esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Statistical Analysis of the System Loss Model  
The developed propagation model, whose components are determined by Equations (2), (5), and (6), can be expressed as:  
\[L_{\text{SL(dB)}}=\overline{L_{S}(d_{0})_{\text{(dB)}}}+10\cdot n\cdot\log_{10} \Bigl{(}\frac{d_{\text{[m]}}}{d_{\text{[m]}}}\Bigr{)}+10\cdot\log_{10}\Bigl{[} \Lambda\Bigl{(}\mu_{\text{B(dB)}},\sigma_{\text{B(dB)}}\Bigr{)}\Bigr{]}+20\cdot \log_{10}\Bigl{[}\Lambda\Bigl{(}\mu_{\text{F(dB)}},\sigma_{\text{F(dB)}} \Bigr{)}\Bigr{]} \tag{7}\]  
where:  
* \(\Lambda\Bigl{(}\mu_{\text{B(dB)}},\sigma_{\text{B(dB)}}\Bigr{)}\) is a random variable with lognormal distribution, which represents the power of the large-scale fading, due to the body shadowing effect, where \(\mu_{B}\) and \(\sigma_{B}\) are the mean value and standard deviation, respectively;
* \(\Lambda\Bigl{(}\mu_{\text{F(dB)}},\sigma_{\text{F(dB)}}\Bigr{)}\) is a random variable with lognormal distribution, which represents the amplitude of the small-scale fading, caused by the multipath effect, where \(\mu_{F}\) and \(\sigma_{F}\) are the mean value and standard deviation, respectively. In order to analyze the accuracy of the developed propagation model, two statistical evaluation measures were used, i.e., the SEE and the ME [32]. In this case, the SEE is a measure of the model accuracy, and the ME determines the degree of dispersion of empirical data around the model. For the better fitting model, the SEE value is lower and the ME value is closer to 0 dB. It should be also noted that the model is considered to be sufficiently accurate if the SEE value does not exceed 8 dB [3; 39]. Due to the two random factors included in Equation (7), it was decided to calculate both ME and SEE from 10,000 model simulations. In Table 6, the statistical analysis of the accuracy of the developed model for all scenarios is presented.
# Effects of Ethanol-Diesel on the Combustion and Emissions from a Diesel Engine at a Low Idle Speed
## 3 Results and Discussion
### Combustion Characteristics
#### 3.1.1 Combustion Pressure and Heat Release Rate
In addition, with the addition of ethanol, fuel in parts of the pilot injection that have not been burned due to the slow burning of the pilot injection in DE_10 will be burned with the main injection, and the heat release rate will increase rapidly. Figure 3: (**a**) Combustion pressure and (**b**) heat release rate of test fuels. Figure 4 shows (a) the BSFC and BTE and (b) the exhaust gas temperature for each fuel condition. The BSFC increased from 336.9 g/kWh (DE_0) to 348.3 g/kWh (DE_3) as 3.3%, 350.3 g/kWh (DE_5) as 4.0% and 356.7 g/kWh (DE_10) as 5.9% with an increasing blend ratio of ethanol. This is because more fuel was consumed due to the lower LHV of ethanol. The BTE, on the other hand, is lower than that of diesel fuel when ethanol is blended, and remains at a similar level of 23.8% as the ethanol blend ratio increases. In all studies using ethanol-blended or alcohol-blended fuel, the BSFC was seen to increase. In a study by [PERSON] et al. [18], ethanol had a lower BTE than diesel fuel, and in the study of [PERSON] et al. [20] under low engine load conditions, the BTE tended to be lower than that for pure diesel fuel. [PERSON] [14] reported that the BTE of alcohol (ethanol and methanol) blended fuel is lower than that of diesel fuel because of the higher BSFC. [PERSON] et al. [31] reported that the reason for the reduced BTE of blended ethanol was the low LHV, resulting in more fuel being consumed to produce the same power, an increased ignition delay due to the low cetane number and a decreased combustion temperature due to the quenching effect of ethanol. [PERSON] [13] and [PERSON] et al. [17], on the other hand, reported that the BTE increases with the blending of ethanol. [PERSON] et al. [19] explained that there are several factors that affect the BTE. (i) The combustion is improved by the increase of oxygen due to the inclusion of ethanol. (ii) The temperature of the flame decreases due to the high evaporative latent heat, reducing the heat loss of the cylinder. (iii) The increase in the ignition delay due to the low cetane number increases the fuel burned during the premixed period. (iv) In order to produce the same output with a lower LHV, more fuel is injected and burned by the expansion stroke, and the diffusion combustion is also increased. These factors have complex effects depending on the engine load, speed and fuel conditions. Additionally, the exhaust gas temperature decreases with the increasing ethanol blending ratio. A study by [PERSON] et al. [22] with ethanol and methanol blended fuel, an alcohol fuel, also reported that the exhaust temperature was lowered as the blend ratio of alcohol increased.
