esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Selection of Parameters to Be Used in the Data-Mining Hub
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Parameters** & **Heat Stress** & **Subclinical M mastitisti (SCM)** \\ \hline Environmental conditions & Ambient Temperature (Ta) & Increased temperature tends to increase risk for heat stress & Increased temperature tends to increase risk for SCM \\ Relative Humidity (RH) & Increased humidity increases THI & Increased humidity increases THI & Increased humidity increases THI \\  & Temperature-Humidity Index (THI) & 73–79 THI: Mild heat stress; 80 to 89: Moderate heat stress & \textgreater{}9 THI values are associated with a higher risk of CM development [20] \\ \hline  & Bodyweight (BW) & No changes were reported in the bodyweight of the cows as affected by heat stress & Bodyweight is positively related to SCC [21] \\ Physiological body conditions & Body condition score (BCS) & No changes were reported in the bodyweight of the cows as affected by heat stress & BCS is not significantly related to SCM [21] \\  & Body temperature & Heat stress dairy cows show increased temperature [18] & SCM is accompanied by a high rise in temperature [23] \\  & Rumen temperature & Rumen temperature & Cows affected with SCM increasing environment temperature [22] & Cows reduce ruminating time when affected [32] \\  & Rumen pH & Heat stress results to reduced pH [25] & Cows increased standing time when affected [32] \\  & Respiration rate & Cows are heat-stressed at 61 breaths per minute [28] and severe heat stress at 120 breaths per minute [29] & Cows increased respiratory rate if affected with moderate mastitistits [27] \\ \hline  & Ruminating & Rumination time decreases with increasing THI [30; 31] & Cows reduce ruminating time when affected [32] \\ Behavior status & Standing & Prolonged standing indicates heat stress to cows [33] & Cows increased standing time when affected [32] \\  & Lying & Heat-stressed cows show a restless state [34] & Cows decreased lying time when affected [32; 32] \\  & Drinking & Heat-stressed cows require increased drinking water [35] & Cows drink less when affected [32] \\ \hline  & Somatic cell count (SCC) & SCC tend to rise with temperature and humidity, especially during the summer months [37] & SCM show sharp increase of SCC [24; 47] \\ Milk performance indexes & Milk solids-not-fat (SNF) & Heat stress reduced SNF [38] & SCM reduced SNF [45] \\  & Milk fat (MF) & Heat stress reduced MF [40] & SCM reduced MF [45] \\  & Milk protein (MP) & Heat stress decreased MP [41] & SCM reduced MP [45] \\  & Milk urea nitrogen (MUN) & Heat stress tends to increase MUN 42 & SCM tend to reduce MUN [46] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected parameters that were used for detecting the status of the cows.
# Technology-Economy-Policy: Biochar in the Low-Carbon Energy Transition--A Review
## 6 Economy: Application of Biochar
### Application of Biochar in the Renewable Energy Sector
#### 6.1.2. Biomaterials in MFCs for Bioelectricity Production  
A microbial fuel cell (MFC) can be an effective solution to energy shortages and environmental pollution. The MFC is a device that consists of anode and cathode chambers separated by a proton exchange membrane, and that uses microorganisms as catalysts to convert chemical energy into electricity. In their life cycle (growth, reproduction, and production of intermediates), microorganisms metabolize organic material, which undergoes oxidation and reduction, and produce electrons and protons [168]. Under anaerobic conditions, the electrons generated in the anode chamber pass to the electrode through a membrane-bound electron transporter, electron mediator, or nanowires [169]. From the anode electrode, electrons move to the cathode through the outer circuit, while at the same time, a proton moves to the cathode through the proton exchange membrane [170]. The reduction process takes place in the cathode chamber between the electron, proton, and electron acceptor, and direct current is produced. Different types of electrode materials have been studied, and their performance depends on their physical and chemical properties, which affect microbial adhesion, electrode resistance, and the electron transfer rate. Despite advances in MFC technology, a lower current output and higher costs remain major constraints to using the technology on a larger scale. Many studies indicate that 20-50% of the cost of operating an MFC is due to the electrode material (gold, stainless steel, nickel, titanium, or copper, etc.), as they are non-renewable and require additional surface modification [63]. Biochar is considered an alternative, renewable material for electrode production [171]. It is usually produced from locally available raw materials and results in a reduction in the costs associated with purchasing, transporting, and storing raw materials [172]. Biochar-based electrodes are inexpensive compared to other commercial solutions. A team of researchers ([PERSON] et al.) prepared an N/Fe-doped carbon electrode (N/Fe-C) by directly carbonizing waste adsorbent, and showed that the incurred cost of the N/Fe-C material was about a thousand times lower (depending on the raw material's cost and energy consumption) than that of a commercial Pt electrode [173].
