esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Validation of the Numerical Model
### Experimental Tests  
A series of experimental tests were carried out in the 1990s, in 21 configurations of different types of slim floor beams, and a final compendium was written in 2008 [13]. Among these configurations, two were considered herein for the validation of the numerical model: WFRC 44174 [23] and WFRC 66162 [24]. The steel beams in the experimental tests were double-T profiles (Figure 4), similar to the slim floor beam types assessed in this study (only the steel profile, no supplementary plate welded under the bottom flange).  
The selected experimental tests, conducted in 1989 [23] and 1996 [24], respectively, at the Warrington Fire Research Center, consisted of a simply supported beam with a 4.5 m distance between the supports. The test assemblies were placed on top of the oven, with the beam supports outside of the oven, in such a way that only a 4 m span was heated under ISO fire. They were loaded through four hydraulic rams, along the longitudinal axis of the steel profile, in four points at 1/8, 3/8, 5/8, and 7/8 of the supported span. The load applied through each ram was 76.59 kN for the WFRC 44174 test and 84.6 kN for the WFRC 66162 test, respectively.  
Figure 4a shows the cross-section of the WFRC 44174 composite behavior test, with a 254 \(\times\) 254 mm, S275 steel profile, and the upper part embedded in a Grade 30 light weight concrete slab. The bottom part of the steel profile was covered with Grade 30 normal weight concrete, and the lower flange remained exposed. The tensile test made on a material sample taken from the tested steel profile showed a value of 267 N/mm\({}^{2}\) for the yield strength.  
Figure 4b shows the cross-section of WRFC 66162 non-composite behavior test, realized of an asymmetric 280 mm deep 280/180 mm wide hot rolled profile, steel grade S355. On the lower flange, a 210 mm deep metal docking was placed to support the Grade 30 normal weight concrete cast in place. The tensile test made on a material sample taken from the steel profile showed a value of 402 N/mm\({}^{2}\) for yield strength.  
The failure criterion was considered when reaching a vertical displacement of 1/20 of span, in both configurations. The WFRC 44174 test was stopped when the vertical displacement at mid-span reached 226 mm after 52 min, while the WFRC 66162 test was stopped at 225 mm after 107.5 min.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 3 Results and Analysis
### Application in Junction Zone of Hot and Cold Clutter  
As the influence of the internal clutter movement is much smaller than that of targets with strong pollution in the training sample, several STAP performance indicators, such as output SCNR loss (relative to the output SCNR of the optimal processor), SCNR improvement factor, and minimum detectable velocity (MDV), were used for comparison.  
Figure 12(a) shows the output SCNR loss curves of the traditional method and the proposed method. It can be seen that the SCNR loss of the proposed method is between zero and one, and the SCNR loss of the proposed method is 3.4 dB less than that of the traditional method, meaning that this method can obtain a better detection performance. Another way to measure the performance of the STAP algorithm is the SCNR gain relative to a single array element and a single pulse. The SCNR improvement factor is usually large and increases as the pollution grows stronger. Figure 12(b) shows the SCNR improvement factors obtained by the two methods. It can be seen from the figure that the improvement factor of the traditional method is more than 10 dB higher than that of the proposed method.  
The MDV is defined as the velocity closest to the main lobe clutter velocity at which an acceptable SCNR loss can be achieved. The acceptable SCNR performance is defined as the SCNR loss, at that time, where the acceptable performance is at least 50% of the maximum detection range. Here, we used the reference threshold to calculate the minimum detectable speed. The SCNR loss diagrams obtained by the traditional method and the proposed method are shown in Figure 12(c),d. Let \(\rm{f_{L}(x)}\) and \(\rm{f_{U}(x)}\) be the Doppler frequencies below and above the main-lobe clutter Doppler at which an acceptable SCNR loss is achieved. We defined the minimum detectable Doppler \(\rm{f_{min}}\) as follows:  
Figure 11: STAP results of traditional method: (**a**) two-dimensional frequency response plane diagram, (**b**) the output result after detection.  
