esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Finite Element Modeling
### Parametric Finite Element Study  
As discussed in the previous section, the FE model is capable of simulating the actual testing condition with minimum error. Hence, in this section, the verified FE method is utilized for a parametric study to investigate various uprights with different heights and thicknesses as well as different reinforcement spacings under flexural loading. The models for the parametric study are presented in Table 1. The results of the parametric study are classified into two groups of loading under major and minor axes, which are presented in the following sections. The convention used to name the specimens is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Non-reinforced model} & \multicolumn{2}{c}{Evaluation criteria} \\ \cline{2-3}  & Std * & 0.0619 \\ \cline{2-3}  & Pearson (r) & 0.9981 \\ \cline{2-3}  & \(\text{R}^{2}\) & 0.9963 \\ \hline \multirow{4}{*}{200 mm reinforced model} & Evaluation criteria & \\ \cline{2-3}  & Std & 0.0803 \\ \cline{2-3}  & Pearson (r) & 0.9937 \\ \cline{2-3}  & \(\text{R}^{2}\) & 0.998 \\ \hline \multirow{4}{*}{300 mm reinforced model} & Evaluation criteria & \\ \cline{2-3}  & Std & 0.0698 \\ \cline{2-3}  & Pearson (r) & 0.998 \\ \cline{2-3}  & \(\text{R}^{2}\) & 0.996 \\ \hline \hline \end{tabular}
\end{table}
Table 3: FEM vs. experimental results accuracy details in terms of evaluation criteria.  
Figure 8: Comparison of FE model against major-axis test results for (**a**) non-reinforced model, (**b**) 200 mm reinforced model and (**c**) 300 mm reinforced model.  
Figure 9: Linear regression diagram for; (**a**) non-reinforced model, (**b**) 200 mm reinforced model, and (**c**) 300 mm reinforced model.
# A Simple Estimation Method of Weibull Modulus and Verification with Strength Data
## 2 Conclusions  
1. Methods of estimating Weibull modulus (m) of an experimentally obtained dataset were examined. These utilized the average (\(<\)\(\sigma\)\(>\)) and standard deviation (S) (or coefficient of variation, CV) based on the normal distribution. Several approximate relationships have been proposed starting from [PERSON] [11], but all of them deviate from the exact expression given with the gamma function.
2. The exact expression can be represented by m = 1.271 \(<\)\(\sigma\)\(>\)/S = 1.271/CV with R\({}^{2}\) = 0.9999. [PERSON] used 1.20 as the constant [11].
3. In order to obtain m values that fit with the actually observed material strength datasets, a reduction of the constant from 1.271 to 1.10 is found to be optimal. This produces the modified Robinson relation of m = 1.10 \(<\)\(\sigma\)\(>\)/S = 1.10/CV, which can estimate m values that are in good agreement with the m values obtained from Weibull analyses. This agreement was verified by over 260 datasets of the strength of metals, ceramics, fibers, and composite materials, with most of the data from tensile or flexure testing.
4. Applications of this simple estimation method are discussed. A common notion that ductile metals always have high m values must be discarded. Causes of m reduction need to be considered as material variation, and test accuracies can affect the outcomes. The method can add a quantitative tool based on the Weibull theory to engineering practice.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 2 Model of Cross-Medium Communication Channel
### Model Adjustments and Analysis
#### 2.2.3 Second-Order Nonlinear Correction  
The modeling of water surface waves involves fluctuation theory, which encompasses both linear and nonlinear approaches. In linear fluctuation theory, it is typically assumed that the fluctuations have a small amplitude, resulting in a negligible wave steepness.  
Figure 2: Multipath fading under experimental conditions.  
Higher-order terms in the wave equation can be disregarded, and only first-order terms are considered. Equation (5) represents a model of microamplitude waves at the water surface, derived using linear fluctuation theory. It is commonly accepted that fluctuations at the submicron level can be referred to as microamplitude waves.  
