FF output signals clearly feature open eye diagrams, revealing an average ER of 6.5 dB. The two operations of the CAM cell were also evaluated with the aid of BER measurements and the results are shown in Figure 6i,j for the content comparison and the write functionality, respectively. An error free operation was achieved at the \(10^{-9}\) operating condition for both states of the FF and for both CAM cell functionalities. The BER diagram in Figure 6i reveals a successful content comparison at 10 Gb/s with a power penalty of 1 dB, associated mainly with the signal degradation induced by the optical XOR gate, while the BER diagram of Figure 6j for the Write functionality exhibits a power penalty of 4 dB, owing to the dynamic operation of the FF. For the CAM cell experimental demonstration, the four SOAs were again operated at a current of 250 mA each, leading to an energy efficiency of 200 pJ/bit at a 10 Gb/s operational speed.
# Mathematical Modelling of the Effects of Plasma Treatment on the Diffusivity of Biofilm
###### Abstract  
Biofilm formation on implanted medical devices is the reason for most of the nosocomial infections in clinical settings. Biofilms are more resistant to antimicrobials than their planktonic cells mainly because of the presence of the matrix of extracellular polymeric substances (EPSs), which acts as a physical barrier that limits the transport of antimicrobials inside the biofilm. A combinatorial antimicrobial approach of a non-thermal plasma and chlorhexidine (CHX) digluconate can be used to sterilize those surfaces contaminated with biofilm. However, the reason behind achieving this combinatorial decontamination is not known. Thus, in this study, we developed a mathematical model to explain the reason behind sterilization with the combinatorial treatment approach. It was found that the application of plasma prior to treatment with CHX is disrupting the biofilm and making it very porous. This is allowing CHX to penetrate deeper inside the porous biofilm, which is then effective at sterilizing the biofilm.  
biofilm; Pseudomonas aeruginosa; non-thermal plasma; CHX; diffusion; mathematical modelling +
Footnote †: journal: Applied Sciences
## 6 Numerical Example
### Drive on \(\text{Rd}_{h}\)
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{1}{c}{**MPs**} & \multicolumn{1}{c}{**P\({}_{1}\)**} & \multicolumn{1}{c}{**P\({}_{2}\)**} & \multicolumn{1}{c}{**P\({}_{3}\)**} & \multicolumn{1}{c}{**P\({}_{4}\)**} \\ \hline Type of road & Rd\({}_{\text{h}}\) & Rd\({}_{\text{h}}\) & Rd\({}_{\text{h}}\) & Rd\({}_{\text{h}}\) & Rd\({}_{\text{h}}\) & Rd\({}_{\text{h}}\) \\ Maximum load/each trip & 2 & 2 & 2 & 2 & 2 \\ Times to complete the delivery of 3 & 3 & 3 & 3 & 3 & 3 \\ units of goods & 3.946462 & 3.739355 & 3.716989 & 4.150282 \\ Total transport lead time & 11.839386 & 11.21806 & 11.15097 & 12.450846 \\ \hline \hline \multicolumn{1}{c}{P\({}_{1}\): T\({}_{\text{h}}=0.5\) (L) = 0.5(1.414214+4.242641+2.236068) = 3.946462 (h)} & (13) \\ P\({}_{2}\): T\({}_{\text{h}}=0.5\) (L) = 0.5(1.414214+2.236068+2.828427+1) = 3.739355 (h)} & (14) \\ P\({}_{3}\): T\({}_{\text{h}}=0.5\) (L) = 0.5(2.828427+3.605551+1) = 3.716989 (h)} & (15) \\ P\({}_{4}\): T\({}_{\text{h}}=0.5\) (L) = 0.5(2.828427+1+2.236068+2.236068) = 4.150282 (h)} & (16) \\ Step 5. & P\({}_{4}\) is deleted because the total transport lead time is greater than 12 (h). & \multicolumn{1}{c}{Find the flow vector of (_d_;_W_,_T_)-MP\({}_{\angle A}\) (3,2,12)-MP\({}_{\angle 90}\) via Equations (17)-(20)} \\ Step 6. & \multicolumn{1}{c}{listed below.} & \multicolumn{1}{c}{\(\sum_{j=1}^{3}f_{j}=3\)} & (17) \\ \cline{2-6} \cline{5-6} \cline{5-6}  
\[ \begin{array}{rl}\text{R}_{(3;2,12)-\text{MP}\_{\text{200}}}&=\sum\limits_{i=1}^{3} P(x_{i})-\sum\limits_{j=2}^{3}\sum\limits_{i=1}^{j-1}P(x_{i}\cap x_{j})+(-1)^{3+1}P(x_{i} \cap x_{j}\cap x_{k})\\ &=1.665198-1.411024+0.4210704\\ &=0.675244\end{array}  \tag{21}\]  
The reliability equals 67.5244% for the distribution of three units of goods from Taipei in northern Taiwan, i.e., the source node s, to Kaohsiung in the south, i.e., the sink node t, as shown in the MFN for driving on the highway (\(\text{Rd}_{\text{h}}\)) in Figure 6.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Enhancing Communication  
Effective communication is paramount in maritime operations, where misunderstandings can lead to significant safety risks. LLMs offer solutions to overcome language barriers and streamline information exchange.