# A New Method to Verify the Measurement Speed and Accuracy of Frequency Modulated Interferometers
###### Abstract  
The measurement speed and measurement accuracy of a displacement measuring interferometer are key parameters. To verify these parameters, a fast and high-accuracy motion is required. However, the displacement induced by a mechanical actuator generates disadvantageous features, such as slow motion, hysteresis, distortion, and vibration. This paper proposes a new method for a nonmechanical high-speed motion using an electro-optic modulator (EOM). The method is based on the principle that all displacement measuring interferometers measure the phase change to calculate the displacement. This means that the EOM can be used to accurately generate phase change rather than a mechanical actuator. The proposed method is then validated by placing the EOM into an arm of a frequency modulation interferometer. By using two lock-in amplifiers, the phase change in an EOM and, hence, the corresponding virtual displacement could be measured by the interferometer. The measurement showed that the system could achieve a displacement at 20 kHz, a speed of 6.08 mm/s, and a displacement noise level \(<100\) pm\(//\sqrt{\text{Hz}}\) above 2 kHz. The proposed virtual displacement can be applied to determine both the measurement speed and accuracy of displacement measuring interferometers, such as homodyne interferometers, heterodyne interferometers, and frequency modulated interferometers.  
electro-optic modulator; frequency modulation; displacement measuring interferometer +
Footnote †: journal: Applied Sciences
# Changes in the Volatile Profile of Wheat Sourdough Produced with the Addition of Cava [PERSON]
## 3 Discussion
### Analysis of Volatile Compounds in Different Sourdough Samples
#### 3.2.3 Aldehydes and Ketones  
Aldehydes are formed by unsaturated fatty acid decarboxylation as well as lipid oxidation [2; 5; 7; 31]. Hexanal was one of the dominant aldehydes in this study (Table 4). It produces fatty, green, grassy, powerful, and tallow odors and has an odor threshold in water of 4.5-5 ppb [2]. Although lipid oxidation products such as hexanal have been reported several times and in high concentrations in bread crumbs, they generally produce off-flavors [43].  
Nonanal, an aldehyde that has also been reported in sparkling wine [32], showed the greatest increment in samples with lees compared to the control, increasing eight times its value when 2% Cava lees (\(w/w\)) were added to the sourdoughs formulation. Moreover, benzaldehyde was only identified in sourdoughs with 1% and 2% Cava lees. It is an aldehyde commonly found in sparkling wine [31; 32] and it has been reported in other foodstuff formulated with wine lees [23]. Nevertheless, the absence of certain aldehydes or their low production may be explained by the ability of heterofermentative LAB to reduce aldehydes to other compounds [37; 38].  
Regarding ketones, acetoin was the main one in all samples (Table 4). Acetoin is a key aroma in bread formed during fermentation, with a positive correlation with wheat bread; therefore, the higher the concentration, the better the acceptance by consumers [43]. It is characterized by a buttery and creamy odor and comes from the bacterial conversion of citrate into pyruvate, which then results in acetoin in order to equilibrate the redox balance of the cell metabolism [44].  
As for 2-undecanone, this ketone has only been reported once in gluten-free hemp-enriched sourdough bread [11], but it has been identified in wine as well [45; 46; 47]. In this study, 2-undecanone was only found in samples with Cava lees, and it was the only ketone identified in lees (Table 4). This could indicate that it comes from lees, perhaps being attached to their surface during the ageing of the sparkling wine.
## 3 System Models and Problem Formulation
### Waiting Time  
Once client \(i\) finishes its local model training, it can immediately upload its local model to the FL server if the bandwidth is unoccupied, i.e., there is no other client currently uploading its local model. However, if the bandwidth is occupied, client \(i\) has to wait, and the waiting time depends on its position in the queue. Specifically, let \(\mathbf{\mathcal{I}}_{j}\) denote the set of clients in tier \(j\), who are sorted based on the increasing order of their computing latency, i.e., \(\forall i^{\prime}\in\mathbf{\mathcal{I}}_{j},t_{i^{\prime}}^{comp}\leq t_{i+1}^{ comp}\), where \(i^{\prime}\) is the index of the clients in \(\mathbf{\mathcal{I}}_{j}\). Hence, if client \(i\) is in tier \(j\), its waiting time can be calculated based on the following recursive function.  