However, in the current experimental setting, characterized by a shallow water depth and high sound pressure level, the amplitude of surface waves could reach several hundred micrometers, leading to a significant wave steepness. In this scenario, the microamplitude wave model derived from linear fluctuation theory was no longer suitable. To correct this, we introduced a quadratic correction coefficient \(\eta p_{i}\)[2] to the amplitude of the water surface, which can be expressed by the following correction formula:  
\[\delta_{0}(\eta)=\delta+\eta p_{i}^{\ 2} \tag{9}\]
# Misconfiguration in Firewalls and Network Access Controls: Literature Review
## 3 Misconfigurations
### Irrelevance  
Like redundant rules, irrelevant rules are of little to no severity to the security of a network. A rule is irrelevant if it is impossible for the firewall to encounter a packet that matches it [16]. Examples of an irrelevant rule could be blocking packets from a subnet that has no route to this particular firewall. If communication is outright impossible due to lack of a physical/wireless connection or otherwise, there is no need to make any rules regarding that subnet. Another example could be blocking traffic from the internet when hosts outside the user's network cannot reach their subnet. If no hosts behind a particular firewall have port forwarding or some way to be accessible from the internet, there would be no need to block traffic from the internet or from certain IP addresses out on the internet because outside hosts cannot send packets to the hosts behind the firewall.  
Unlike redundancy, irrelevance cannot occur because of the configuration of other devices. If an upstream firewall blocks traffic that a downstream firewall also blocks, that situation is not irrelevance. That traffic cannot reach the downstream firewall, but it is considered to be inter-firewall redundancy because the traffic is blocked in two different firewalls. The distinction is that irrelevant rules are irrelevant because even in the absence of any rules, the traffic is still impossible [19].  
Irrelevant rules are another one of the misconfigurations with zero impact on a network's security. Whether or not a network has irrelevant rules will not impact the security of the network. In addition, like redundant rules, the largest impact of irrelevant rules is on the performance of the firewall [18]. Irrelevant rules suffer from the same issues where their very existence slows down the firewall with no benefit to the security of the network. If a rule is found to be irrelevant, it can be safely removed without any effect on the overall security on the network, and doing so can only be beneficial to network speeds.
# Chlorobenzene Removal Using DBD Coupled with CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) Catalyst
## 3 Results and Discussion
### Effect of SED on the Performance of DBD Reactor
#### 3.2.3 Ozone Production  
The production of ozone is one of the most significant evaluation indexes for the DBD discharge. Figure 7 shows the variation of O\({}_{3}\) production with different SED in a single DBD reactor and the plasma-catalytic reactors. It can be seen that the addition of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) in the DBD reactor reduces the production of ozone when the SED is less than 750 J\(\cdot\)L\({}^{-1}\), but as SED increases to 900 J\(\cdot\)L\({}^{-1}\) or higher, the addition of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) in the DBD reactor turns to improve the production of ozone. [PERSON] also found that catalysts behave conversely when placed after or inside the reactor [55]. The contrary effect of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) loading under different SED indicates that the loading of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) might influence the production of ozone by different mechanisms. On the one hand, the oxidation of chlorobenzene induced by CuO catalyst expands the consumption path of active oxygen. It was considered that the addition of a catalyst could provide adsorption sites for active oxygen [43]. The absorbed oxygen can be consumed through the oxidation of Cu\({}^{+}\) (Section 3.2) or the reaction of chlorobenzene (Section 3.5). Moreover, considering the oxidation potential of ozone (2.07 eV), ozone can also be consumed through the oxidation process of chlorobenzene [56]. On the other hand, it was found that the coupling of the catalyst can improve the discharge condition of the reactor, which is beneficial for the production of active oxygen [57]. As is known, the production of active oxygen is related to the formation of ozone. The contrary effect of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) loading for the production of active oxygen ultimately makes the behavior of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) loading changes under different SED.  
Figure 6: CO\({}_{\rm x}\) (**a**), CO (**b**), and CO\({}_{2}\) (**c**) selectivity of chlorobenzene with catalyst loadings and absence of catalyst as a function of SED (flow rate = 3 L/min).
## 3 Results and Discussion
### L*a*b* Value Determination  
As shown in Figure 3, the color and structure of samples heated by either heating method, i.e., OH or COV, did not differ when evaluated by eye. This was also confirmed by visual evaluations during the descriptive sensorial testing by the panelists, as no significant differences in color were scored. Using a colorimeter to measure the L*a*b* values of the samples, however, revealed a significant difference between the two heating methods. OH samples had decreased a and b values and thus less red and yellow color, but more grayish attributes compared to COV-heated samples. Additionally, OH-treated samples had a brighter appearance compared to COV-treated samples, evidenced by higher L values. [PERSON] et al. also describe OH-treated meat batter emulsions as having less color development compared to COV-treated samples when heated to equal final temperatures and at comparable treatment times to this study (i.e., when an electric field of 3 V/cm was applied) [11]. Notably, the authors did not measure significant differences. However, when heated at faster heating rates (i.e., when electric fields of 5 V/cm and 7 V/cm were applied), [PERSON] et al. describe an increased a value [11], which is contrary to the findings of this study.  