## 3 Method
### Estimation of the HVR  
Removing the influence of nonresidential areas is the first step. The impact of roads and nonresidential areas needs to be removed based on the previously identified built-up areas before performing calculations because the main study object was residential areas. The method of extracting the built-up area is true and reliable, which has been verified by many studies [36]. The built-up area includes residential and non-residential areas.  
The average DN for the non-residential areas was calculated. The 100 points of nonresidential areas were identified through OSM and manual visual interpretation to calculate the average DN value of the nonresidential areas (Formula (4)). The true DN values for the residential areas of grids were calculated. We subtracted the DN value of the nonresidential areas from the original DN value of the study area to obtain the DN value of the residential area (Formula (5)). The calculation formulae are as follows:  
\[N_{f}=\frac{1}{n}\sum_{j=1}^{n}N_{j} \tag{4}\]  
\[N_{i}=L_{i}-N_{f} \tag{5}\]  
where \(N_{f}\) is the average DN value of the nonresidential areas; \(N_{j}\) is the DN value of \(j\) roads and nonresidential areas; \(n\) is the number of roads and nonresidential areas identified (\(n=100\)); \(N_{i}\) is the DN value of \(i\) residential areas; \(L_{i}\) is the original DN value of \(i\) built-up areas.  
This paper selected a grid with a large DN value as a fully occupied residential area in the filtered grid. The DN value of the fully occupied residential area was calculated as follows:  
\[N_{F}=\frac{1}{m}\sum_{k=1}^{m}N_{k} \tag{6}\]  
where \(N_{F}\) is the average DN value of the fully occupied residential area; \(N_{k}\) is the DN value of the \(k\) fully occupied residential area grid; \(m\) is the number of fully occupied residential area grids selected (\(m=10\)).  
Finally, the HVR in the main urban area of Xi'an city can be estimated based on the previously calculated results of the built-up area that removed the impact of roads and nonresidential areas. The average DN value when the houses were full, was calculated as follows:  
\[HVR_{i}=\begin{cases}1-\frac{N_{i}}{N_{F}}\times 100\%&N_{i}<N_{F}\\ 0&N_{i}\geq N_{F}\end{cases} \tag{7}\]  
where \(HVR_{i}\) is the \(HVR\) for the \(i\) grid. When the DN value of the \(i\) grid was greater than the full \(HVR\), it was considered an outlier, and the \(HVR\) was 0.
# A Stackelberg Game Approach to Model Reference Adaptive Control for Spacecraft Pursuit-Evasion
## 3 Stackelberg Equilibrium-Based Model Reference Adaptive Control Algorithm
### Resolution of Stackelberg Game
\right] \tag{9}\]  
The optimal control gain vector \(\mathbf{L}_{t}^{*}=\left[ \begin{array}{cc}\mathbf{L}_{p,t}^{*}&\mathbf{L}_{e,t}^{*}\\ \end{array} \right]^{\mathsf{T}}\) can then be solved for simultaneously:  
\[\mathbf{L}_{t}^{*}=\mathbf{\Psi}_{t}^{-1}\left[ \begin{array}{cc}-\mathbf{Y}_{p}^{ \mathsf{T}}\mathbf{P}_{p,t+1}\mathbf{X}_{d}\\ \mathbf{Y}_{e}^{\mathsf{T}}\mathbf{P}_{e,t+1}\mathbf{X}_{d}\end{array} \right] \tag{10}\]  
Mathematically, a Nash Equilibrium solves a set of simultaneous optimization problems: \(\mathbf{a}_{p}^{*}=\underset{\mathbf{a}_{p}}{arg\,min}\,\mathbf{I}_{p}(\mathbf{a}_{p}^{*},\bm {a}_{e})\) and \(\mathbf{a}_{e}^{*}=\underset{\mathbf{a}_{e}}{arg\,min}\,\mathbf{I}_{e}(\mathbf{a}_{p}^{*},\bm {a}_{e})\). In contrast, the Stackelberg equilibrium solves the hierarchical problem shown in (6). This structural difference, captured by the coupled Riccati Equations (7) and (8) and the resulting asymmetric gain solution in (10), avoids the unrealistic assumption of simultaneous moves inherent in Nash models. In terms of computational complexity, the main burden is solving the N-coupled Riccati equations, which scales linearly with the horizon N. Each step involves matrix multiplications and an inversion of the \(6\times 6\) coupling matrix \(\mathbf{\Psi}_{t}\), which has a constant complexity. The online adaptive update is computationally light.