\[t_{i^{\prime}j}^{wait}=\begin{cases}0,&i^{\prime}=1,\\ \max\Bigl{\{}0,t_{i-1}^{comp}+t_{i-1,j}^{wait}+t_{i-1,j}^{up}-t_{i}^{comp} \Bigr{\}},&i^{\prime}>1,\end{cases} \tag{3}\]
## 3 Results and Discussion
### \(S_{L}\)-\(x\)-\(R\) Calibration Surface Considering Fan-Shaped Effect  
The \(S_{L}\)-\(x\)-\(R\) calibration surface of light metals (Al, Mg) is shown in Figure 11, and that of heavy metals (Cu, brass, SS) is shown in Figure 12, from which it can be seen in the sortable range, the R_value was mainly related to \(S_{L}\) and somewhat related to the position in the X-direction, that is, the fan-shaped effect also had a certain influence on it. For light metals, the overall R_value was less than 1.7, and the calibration surfaces of Al and Mg were close to each other; for heavy metals, the overall R_value was between 1.7 and 2.4, and the calibration surfaces of Cu, brass and SS were also close to each other. That shows when the discrimination coefficient \(\alpha 5\) for light metals and heavy metals is taken by 1.7, it is easier to sort light metals from heavy metals but relatively difficult for identification of light metals or heavy metals.  
Figure 10: Binary image of effective points.  
Table 3 shows \(S_{L}\)-\(x\)-\(R\) calibration surface coefficients with binary nonlinear polynomial and RMSE (root mean square error) of 5 materials (Mg, Al, SS, brass, and Cu). The RMSE between 0.018 and 0.027 was relatively small compared with the distance between fitting surfaces, indicating that each fitting surface was featured by sufficiently high accuracy.
# Robotics Applications in the Hospital Domain:
## 4 Robotic Systems in Hospitals Settings
### Cleaning and Disinfection Robots
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Robot System** & **Xenex Lightstrike High-Intensity Cleaning and Disinfection Robot [49]** & **Tru-D—Portable UVC Disinfection System [50]** & **Sterilray—Autonomous Disinfection Vehicle [51]** \\ \hline
**Description** & \begin{tabular}{l} High-intensity cleaning and disinfection robot used to \\ disinfect patient rooms, surgical areas, and other critical zones \\ (cost \(\approx\) EUR 12 k) [51]. \\ \end{tabular} & \
\begin{tabular}{l} Used mainly for terminal \\ disinfection of bedrooms of \\ patients after discharge or before \\ the admission of new patients \\ (cost \(\approx\) EUR 12 k) [51]. \\ \end{tabular} &
\begin{tabular}{l} Employed in large and \\ difficult-to-reach areas, such as \\ corridors and common areas, \\ where you can operate it \\ autonomously for continuous \\ disinfection [53]. \\ \end{tabular} \\ \hline
**Stage** & \begin{tabular}{l} Operational stage [49]. \\ \end{tabular} & \
\begin{tabular}{l} Operational stage [50]. \\ \end{tabular} &
\begin{tabular}{l} Research stage [53]. \\ \end{tabular} \\ \hline
**Features** & \begin{tabular}{l} Integrated sensors have the \\ capacity to interrupt the device \\ when they detect motion and are \\ connected to a network. \\ \end{tabular} & \
\begin{tabular}{l} Sensor360 technology can \\ calculate the precise UVC dose \\ that is required. \\ \end{tabular} &
\begin{tabular}{l} A programmable robotic \\ instrument is a beneficial tool in \\ situations where the quantity of \\ disinfect required varies. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Related cleaning and disinfection robot systems.
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 4 Error Analysis  
In the gas column concentration inversion experiment, the gas escaped rapidly under the influence of the atmosphere, and it was difficult to calculate the specific optical path of the gas at a certain position, so the accuracy of the inversion method could not be evaluated. In order to obtain the accuracy of gas column concentration inversion, the error of gas column concentration inversion was analyzed using a dual-channel mapping error analysis experiment and a gas column concentration inversion error evaluation experiment.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 3 Results and Experiments
### Result of Detection from Real Demo Camera  
Figure 20, a composite, shows a comparison of livestreaming and object identification utilizing a sophisticated video processing system. The \"Live Transmission\" part on the left shows raw footage: the top frame shows an Ayrshire cow in its natural habitat, while the bottom frame shows two people. Figure 21 depicts the intrusion alarm alert messages received by the user. The \"Detection\" part on the right, in contrast, illustrates the system's capacity to recognize and name things within the same situations. The cow, now labeled with a bounding box, shows the system's capacity to recognize and classify animals. Similarly, one of the people in the bottom right shot is highlighted with a bounding box labeled \"person\", indicating the system's skill in recognizing humans. The contrast between the raw feed and processed output emphasizes the system's efficacy in real-time object identification and the display of contextual information.  