## 1 Introduction
This infers that samples with large simple pores and few microporosity display higher P and S waves velocities, as well as higher cementation factors than samples with small and complicated pores [27]. Concerning chalk specifically, P and S waves have been studied based on porosity and pore fluid saturation at three different scales [29]. A power law between porosity and formation factor in cracked mlomite is then found [30]. Studies of pore level heterogeneity on petrophysical properties on carbonates cores have shown that much of the porosity is dominated by microporosity, and that large intergranular pores are responsible for increasing permeability and decreasing electrical conductivity [31]. Electrical microscopic parameters are highly influenced by micro and centimetric scale heterogeneity [9]. Limestone rock porosity and permeability are also controlled by pore shapes and sizes [32; 33]. By dissolving carbonate rock samples, structural parameters such as tortuosity and network geometry control changes in porosity and permeability [34; 35]. Thus, characterizing the pore structure is also crucial in predicting dissolution rate and localization. At pore scale, 3D image analysis is very useful to save time and to study the hydrodynamic parameters of rocks without destructing them [27]. However, the pixel size is a discriminant parameter, especially for rocks with many small pores, such as carbonates rocks and particularly chalk. This technique, paired with laboratory analysis, yields a more complete characterization of rocks [33], including pore distribution determination [36]. Porosity, permeability and pore shape can be studied on 2D slices [27] and 3D samples [32; 37; 33], as well as structural properties [9]. Most of these studies are conducted on one rock type and take into account some existing petrophysical parameters. Relationships between properties are therefore limited to few petrophysical parameters, which imply an incomplete rock characterization. Consequently, in order to better characterize carbonate reservoirs at various scales, the identification of relevant relationships between measured and calculated properties is essential in order to determine which are the most efficient and necessary. For this purpose, about thirty samples from three carbonates types have been selected: chalk, entrochallimestone and dolomite. Laboratory petrophysical measurements and imaging calculations were performed at core scale and then compared to large scale data.
## 5 Simulation Results and Analysis
### Network Throughput without Obstacles  
Based on IEEE 802.11 g, network bandwidth is about 5.8 MBps; we evaluate the proposed work on that basis. The ICBM-UAV protocol performed better than the DFM flocking algorithm, as shown in Figure 10. The ICBM-UAV performs much better than AODV and OLSR protocols, as shown in Figure 10. In FANET, the drones are continually moving and changing their coordinates, which impacts packet transferring routes since OSLR changes the database and develops a route in advance for packet transfer. This effect can be seen in the graph. On other hand, AODV carries out on-demand routing; AODV performs better than OSLR because it builds the route for a packet when needed. DFM flocking throughput is higher than the AODV's and OSLR's due to its leader and follower mechanism. Each leader contains two followers, which means the route is predefined for packet transfer. On the other hand, ICBM-UAV performs a little higher than DFM, as ICBM-UAVs keep their table updated in a timely manner and always keep an active transmission route for packet transmission.  
Figure 9: Final result with obstacle placement.