Figure 12: STAP results of proposed method: (**a**) two-dimensional frequency response plane diagram, (**b**) the output result after detection.  
\[f_{\min}(x)=\frac{1}{2}(f_{L}(x)-f_{L}(x)), \tag{21}\]  
which is equal to one-half of the width of the main-lobe clutter notch. The minimum detectable velocity was then defined as  
\[\text{MDV}(\text{x})=\frac{\lambda}{2}\text{f}_{\min}. \tag{22}\]  
The minimum detectable velocity that was calculated was 2.3759 and 2.0693, respectively. It can be seen that the proposed method reduces the minimum detectable velocity by 0.3 m/s.
## 2 Materials and Methods
Consequently, the complicated form can be simplified as Equation (10) without ignoring accuracy too much in the calculation of the friction factor and Reynolds number product for practical engineering applications. It can also be demonstrated that Equation (6) reduces to its continuum flow limits as \(Kn^{*}\to 0\):  
\[ \begin{array}{rl}f_{app}Re_{D_{h}}=&\frac{24}{\left(1+(\frac{b}{H})\right)^{ 2}\left\{1-\frac{192(\frac{b}{H})}{\pi^{5}}\left[\tanh\left(\frac{\pi}{2(\frac {b}{H})}\right)+\frac{1}{243}\tanh\left(\frac{3\pi}{2(\frac{b}{H})}\right) \right]\right\}}\\ &+\frac{1}{3x^{+}}-2\sum_{i=1}^{2}\frac{\left[3-\exp\left(-4\alpha_{i}^{2}x^{ +}\right)\right]\exp\left(-4\alpha_{i}^{2}x^{+}\right)}{\alpha_{i}^{2}x^{+}} \end{array}  \tag{10}\]  
As the expression of Equation (11) demonstrated, \(fRe_{D_{h}}\) represents the friction factor Reynolds number group in fully developed flow regime, which is merely the function of \(b/H\)  
\[f\text{Re}_{D_{h}}=\frac{24}{\left(1+(\frac{b}{H})\right)^{2}\left\{1-\frac{1 92(\frac{b}{H})}{\pi^{5}}\left[\tanh\left(\frac{\pi}{2(\frac{b}{H})}\right)+ \frac{1}{243}\tanh\left(\frac{3\pi}{2(\frac{b}{H})}\right)\right]\right\}} \tag{11}\]  
The effectiveness of this fully developed model was validated with a great deal of available data. The experimental data of \(0<H/s<1.4\) for pressure loss coefficient of the 90 deg bend are obtained from [PERSON] (2018) and curve fitted as following. \[K_{\text{ort}}=3.64-9.15\frac{H}{s}+10.87\left(\frac{H}{s}\right)^{2}-4.29 \left(\frac{H}{s}\right)^{3} \tag{12}\]The pressure loss coefficient values of 90 deg bend model can be calculated with the formula developed by [PERSON] et al. [41] when \(H/s>1.4\)  
\[K_{\text{ort}}=0.5\bigg{(}\frac{1+V_{cl2}/V_{cl1}}{2}\bigg{)}^{2} \tag{13}\]  
Pressure drop form \(\Delta P=K\Big{(}\rho V^{2}/2\Big{)}\) is provided by [PERSON] et al. [41], which is a function of free-flow area ratio for frontal area \(\sigma=b/(b+t)\) to calculate the pressure losses between the inlet and exit. Contraction and expansion loss coefficients are obtained through curve fitting the data from [PERSON] et al. [42]:  
\[K_{c}=0.4\Big{(}1-\sigma^{2}\Big{)}+0.4 \tag{14}\]  
\[K_{e}=\Big{(}1-\sigma^{2}\Big{)}-0.4\sigma \tag{15}\]
# A Study on the Impact of Blue Light and Its Brightness on Long-Term Memory
###### Abstract  
The purpose of this study is to evaluate the effect of blue light and its brightness on long-term memory. Blue light was tested under four distinct illuminance conditions ranging from 400 to 1000 lux, and the results were compared with those of red light as a comparison group. A total of 25 individuals, including 16 men and 9 women with a mean age of \(22.04\pm 1.74\) years, participated in these tests based on a within-subject research design. Initially, each participant was exposed to individual light conditions for 30 min in the morning. Subsequently, they were asked to complete learning memory tasks, which involved memorizing specific words composed of randomly chosen English consonants. Exactly 20 min after these memory tasks, their long-term memory retention was measured through recall. It was found that blue light was superior to red light in terms of long-term memory enhancement; the difference was statistically significant, with a confidence level of 95%. Moreover, brighter light environments were found to affect long-term memory performance more significantly compared to darker light environments. That said, there was no significant difference in interaction effects between light color and brightness. These findings confirm the results of previous studies highlighting the effectiveness of blue light in enhancing memory retention while also demonstrating that the degree of enhancement may vary depending on light brightness.  