\begin{table}
\begin{tabular}{c c c} \hline
**Ref** & **Approach** & **Accuracy** \\ \hline [PERSON] et al. [11] & SVM & 91.60\% \\ \hline [PERSON] et al. [32] & SVM & 97\% \\ \hline [PERSON] et al. [32] & RF & 78\% \\ \hline [PERSON] et al. [8] & W-CoHOG & 93.30\% \\ \hline [PERSON] et al. [33] & Bi-LSTM & 95\% \\ \hline [PERSON] et al. [34] & CNN & 97\% \\ \hline Proposed Method & Yolo V8 & 99\% \\ \hline \end{tabular}
\end{table}
Table 6: Comparison with previous research.  
Figure 20: Detection using Yolo V7 upon ESP32 transmission.
# Potential Benefit of Carbon Benchmarking: Analysis of Semiconductor Industry in Taiwan
## 3 Empirical Data Sources and Data Processing
These groups are divided into high-revenue DMUs (HRs), middle-revenue DMUs (MRs), and low-revenue DMUs (LRs), according to the annual net sale revenue of US$6001 million and above, US$2001-6000 million, and US$2000 million and below, respectively. A summary of the basic statistical results obtained is shown in Table 1 below and the scatter diagram shown as Figure 2.  
To evaluate the effectiveness of carbon reduction efforts, the SCC method was used. SCC data were derived mainly from a report prepared by the United States Environmental Protection Agency [31]. The SCC in the report covers the period 2015-2050. The results given for later years in this period have a high degree of uncertainty. This study then uses the SCC results for 2015 as the basis for calculation. On the basis of the SCC data shown in United States Environmental Protection Agency [31], at discount rates of 5%, 3%, and 2.5%, the SCC for 2015 is calculated to be US$11, US$36, and US$56, respectively. Regarding the choice of discount rate, the interest rate on government bonds in Taiwan in 2015 was 2.98%, which is closest to the 2.5% discount rate in the SCC data, so it was decided to use the 2.5% discount rate for SCC data. That is, an SCC that is equivalent to US$56 per ton was used as the basis for evaluation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Statistical** & **Net Sales** & **CO\({}_{2}\) Emissions** & **Salary** & **Net Fixed** & **Cost of Goods** \\
**Indicators** & **Revenue** & **(thousand** & **Expenses** & **Assets** & **Sold** \\  & **(million$)** & **tons/year)** & **(million$)** & **(million$)** & **(million$)** \\ \hline \hline \multicolumn{6}{c}{All 81 DMUs} \\ \hline Mean & 1997.25 & 764.78 & 186.10 & 2268.26 & 1446.71 \\ Standard Deviation & 2665.39 & 846.95 & 235.31 & 2317.73 & 1484.20 \\ Maximum Value & 12,745.48 & 3387.74 & 1442.02 & 11,489.33 & 6574.42 \\ Minimum Value & 84.71 & 48.71 & 25.66 & 60.93 & 90.10 \\ \hline \multicolumn{6}{c}{HRs: 8 DMUs} \\ \hline Mean & 9610.62 & 2969.35 & 749.64 & 7867.68 & 5359.97 \\ Standard Deviation & 1678.47 & 324.52 & 356.41 & 1546.82 & 655.37 \\ \hline \multicolumn{6}{c}{MRs: 14 DMUs} \\ \hline Mean & 3133.46 & 1214.83 & 314.62 & 4046.36 & 2459.65 \\ Standard Deviation & 840.82 & 502.01 & 138.63 & 1534.36 & 503.24 \\ \hline \multicolumn{6}{c}{LRs: 59 DMUs} \\ \hline Mean & 817.69 & 359.07 & 89.05 & 1231.07 & 761.85 \\ Standard Deviation & 508.26 & 183.12 & 37.46 & 1008.87 & 554.25 \\ \hline \hline \multicolumn{6}{c}{Note 1: Data expressed in monetary terms were deflated using 2010 as the base year. Note 2: The average exchange of US$6 to TWDS is 1:31.93 in 2015.} \\ \end{tabular}
\end{table} 
Table 1: Descriptive Statistics for Empirical Variables. DMUs: decision-making units, HR: high revenue, MR: middle revenue, LR: low revenue.