## 4 Experiment and Results
### Experimental Results  
In the verification experiments, three SiO\({}_{2}\) films (Si substrate) with different thicknesses (25, 45, and 100 nm) were measured by acquiring BFP images at 20 defocus positions using a positioning stage with an interval of 10 \(\upmu\)m. Multiple images were collected at the same defocus position for repeatability analysis. The ellipsometric parameters \(\Psi_{\text{exp}}\) and \(\Delta_{\text{exp}}\) of the defocus image were calculated according to an estimated defocus position using the method shown in Section 3.3. Then, the ellipsometric compensation amounts \(\tan^{2}\delta\Psi\) and \(2\delta_{\Delta}\) were obtained by referring to the compensation maps at the corresponding defocus position. Film thickness was finally calculated by fitting using the LM algorithm. Simultaneously, film thickness was also calculated from the original BFP images without vibrational compensation. All the obtained film thickness results are presented in Figure 9.  
The experimental results showed that the measurement accuracy was significantly improved by correcting the defocused BFP images using the proposed method. Compared to the case without vibration defocus compensation, this method significantly improved the measurement accuracy of thin film, from more than 30 nm to less than 1 nm.  
Figure 8: Calibrated ellipsometric maps of compensation (**a**) \(\tan^{2}\delta\Psi\) and (**b**) \(2\delta_{\Delta}\) from a SiO\({}_{2}\) film.
## 2 Overview of Study Area, Data, and Methods
### Research Methods
#### 2.4.2 Individual Fixed-Effects Model  
The individual fixed-effects model refers to deterministic variables other than explanatory variables, whose effects vary only with the individual and not with time. Because the carbon sink value of the explained variable in the study was affected by the previous period, the lag term of the explained variable was added to the model explanatory variable. Then, the Hausman test found that the fixed-effects model was better than the random-effects model, so the individual fixed-effects model was used. This paper first built an individual fixed-effects model and judged the regression results of the model without considering the carbon sink as a threshold variable. The model was basically constructed as follows:  
\[\begin{array}{ll}\textit{Incarbonsink}_{it}=&\beta_{1}\textit{invest}_{it}+ \beta_{2}\textit{Ingdp}_{it}+\beta_{3}\textit{Ingdp}_{it}^{2}+\beta_{4}\textit{ Inuvage}_{it}+\beta_{5}\textit{Inworkpop}_{it}+\beta_{6}\textit{Incarbonse}_{it}\\ &+\beta_{7}\textit{Inper}_{it}+\beta_{8}\textit{Intemp}_{it}+u_{i}+\varepsilon _{it}\end{array} \tag{5}\]  
In Formula (5), \(i\) represents the SOFR, and \(t\) represents the year. \(\textit{carbonsink}_{it}\) represents the carbon sink of each SOFR; \(\textit{invest}_{it}\) represents the amount of FMI; \(\textit{gdp}_{it}\) represents the gross domestic product of each SOFR; \(\textit{gdp}_{it}^{2}\) represents the square of the gross domestic product of each SOFR; \(\textit{wage}_{it}\) represents the total wages; \(\textit{workpop}_{it}\) represents the number of employees in each SOFR; \(\textit{lcarbonsink}_{it}\) represents the carbon sink level after a period of lag; \(\textit{per}_{it}\) represents the annual precipitation; \(\textit{temp}_{it}\) represents the average temperature in July; \(\beta_{1}\) to \(\beta_{8}\) are the parameters to be estimated in the model; \(u_{i}\) is the individual effect; and \(\varepsilon_{it}\) is the random disturbance term. If the regression result of the model is unreasonable, the \(\textit{invest}_{it}\) variable was adjusted for the lag period to determine a reasonable lag period.
# Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
## 3 Results
### Measurement Results  
#### 3.2.1 One-Dimensional Point Target Experiment  
In this section, two adjacent corner reflectors and a MIMO radar with two transmitters and four receivers are used to verify the DOA performance of the proposed method. The layout of the radar system and corner reflectors is shown in Figure 7a; the corner reflectors are illustrated in Figure 7b. The radar and corner reflectors are at the same height, and the carrier frequency of the former is 77 GHz.  
Figure 5: DOA result of the proposed method at the arrival of each block of sub–aperture data.  
Figure 6: RMSE and CRB of each method.  
Figure 7: Measurement scenarios. (**a**) Locations of MIMO radar and corner reflectors, (**b**) corner reflector.  