###### Abstract  
(1) Background: Viral respiratory infections are common triggers for asthma exacerbation, often leading patients to the emergency department (ED). COVID-19, the disease caused by the SARS-CoV-2 virus, typically presents with respiratory symptoms, from minor symptoms, up to and including severe acute respiratory failure. Data on the association between asthma and COVID-19 are conflicting, and those from an ED setting are scarce. Our aims were to assess the prevalence and outcome of patients with asthma admitted to the ED for COVID-19. (2) Methods: We performed a case-control study, extracting data from a registry of adult patients with confirmed COVID-19 consecutively admitted to the ED of our hospital between March 2020 and January 2021. (3) Results: We identified 83 patients with asthma out of 935 individuals (prevalence 8.9%). There were no significant differences between cases and controls regarding both the proportion of hospital admissions and patients with critical COVID-19. (OR 1.37; 95% CI 0.52-3.56; and (OR 0.74; 95% CI 0.31-1.78 respectively). (4) Conclusions: In patients admitted to the ED for COVID-19, the prevalence of asthma was not higher than expected, and asthma was not associated with a worse outcome, in terms of the rate of hospitalization and critical COVID-19 disease.  
Keywords:asthma; COVID-19; emergency department +
Footnote †: journal:
# Advances, Progress, and Future Directions of Renewable Wind Energy in Brazil (2000-2025-2050)
## 2 Potential of Wind Energy Resources in Brazil
### Offshore Wind Energy Potential
Although Brazil's offshore wind potential is enormous, its development faces challenges, such as high initial installation costs, the need for transmission infrastructure to bring offshore-generated power to the onshore grid, and environmental considerations, such as the impact on marine wildlife and bird migration routes [29; 30; 31]. However, with technological advancements and cost reductions in the offshore wind industry, these challenges are expected to be overcome in the medium term [27].
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Fuzzy Logic
#### 3.1.6 Eaucf  
Authors of [36] have proposed a fuzzy based algorithm for the energy-aware in unequal clustering called EAUCF. It aims to reduce the energy consumption of CHs within clusters since they are either close to the BS or may have low remaining battery power. The FL implemented in EAUCF uses an _if-then_ mapping rules to handle doubts in CH radius estimation. Moreover, the algorithm also uses a probabilistic model which is employed for electing tentative CHs. In each clustering round, every sensor node has to generate a random number between 0 and 1 and compares it with the predefined threshold. If the random number is more than the threshold, the node becomes a tentative CH, for electing the final CH, EAUCF take into account the residual energy and the distance to the BS before calculating the competition radius. EAUCF has a better performance compared to LEACH, CHEF, and EEUC in terms of the death of first node, the half nodes alive, and the energy consumption.
## 3 Spoofing Detection Method
### Hypothesis Test
According to Formulas (8) and (9), calculate the estimated values and variances of the parameters \(\hat{\phi}_{i}\) and \(\hat{\phi}_{j}\) for the satellites \(i\) and \(j\). 2. Calculate the estimated values and variances of parameters \(\hat{\gamma}_{i}\) and \(\hat{\gamma}_{j}\) for satellites \(i\) and \(j\) according to Formulas (18) and (20), and calculate the estimated values and variances of parameters \(\hat{M}_{i}\) and \(\hat{M}_{j}\) for satellites \(i\) and \(j\) according to Formulas (19) and (21). 3. Calculate the arrival angle difference \(dq_{ij}\) and elevation angles \(\theta_{i}\) and \(\theta_{j}\) for satellites \(i\) and \(j\) based on ephemeris information. Phase 3: Likelihood Ratio Test 1. Determine the monitoring threshold based on the set detection and false alarm probability. 2. According to Formula (25), calculate the SSE. If the SSE is lower than the threshold, the signals of satellites \(i\) and \(j\) are real signals. Otherwise, at least one of the signals is a spoofing signal.
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
## 2 Materials and Methods
### Adaptive Blocking Matrix  
Our proposed methods rely on the optimal identification and removal of background brain activity and interference and, in the context of the GSC, it is the blocking matrix \(C_{\perp}\) the main responsible of that task. Its quiescent form comes with the disadvantage of not providing an optimal value for the level of rank-reduction. In order to overcome such difficulty, it has been proposed in [17] that such blocking can be optimally designed with the use of an adaptive blocking matrix (ABM). Let us consider the following design for it:  
\[C_{\perp}=\mathrm{I}-\mathbf{b}W_{h}, \tag{11}\]  
where  
\[\mathbf{b}=\widehat{R}W_{h}\Big{[}W_{h}^{T}\widehat{R}W_{h}\Big{]}^{-}, \tag{12}\]  
such that the filters in (12) correspond to the Wiener-Hopf solution that minimize the mean-squares of \(X_{0}\). Then, the ABM in (11) comes from writing \(X_{0}\) as a function of \(Y_{k}\).