blue light; brightness; long-term memory; LED lighting; red light +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Leachate Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**DM**} & \multicolumn{2}{c}{**Coumarin**} & \multirow{2}{*}{**pH**} & \multicolumn{2}{c}{**Lactic Acid (LA)**} & \multirow{2}{*}{**Accile Acid (AA)**} & \multirow{2}{*}{**LA/AA**} \\ \cline{2-2} \cline{6-11}  & & \% FM & HSD & & mg/kg DM & HSD & & HSD & \% DM & HSD & & HSD \\ \hline FM & 20.75 \(\pm\) 0.02 & C & 0.57 \(\pm\) 0.34 & C & 4.15 \(\pm\) 0.02 & B & 5.44 \(\pm\) 0.56 & B & 1.42 \(\pm\) 0.26 & A & 4.16 \(\pm\) 0.63 & A \\ WSC & 32.35 \(\pm\) 0.12 & A & 8.04 \(\pm\) 1.14 & A & 4.20 \(\pm\) 0.01 & A & 8.20 \(\pm\) 0.43 & A & 2.51 \(\pm\) 0.82 & A & 3.47 \(\pm\) 0.96 \\ FM + WSC & 27.79 \(\pm\) 0.11 & B & 5.59 \(\pm\) 0.49 & B & 4.16 \(\pm\) 0.00 & B & 8.13 \(\pm\) 0.67 & A & 2.46 \(\pm\) 0.11 & A & 3.31 \(\pm\) 0.20 & A \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of silage variants FM, WSC and their mixture. that LA is stronger than the other acids in silages (acetic, propionic and butyric acids) and, therefore, it is usually the main cause of the decline in the silage pH. A similar trend as for LA was observed for acetic acid (AA): AA increased with the increasing WSC content of the silage. However, there were no statistically significant differences between the variants. According to [PERSON] and [PERSON] [51], the usual values of the concentration of AA in legume silages with a DM of 30-40% range from 2 to 3% DM, while, in corn silages, these values vary from 1 to 3% DM. Moderate concentrations of AA in silage can be beneficial because AA has the ability to inhibit the development of yeasts and fungi that cause the development of aerobic decay [48]. Silage with very low concentrations of AA may be unstable when exposed to air. The LA:AA ratio is commonly used as a qualitative indicator of fermentation. Good silage fermentation usually has a ratio of these acids approximately in the range from 2.5 to 3.0 [47]. In our experiment, the LA:AA ratio ranged from 3.31 (variant FM + WSC-50) to 4.16 (FM-100). Thus, no variant was within the optimal interval of LA:AA ratio. Silages with a very high LA to AA ratio can sometimes be more aerobically unstable than those with a normal ratio because low concentrations of acetic acid may not be sufficient to inhibit yeasts that assimilate lactate. In contrast, a ratio of LA to AA of less than 1 is usually indicative of abnormal fermentation [47; 51]. Butyric acid (BA) was not detected in any of the silage samples. The ethanol content (EE) ranged from 0.30 (variant FM-100) to 0.83 (variant FM-100), falling within the range described as usual by [PERSON] and [PERSON] [51]. According to these authors, the optimal EE content in legume silages with a DM of 30-40% reaches values of 0.2-1.0% DM and, for corn silages, 1.0-3.0% DM. Values of NH\({}_{3}\)-N content were very low and ranged from 2.03 (FM + WSC-50) to 4.82 (FM-100)--Table 6. According to [PERSON] and [PERSON] [51], NH\({}_{3}\)-N concentrations in the range of 10-15% CP for legume silages with 30-40% DM and 5-7% CP for corn silages are common.
# Statistical Analysis of Cutting Force and Vibration in Turning X5 CrNi18-10 Steel
## 3 Results
### Standard Deviation Results  
The standard deviation was calculated for the magnitude of cutting forces data and the vibration data using MATLAB_R2023a academic license. The standard deviation is calculated using Equation (3), where _s_--sample standard deviation, EX1 represents experiment number 1, _n_--number of elements in the sample, _x_--each data point, and _x_--mean of the dataset [31].  
\[std(EX1)=\sqrt{\frac{1}{n-1}\sum\limits_{i=1}^{n}\ \left(x_{i}-\bar{x}\right)^{2}} \tag{3}\]  
The two datasets have nearly close standard deviation values, which suggests that the amount of variability or spread in each dataset is the same, even though the datasets are different in terms of their values or distributions. Experiment 1, for both force and vibrations, has a low standard deviation indicating more consistent sampling with less fluctuation. The standard deviation between the force and vibration data is shown in Figure 8.  
In Experiment 12, the force has a high standard deviation compared to the vibration, suggesting greater variability. In Experiments No. 3, 5, 8, and 17, the values are closer for both the force and vibration, suggesting that the variability is similar for both the cutting force and vibration. It is important to assess the consistency and variability during each test.
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 3 Method
### Hypergraph Position Attention Convolution Networks
#### 3.3.3 Pooling Operation  
The pooling operation performs aggregation of local area features to reduce the dimensionality of the point cloud, it can help to retain important feature information as well as reduce the amount of computation. In point cloud segmentation, the features of each vertex in the hypergraph \(G^{l+1}\) come from the set of aggregated output features in the previous-level hypergraph \(G^{l}\). The vertex feature \(V^{l+1}\) of the l+1 layer in \(G^{l+1}\) is computed as follows:  
\[V^{l+1}=pooling(\Phi(V^{l}_{out}))) \tag{13}\]  
where \(V^{l}_{out}\) denotes the vertex output feature at layer \(l\). \(\Phi\) denotes the ResNet-like attentional convolution operation. \(Pooling\) denotes the pooling function, and we use maximum pooling for the downsampling operation.
## 2 Material and Methods
### Spectral Analysis  
From each of the fragments, a spectrogram calculation was performed. The spectrogram is a two-dimensional matrix where the \(x\)-axis is the time, the \(y\)-axis describes the frequency (\(\omega\)), and the value is signal intensity, which is usually visualized as a color map. Overall, the spectrogram gives a visual representation of the power of frequencies in signal over time. The spectrogram operation consists of dividing the signal \(x(n)\) (where \(n\in(0,L-1)\)) into short sections of length \(m\) with a window \(w(n)\) and then performing the Fast Fourier Transform on each of them [29] (Equation (1)):  
\[STFT\{x[n]\}(m,\ \omega)\equiv X(m,\ \omega)=\sum_{n=0}^{L-1}x[n]w[n-m]e^{-j \omega n} \tag{1}\]  
The spectrogram with time axis \(t\) and frequency axis \(\omega\) is next achieved by calculating the magnitude squared of the STFT (Equation (2)):  
\[spectrogram\{x(t)\}(m,\ \omega)\equiv\left|X(m,\ \omega)\right|^{2} \tag{2}\]  
All operations related to the spectrogram and subsequent steps were performed using the SciPy library for the Python language [30]. When executing a spectrogram, this library returns a two-dimensional matrix where the time axis is graded every second, and the frequency axis is graded by the value that results from dividing the Nyquist frequency (half the sampling frequency, i.e., 22,050 Hz) [31] by 129, which gives values every 171 Hz. Thus, when the spectrogram is visualized as a color map, each pixel of such image will describe the signal intensity for the frequency band from \(X\) to X + 172 Hz and 1 s. The next step is the segmentation of the spectrogram matrix with respect to the frequency axis. The matrix is divided into 129 one-dimensional pieces, each of which describes the Power Spectral Density for the selected frequency band during the entire measurement. This process is presented in Figure 6.  
The purpose of the above operations performed on the spectrogram is to facilitate the detection of the indicated defect. As is visible in Figure 2, the defect presents itself as a significant peak passing through the vast majority of the frequencies present in the signal. After transforming the spectrogram into a series of 129 vectors, such peaks are visible on most of them as a local maximum. Since the defect produces a periodically repeated sound, the local maxima signifying this sound also repeat from time to time.
## 2 Conceptual Background
### Software Valuation Methodology  
There are various methodologies for technology valuation, which can be classified into three categories: the income approach, market approach, and cost approach. A mixed methodology that combines these categories has also been developed.  
The income approach is based on discounted cash flow methods (DCF). This approach calculates the expected future free cash flow that a company can generate from its operating activities in the future, and the level of risk associated with this cash flow. By dividing the net present value, which is discounted using a rate that reflects the total number of issued shares, this method calculates the future rate of return that a company can achieve as a future profit per share.  
The market approach is a valuation method that assesses the value of technology based on the market price that is typically established between parties in a transaction who possess sufficient transaction information and willingness to trade. To utilize the market approach, there must be transactions between independent parties in an active market where comparable technological assets are traded, and there must be ample technological assets that have been traded in the past, with accessible information on their transaction prices [39]. In many instances, it can be challenging to find a comparable target [40].  
The cost approach is a valuation method that estimates the various costs of software development, based on the principle that a rational person will not pay more than the cost to obtain a software asset with equal need or utility [41]. However, there are limitations to this approach in that input costs do not necessarily relate to future value or revenue generation, and not everyone can create the same technology asset value with the same cost [42; 43]. To overcome these limitations, research is being conducted to determine optimal prices through software development cost analysis, markup level analysis, and software standard pricing based on the cost-oriented pricing method.  
This study employs a combination of the income approach, market approach (specifically, the royalty approach), and cost approach to establish a reasonable standard for valuing software in the automotive industry. Furthermore, the study includes an in-depth analysis of the concept proposal for technology transactions and the utilization of existing legacy code to expand IP-based business.
# Comparative Analysis of Marine Alternative Fuels for Offshore Supply Vessels
## 2 Methodology
### Credible Design Solutions (Step 2)  
#### 2.2.1 Market Availability for Fuels and Propulsion Systems by 2026  
A realistic roadmap for deploying carbon-neutral propulsion systems requires a corresponding technological maturity. This includes non-fossil fuels such as ammonia, hydrogen, biodiesel, LNG, methanol, and electricity, and also a carbon capture system. To identify all credible fuels and technologies available in 2026, the market availability was explored.  
A DNV report [16] provides a timeline for the implementation of alternative fuels and CCS technologies in maritime applications. Methanol and ammonia are the closest to regulatory and technological readiness, particularly for engines, while hydrogen and CCS are expected to take longer to reach high regulatory maturity and full onboard deployment. The graph underscores the gradual transition in maritime energy sources, indicating which technologies will likely become viable and safe for onboard application first, with methanol leading, followed by ammonia, hydrogen, and CCS. The following is a summary of the current status/projection of each fuel technology projected by the report.  
* Methanol: Methanol is expected to reach high regulatory maturity for onboard use before 2026. Both 2-stroke and 4-stroke engines for methanol are projected to be available around 2024, while other applications, such as in boilers and fuel cells, are anticipated to become available by approximately 2028.
* Ammonia: The 2-stroke engine technology is anticipated to be available around 2025, with regulatory maturity still in progress. Full maturity for regulations is expected by 2030. Other applications for ammonia, such as in fuel cells and boilers, are projected to reach availability in later years, with some potentially extending into the early 2030s.
* Hydrogen: The 4-stroke engine and fuel cell technologies are anticipated to become available around 2026-2028. Regulatory maturity for hydrogen technologies is forecasted for around 2030.
* CCS (Carbon Capture and Storage): CCS technology is in the early stages of regulatory maturity. Full maturity is expected to be achieved around 2030, with onboard technology potentially available around the same timeframe.
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
###### Abstract  
The fundamental issue for Beyond fifth Generation (B5G) is providing a pervasive connection to heterogeneous and various devices in smart environments. Therefore, Drone's play a vital role in the B5G, allowing for wireless broadcast and high-speed communications. In addition, the drone offers several advantages compared to fixed terrestrial communications, including flexible deployment, robust Line of Sight (LoS) connections, and more design degrees of freedom due to controlled mobility. Drone's can provide reliable and high data rate connectivity to users irrespective of their location. However, atmospheric disturbances impact the signal quality between drones and users and degrade the system performance. Considering practical implementation, the location of drones makes the drone-user communication susceptible to several environmental disturbances. In this paper, we evaluate the performance of drone-user connectivity during atmospheric disturbances. Further, a Machine Learning (ML)-assisted algorithm is proposed to adapt to a modulation technique that offers optimal performance during atmospheric disturbances. The results show that, with the algorithm, the system switches to a lower order modulation scheme during higher rain rate and provides reliable communication with optimized data rate and error performance.  
drones; adaptive modulation; K-means clustering; B5G; machine learning Article