Figure 8a shows the DOA result of the DAS method for which the processing time is 0.0024 s. Due to the short distance between the two corner reflectors, their azimuths are indistinguishable. Figure 8b shows the DOA result of the IAA method for which the processing time is 0.1304 s. Figure 8a,b are consistent with the corresponding simulation results. This method effectively improves the DOA in resolution. The corner reflector is clearly separable, but the side lobes are still not well suppressed.  
Figure 8c shows the DOA result of the LASSO method for which the processing time is 0.2139 s. By taking advantage of the sparseness of the target sources, the LASSO method distinguishes the targets very well, and the side lobe is also quite well suppressed. Compared with traditional methods, the resolution is greatly improved. However, the LASSO method is not applicable to the real-time processing of TDM-MIMO systems. Figure 8d shows the DOA result of the proposed method. This method maintains the same angular resolution as the LASSO method and can update the DOA results of the targets in real time as the MIMO sub-aperture data are received in real time. In addition, the proposed method offers excellent online processing and a super-resolution capability. The average processing time for each sub-aperture data update is 0.0318 s. According to the peaks, the angular estimation errors of the LASSO method and the proposed method are 0.15\({}^{\circ}\) and 0.17\({}^{\circ}\), respectively, after comparing with the calibrated average angle of the targets.
## 3 Results
Figure 2: Planktonic and biofilm monitoring in LB (black squares) and M9 ZB (gray squares): (**A**) planktonic total cells, (**B**) biofilm total cells, (**C**) planktonic culturable cells, (**D**) biofilm culturable cells, and (**E**) biofilm thickness. The means \(\pm\) standard deviations (SDs) for at least three independent experiments are presented. Statistically significant differences to each time point were considered for a confidence level greater than 90% (an asterisk indicates \(p<0.1\)) and 95% (a double asterisk indicates \(p<0.05\)). In general, the PCN (Figure 3E,F) was higher in planktonic cells for both culture media (on average three-fold for M9 ZB and two-fold for LB). In planktonic cells grown in LB (Figure 3E), a strong reduction in PCN (of around 89%) was observed until day 5, and from then on, the PCN levels were kept between 4 and 51 plasmids per cell. On the other hand, in planktonic cells grown in M9 ZB medium, following an initial decrease by 75% until day 6, the PCN increased until day 8 to the values registered at the start of the experiment, and the PCN level was increased until day 9 to the values registered at the start of the experiment. Figure 3: Planktonic and biofilm monitoring in LB (black squares) and M9 ZB (gray squares): (**A**) planktonic eGFP-expression cells, (**B**) biofilm eGFP-expression cells, (**C**) planktonic eGFP production, (**D**) biofilm eGFP production, (**E**) planktonic plasmid copy number, (**F**) biofilm plasmid copy number. The means \(\pm\) standard deviations (SDs) for at least three independent experiments are presented. Statistically significant differences to each time point were considered for a confidence level greater than 90% (an asterisk indicates \(p<0.1\)) and 95% (a double asterisk indicates \(p<0.05\)). decreasing to about 2 plasmids per cell in the next two days. In biofilm cells (Figure 3F), the PCN reached its maximum at days 5 and 7 in LB and M9 ZB, respectively. Then, sessile cells in LB lost about 59% of plasmid content, maintaining values around 29 plasmids per cell until the end of the assay, while in M9 ZB, this reduction was about 75% with cells keeping on average 14 plasmids inside them.
# Algorithm for Option Number Selection in Stochastic Paired Comparison Models
## 4 Simulation Results
### The Cases \(s=3\) and \(s+2=5\)  
Now we compare the three-option and five-option models. The reason for choosing \(s=3\) as the general case is that it is often used, and particularly in sports applications. In numerous sports, the possible outcomes of matches are a draw, a victory, or a defeat. Examples of such sports include football and handball. However, if we want to evaluate the results using a three-option model, we cannot do so. One possible reason for this could be that one team defeats every other team. This happened in the 2024 UEFA European Championship: Spain won all of its matches. If we evaluate these results using the five-option model, the evaluation can be carried out. Many other examples can be given of this phenomenon. These also highlight the importance of examining the problem carefully.  
We conducted computer simulations. In the presented example, the number of compared objects was eight. The expectations were chosen from the interval \([0,2]\) and \(d\) and \(D\) were fixed: \(0<d<D\), \(d=0.1\), \(D=1.35\). In this case, we wanted to show an extreme scenario in which there are almost no 'equal' decisions, while the number of'slightly better' and'much better' decisions is approximately equal. The ratios of the decisions 'equal','slightly better', and'much better' are \(0.0540/0.4768/0.4692\), respectively. The simulation results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(|A|\) & **3 and 5** & **Only 5** & **Neither** & \(\overline{e^{(3)}}\) & \(\overline{e^{(3)}}\) & \(R^{(3),(5)}\) \\ \hline  
\end{tabular}
\end{table}
Table 2: Comparison of the simulation results of the three- and five-option models using the parameters \(m_{i}\): \(0-2,d=0.1,D=1.35\).  
The structure of Table 2 follows the structure of Table 1; the columns' names and reports change according to the option numbers of the models. Here, as in the two-option model, the accuracy of estimating expected values is better for the simpler model if the numbers of comparisons are small, while, for larger numbers of comparisons, the more complex model performs better. As more and more comparisons are made between random objects, the errors become smaller and smaller. Based on lots of further experiments, we can ascertain that, in the case of 8 objects and at least 20 comparisons, the five-option model estimates the expectations \(m_{i}\)\((i=1,2,\ldots,n)\) better, on average; therefore, it is recommended.  
Of course, the number of comparisons depends on the number of objects to be evaluated when the model that allows more options is able to use its additional information. By conducting a large number of simulations with different object numbers, we can conclude that five-option model performs better than the three-option model if \(2\cdot n+4\leq|A|\).
# A Dual-Gas Sensor Using Photoacoustic Spectroscopy Based on a Single Acoustic Resonator
## 2 Materials and Methods  
### Measurement Principle  
In a wavelength modulation photoacoustic spectroscopy (WMPAS) system, the modulation of the injection current in a DFB laser results in the simultaneous modulation of optical power (intensity modulation) and laser frequency (wavelength modulation). The laser wavelength is scanned through the absorption line using a slow current ramp [29]. The laser intensity is modulated using a sine signal at frequency \(f_{mod}\), which is the nth resonant frequency in the PA cell. The modulated laser wavelength and intensity are expressed as follows [29,30]:  
\[v(t)=v_{c}(t)+\Delta v\cdot\cos(\omega t) \tag{1}\]  
\[I_{0}(t)=I_{c}(t)+\Delta I\cdot\cos(\omega t-\Delta\varphi) \tag{2}\]  
where \(v(t)\) is the laser frequency, \(v_{c}(t)\) is the central frequency of modulation, \(\Delta v\) is the frequency modulation amplitude, \(I_{0}(t)\) is the light intensity, \(I_{c}(t)\) is the light intensity at the central frequency, \(\Delta I\) is the intensity modulation amplitude, \(\omega\) is the modulation angle frequency \(\omega=2\pi f_{mod}\) and \(\Delta\varphi\) is the phase difference between the laser intensity and the optical frequency.  
Photoacoustic spectroscopy is based on the absorption of light by the molecules of the target species. The fundamental theory governing absorption spectroscopy is based on the well-known Beer-Lambert law. Upon Fourier expansion of the Beer-Lambert law, the following first harmonic signal of the PA signal is used for determining the target gas concentration, expressed as follows [30]:  
\[S_{1f}(v,v_{0})=M\cdot C\cdot F\cdot I_{0}\cdot\cos(\psi)\times H_{1}(v,v_{0}) \tag{3}\]  
where \(M\) is the microphone sensitivity, \(C\) is the gas concentration, \(F\) is the response constant of the PAS cell, \(\Psi\) is the phase difference between the driving signal and the laser amplitude modulation and \(H_{1}(v,v_{0})\) is the first harmonic magnitude derived from the Fourier cosine series expansion of the gas absorption. The photoacoustic signal is proportional to the excitation optical power [31], and thus, the PA signal using the first harmonic (intensity modulation frequency) signal is higher than that using the second harmonic signal.