Figure 3: (**a**) Photograph of freshly cut Bologna-style sausages heated via Ohmic and conventional heating (OH and COV, respectively), which were used for L*a*b* value determination. (**b**) Graphic representation of colors of OH- and COV-treated samples by L*a*b* values from Table 1.  
The color differences measured in this study via a colorimeter are attributed to a shorter heating process during OH treatment, which was less than 5% of the time needed to heat the COV-treated samples. During the cooking of meat emulsion batters, reddening of the batter occurs. Commonly, heating of meat emulsion batters leads to a loss of red color when no sodium nitrite is added. NaNO\({}_{2}\) helps stabilize myoglobin, which has the biggest impact on the red color of the sausage [35]. Reddening is enhanced by time and temperature. Temperatures between 40 and 50 \({}^{\circ}\)C or an increased duration at these temperatures will increase the stability of myoglobin, leading to preservation of the red color [36]. During OH, the residence time at these temperatures is significantly shortened, and this will lead to a less red and saturated color with more grayish attributes.
## 3 Study Area  
The research was carried out in the Emalahleni area (Figure 1), Mpumalanga province, South Africa (Figure 1). Emalahleni, formally known as Witbank, is one of South Africa's key mining areas [24]. Emalahleni falls within the grassland biome and is one of six local municipalities in the Nkangala District municipality, with a total area of 331.353 km\({}^{2}\). Like most portions of the country, the study area experiences seasonal weather variations. The temperature ranges from approximately 2.2 \({}^{\circ}\)C to 26 \({}^{\circ}\)C throughout the year, with temperatures rarely falling below \(-1\)\({}^{\circ}\)C or rising over 29 \({}^{\circ}\)C. January is the hottest month of the year, while July is the coldest and receives the least amount of rainfall. December has the rainiest days.
## 3 Results and Discussion
### Evaluation of PLS Calibration Models
Despite the procedures to minimize the noise in the calibration data set (simple pre-processing, spectral region selection and outlier removal), for some parameters, the low number of samples available and the low diversity of analytical values might have contributed to PLS model overfitting. In order to minimize this effect, a full cross-validation procedure was used for PLS model development. However, an external validation using an independent test set was required to evaluate the degree of overfitting of the developed PLS models by comparing the performance of the test set with that of the calibration set. Figure 2: Representation of the Raman-PLS calibration models developed for (**a**) total carbohydrates; (**b**) carbon dioxide (CO\({}_{2}\)); (**c**) ammonia (NH\({}_{3}\)); (**d**) nitrate (NO\({}_{3}\)); (**e**) polyhydroxyalkanoates (PHAs); (**f**) phosphate (PO\({}_{4}\)); (**g**) total organic content (TOC); (**h**) total phosphorus (total P); (**i**) total suspended solids (TSSs); (**j**) volatile fatty acids (VFAs); and (**k**) volatile suspended solids (VSSs) along with the statistical calibration results.
# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 1 Introduction
### Background
Predominantly, both domestic and international prediction models for animal diseases rely on classical machine learning methods such as support vector machines and decision trees. These methods allow the analysis and forecasting of disease probabilities, often emphasizing comparative evaluations across multiple machine learning algorithms. Currently, there is not universally accepted the most effective or accurate model, leaving ample room for experimentation and innovation. Of particular note are the parameters of machine learning models, which typically require manual configuration prior to training to regulate model learning and complexity [11]. This parameterization significantly impacts model performance and generalizability. Traditionally, grid search (GS) has been employed as a standard method to determine optimal hyperparameters [12]. However, GS becomes computationally expensive when dealing with extensive parameter spaces. Currently, swarm intelligence optimization algorithms are increasingly being adopted to navigate parameter spaces and identify optimal parameter combinations, thereby enhancing model performance and generalization capabilities [13]. In the realm of feature selection, these algorithms prove beneficial by identifying the most pertinent feature subsets [14]. This capability is particularly valuable for managing high-dimensional data and extensive feature sets, ultimately improving model efficiency and accuracy. In this study, our principal objective was to construct a predictive model for subclinical mastitis in dairy cows, integrating an enhanced GOOSE optimization approach with the XGBoost algorithm. We hypothesized that the IGOOSE-XGBoost model would provide superior predictive accuracy compared to traditional machine learning approaches for both disease classification and somatic cell score prediction.
## 2 The Experiment Setup
However, when the laser is detuned, the in-phase component does not vanish due to the inhomogeneous light shifts in the vapor cell, and can be utilized as the feedback control for laser-frequency stabilization. Figure 2: **(a)** Experimental results of the amplitude of demodulated magnetic resonance signal with different laser detunings. The positive detuning is about 800 MHz and the negative detuning is about \(-800\) MHz. The amplitude of in-phase component \(X\) does not vanish when laser is detuned and the Larmor frequency is obtained by the quadrature component \(Y\) locked at zero. **(b)** The theoretically simulated results of the amplitude of demodulated magnetic resonance signal with and without laser detuning, after considering the spatially inhomogeneous light shifts.
# Construction of Compact Finite Difference Schemes by Classic Differential Quadrature
## 1 Introduction
Let \(c=(x-x_{n})/h\) and make the interval \(x\in[x_{n},\ x_{n+1}]\) normalized as \(c\in[0,\ 1]\), then the Equation (10) can be converted into  
\[\frac{d}{dc}y=hf(x_{n}+ch,\ y(x_{n}+ch)) \tag{11}\]  
On this basis, we can apply the \(s\)-stage differential quadrature method (1) to Equation (11), and deduce that [15; 16]  
\[\widetilde{\mathbf{G}}\left[ \begin{array}{l}y_{n+1}\\ \vdots\\ y_{n+s}\end{array} \right]+\mathbf{G}_{0}y_{n}=h\left[ \begin{array}{l}f( \widetilde{x}_{1},\ y_{n+1})\\ \vdots\\ f(\widetilde{x}_{s},\ y_{n+s})\end{array} \right] \tag{12}\]  
in which \(y_{n+z}=y(x_{n}+c_{z}h)\), \(z\in(0,\ s)\), \(\widetilde{x}_{z}=x_{n}+c_{z}h\), \(z\in(1,\ s)\),  
\[\widetilde{\mathbf{G}}=\left[ \begin{array}{ccc}g_{11}&\cdots&g_{1s}\\ \vdots&\ddots&\vdots\\ g_{s1}&\cdots&g_{ss}\end{array} \right],\ \mathbf{G}_{0}=\left[ \begin{array}{l}g_{10}\\ \vdots\\ g_{s0}\end{array} \right] \tag{13}\]  
If the initial value of \(y_{n}\) is given, the value of state variable \(y\) at different grid points, that is \(y_{n+z}\in(1,\ s)\), can be obtained by solving the aforementioned nonlinear Equation (12). It should be noted that, although the ordinary differential Equation (10) is used as a trial case, the variable \(x\) in this equation can either represent the space or the time, and naturally the stencil length \(h\) can be either a spatial discrete step size or a time discrete step. For ordinary differential initial value problems, it has been proven in reference [15] that, when using uniform grids, Chebyshev grids and Chebyshev-Gauss-Lobatto grids, the classical DQM--i.e., Equation (12)--are all \(s\)-stage \(s\)-order methods with A-stability, while the classic DQM combined with Legendre grids can achieve the accuracy of \(2s\)-\(2\) order.
# Experimental Investigation on a Throttleable Pintle-Centrifugal Injector
## 3 Experimental Investigation on the Pintle-Centrifugal Injector
### Experimental Process  
During the test, a constant water mass flow rate is first established using a controllable cavitating venturi. At this fixed flow rate, the pintle stroke is varied to adjust the size of the annular slit in the injection channel, while a high-speed camera records the corresponding spray images and parameters. After completing a full stroke range experiment, the flow rate is adjusted to a new fixed value, and the above procedure is repeated to obtain a series of spray images and parameters corresponding to different mass flow rates and pintle strokes. For optical camera acquisition of spray images, the extraction of the atomization cone angle was previously usually performed by direct measurement. The general approach is to visually inspect the spray boundary, determine the boundary lines on both sides of the spray, and then use a protractor to measure the angle or import the image into the image software and use the protractor in the function module to obtain the angle. This method is simple and straightforward, but highly subjective, especially when the image boundary is blurred, and can lead to large errors in the results.  
In this study, the spray cone angle was extracted using the method proposed by [PERSON] [18]. Two straight lines were taken at different distances downstream of the injector outlet and the spray width on both lines was measured.
## 2 Materials and Methods
### Inoculant Preparation  
_G. diaz_ was cultured from glycerol preserved stock in a modified MESMA/ATGUS medium (2.7 g L\({}^{-1}\) glucose; 4.8 g L\({}^{-1}\) dipotassium phosphate (K\({}_{2}\)HPO\({}_{4}\)); 0.65 g L\({}^{-1}\) monopotassium phosphate (KH\({}_{2}\)PO\({}_{4}\)); 1.8 g L\({}^{-1}\) mannitol; 4.4 g L\({}^{-1}\) 2-(N-morpholino) ethanesulfonic acid (MES hydrate); 2.7 g L\({}^{-1}\) yeast extract) [39]. Nutrients were combined in ASTM Type II, deionized water (>1 M\(\Omega\)-cm) and autoclaved prior to aseptic bacterial introduction to ensure a pure, single-strain culture.  
Inoculated medium and bacteria-free controls were incubated in a positive-pressure, enclosed shaker table (30 \({}^{\circ}\)C at 120 rpm) for five days. Colony forming units (CFU) per milliliter solution were determined by serial dilution and standard plate counts on semi-solid ATGUS medium (same as above, plus 0.8% wt/vol agar) in triplicate, corroborated with OD\({}_{600}\) based estimation (NanoDrop One, Thermo Scientific) against standard curves. The achieved CFUs in concentrate were 4.0 \(\times\) 10\({}^{8}\) mL\({}^{-1}\) for BSS trial #1, BSS trial #2, and BIB trial #1, and 1.8 \(\times\) 10\({}^{9}\) mL\({}^{-1}\) for BIB trial #2. Differences in CFU were attributed to random variations inherent in bacterial culturing.  
Liquid medium for the seed soak inoculation was prepared from a proprietary blend of nutritive and adhesive adjuants in deionized water, as described by an operating protocol provided by Azotic Technologies, Ltd. (Internal document dated 2016) and reported on the safety data sheet (Azotic Technologies, Ltd.; Lancashire, United Kingdom; 2017). For each trial, 50 mL portions of medium were aseptically aliquoted into each of two sterilized flasks. One flask then received a 50 mL portion of cultured bacterial concentrate, and the second flask received a 50 mL portion of deionized water.  
The portion containing bacterium, now at \(\frac{1}{2}\) cultured CFU mL\({}^{-1}\), became the inoculation treatment (INOC), while the adjuvant and water only solution became the uninoculated control (NON). Both treatment stocks were assessed for equal pH and EC to ensure that the seeds soaked would experience identical conditions save for the bacterial presence. Thus, BSS trials #1 and #2 (BSS1, BSS2), and BIB trial #1 (BIB1) received 2.0 \(\times\) 10\({}^{8}\) CFU mL\({}^{-1}\) on seed, and BIB trial #2 (BIB2) received 9.0 \(\times\) 10\({}^{8}\) CFU mL\({}^{-1}\) on seeds at inoculation.
## 3 Experiment and Results
### Calculate Target Light  
After obtaining the transmittance, we recover the final haze-free target radiance image using the atmospheric scattering model in Equation (2). As shown in Figure (13): Figure 13a is Original intensity image. Figure 13b is Dehazed intensity image.  
Figure 12: Pseudo-color maps of transmittance under different scenarios: (**a**): Minimum transmittance pseudo-color map; (**b**): Maximum transmittance pseudo-color map; (**c**): Transmittance pseudo-color map; (**d**): Filtered transmittance pseudo-color map.  
Through visual comparison of light intensity images and their dehazed counterparts across various scenarios, it is evident that the optical information of targets obscured by fog has been effectively restored. In Scenarios 1 and 2, the building structures concealed by heavy fog demonstrate significant recovery. In Scenario 3, vessels submerged in dense fog become clearly identifiable. In Scenario 4, our algorithm efficiently eliminates light fog, successfully revealing target information.