## 2 The Methodology
### One-Dimensional Convolutional Neural Network  
A typical CNN consists of an input layer, an output layer, a convolution layer, and a pooling layer [24]. The convolution layer performs local feature extraction on the input feature map through the convolution kernel. The further downsampling will be performed by the pooling layer. The main features of CNN are local perception, weight sharing, and pooling. In CNN, the convolutional layer and the pooling layer appear alternately. The principle of the one-dimensional CNN is shown in Figure 2.  
Assuming that the first layer is a convolutional layer, the calculation formula of the one-dimensional convolutional layer is as follow:  
\[x_{j}^{l}=f\left(\sum_{i=1}^{M}x_{i}^{l-1}*k_{ij}^{l}+b_{j}^{l}\right) \tag{1}\]  
where, \(x_{j}^{l}\) is the jth feature map of the lth layer, \(f(\cdot)\) represents the activation function, M represents the number of input feature maps, \(x_{i}^{l-1}\) represents the ith feature map of the \(l-1\) layer, * represents convolution operation, \(k_{ij}^{l}\) represents a trainable convolution kernel, and \(b_{j}^{l}\) represents the jth bias of the lth layer.  
With the consideration of the convergence speed and overfitting problem, this paper uses the rectified linear unit (ReLU) activation function. ReLU has a faster convergence rate than the Sigmoid  
Figure 1: The general procedure of the presented method. AE: acoustic emission; CNN: convolutional neural network; GRU: gated recurrent unit.  
Figure 2: The schematic of one-dimensional CNN.  
in the gradient descent and can effectively prevent the over-fitting problem. The ReLU activation function is as follow:  
\[f(x)=max(0,x) \tag{2}\]  
After the pooling layer is connected to the convolution layer, the feature map is downsampled according to a certain pooling strategy to obtain a lower resolution feature map. The most commonly used pooling strategy is the maximum pooling. Maximum pooling reduces the number of output nodes and enhances the robustness of the network to input characteristics. The _l_+1 th layer is the pooling layer. It is calculated as follow:  
\[x_{j}^{l+1}=f\Big{[}down\left(x_{j}^{l}\right)+b_{j}^{l+1}\Big{]} \tag{3}\]  
where, down (\(\cdot\)) is a downsampling function.
Currently, adsorption is considered as a good method for the removal of a wide spectrum of toxic substances--like heavy metals and emerging contaminants--from wastewaters [8]. Bioadsorption uses lignocellulosic residues, which are cheap and highly available, and could be useful in the treatment of effluents [9]. Other merits of the biosorption method include the reuse of used biosorbents, the short operating time, and the absence of production of secondary contaminants [6]. Recently, many inert-biomass-derived adsorbents have been studied for water cleaning; postharvest and agro-industrial residues have been studied for this purpose. Agricultural waste such as tea residues [10], coffee residues [11], kenaf [12], coconut fiber [13], Japanese loquat [14], and orange peel [15] have shown good performance under the different conditions evaluated due to their lignocellulosic nature which guarantees the presence of functional adsorption groups [16], proving that forest residues are a good biomaterial font for the elimination of metallic ions in solution [17]. The use of bioadsorbents of residual lignocellulosic origin has advantages, such as low cost, high availability, and excellent performance at high and low concentrations [18]. There are many forestall residues used as heavy metal adsorbent, including pine [19; 20], picea from Afghanistan [17], poplar wood sawdust [21], Leucana leuccephala [22], and many others, finding that sawdust residues show great skills in wastewater treatment because they are abundantly available and cheap [23]. The _Eucalyptus globulus_ Labill (Myrtacae) is the forest species with the highest production in the Peruvian central highlands for around 100 years, with a production around 68,089.26 m\({}^{3}\) of sawn wood and 526,350 m\({}^{3}\) of roundwood [24]. Due to the high production of this species and its high logging in Peru [25], a large amount of residual material such as sawdust is produced. In this sense, the objective of this study is to investigate the viability of forest and industrial waste product in its natural form as an adsorbent for the elimination of Pb (II). The waste product investigated is sawdust from native _Eucalyptus globulus_ Labill, determining the effect of pH on the adsorption capacity of the biomaterial in batch system. The best fit to the kinetic (pseudo-first order, pseudo-second order, Elovich, and intra-particle diffusion) and isothermal (Langmuir and Freundlich) models was investigated. Sawdust was characterized by ash content determination, scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR).