esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methodology
### Experimental Procedure  
All reagents used in the experiments were analytical grade. VCRR oxidation acid leaching, selective adsorption of vanadium, and precipitation of chromium were integrated for efficient resource recovery and effluent management. A detailed process flowsheet is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Component** & **O** & **Cr** & **Si** & **Na** & **S** & **V** \\ \hline Amount (wt.\%) & 41.44 & 18.80 & 11.30 & 10.93 & 10.64 & 3.11 \\ Component & Ca & Cl & Fe & K & & \\ Amount (wt.\%) & 1.94 & 1.06 & 0.37 & 0.17 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the vanadium–chromium reducing residue (wt.%).  
Figure 1: The XRD pattern of the original residue.  
The oxidation acid leaching of vanadium was performed in a glass beaker with a thermostatic mixing water bath (\(\pm 0.1\)\({}^{\circ}\)C). A predetermined amount of VCRR and deionized water was added to the beaker to produce a homogeneous slurry under constant stirring. The slurry was heated to a predetermined temperature. Next, a predetermined amount of MnO2 and concentration of sulfuric acid (H2 SO4) were added to the beaker. After the required reaction time, the filtrate was separated from the VCRR by vacuum filtration. The concentration of vanadium and chromium in the filtrate was determined by inductively coupled plasma optical emission spectrometry (ICP-3000).  
The leaching efficiencies of vanadium (\(\eta_{\rm V}\)) and chromium (\(\eta_{\rm Cr}\)) were calculated using Equations (1) and (2), as shown below:  
\[\eta_{\rm V}=\frac{\rm V\cdot C_{\rm V}}{\rm m\omega_{\rm V}}\times 100\% \tag{1}\]  
\[\eta_{\rm Cr}=\frac{\rm V\cdot C_{\rm Cr}}{\rm m\omega_{\rm Cr}}\times 100\% \tag{2}\]  
where C\({}_{\rm V}\) and C\({}_{\rm Cr}\) are the concentration of vanadium and chromium in the filtrate, g/L; Vis the volume of the filtrate, L; \(\omega_{\rm V}\) and \(\omega_{\rm Cr}\) are the mass fraction of vanadium and chromium in the vanadium-chromium reducing residue; m is the mass of VCRR used in the leaching experiments, g.  
The selective adsorption of vanadium from the filtrate was achieved by adding an amount of the melamine under the pH of about 1.8 at 90 \({}^{\circ}\)C for 60 min. After reacting for the required time, the adsorption precipitation was obtained by vacuum filtration. Then, the V\({}_{2}\)O\({}_{5}\) was obtained by roasting the adsorption precipitation at 550 \({}^{\circ}\)C for 150 min in a muffle furnace. The filtrate was then neutralized using NaOH to precipitate Cr (III) at pH 8.5 under 90 \({}^{\circ}\)C. After filtration, the precipitate was roasted and washed with distilled water to remove soluble impurities. Cr\({}_{2}\)O\({}_{3}\) was then obtained by drying the refined material at 110 \({}^{\circ}\)C for 2 h [18,20]. The effect of various paraments on adsorption performance of vanadium with melamine had been examined by our previous researches [23,24], thus they were not reported here.  
Figure 2: A flow sheet for the experimental procedure.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 10 Open Challenges with Wi-Fi 6
### Wi-Fi 7
#### 10.2.4 Multi-Access Point Cooperation  
To mitigate interference in overlapping BSSs, Wi-Fi 7 focuses on coordination between BSSs regarding channel access, scheduling, and joint transmission of a single data flow. Multi-access point coordination improves performance in two aspects: OFDMA and SR. Coordination among access points with regard to OFDMA means that different access points may assign the same RU to different stations if these stations do not interfere with each other. Otherwise, they need to assign different RUs to them in order to avoid interference. Coordination among access points with regard to SR means that access points exchange CSI to adjust their transmission power; therefore, they can reuse the spectrum  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FeaturesStandards** & **802.11n** & **802.11 ac** & **802.11 ax** & **802.11 be** \\
**(Wi-Fi 4)** & **(Wi-Fi 5)** & **(Wi-Fi 6)** & **(Wi-Fi 7)** \\ \hline Frequency band (GHz) & 2.4/5 & 5 & **2.4**/5/6 & 2.4/5/6 \\ \hline PHY technology & OFDM & OFDM & OFDM, & OFDMA \\ \hline Channel width (MHz) & 20/40 & 20/40/80/160 & 20/40/80/80/80/160 & 20/40/80/80/80/160 \\  & & & & 160+80/240/160+160/320 \\ \hline Resource Unit size (tones) & Full channel & Full channel & **26, 52, 106, 242, 484** & 26, 52, 106, 242, 484, 996 \\  & bandwidth & bandwidth & **996, 2*996** & 2*996, 3*996 \\ \hline Max data subcarrier modulation & 64-QAM & 256-QAM & **1024-QAM** & 4096-QAM \\ \hline Subcarrier spacing (KHz) & 312.5 & 312.5 & **78.125** & 78.125 \\ \hline Symbol duration (\(\upmu\)s) & 3.2 & 3.2 & **12.8** & 12.8 \\ \hline Guard interval (\(\upmu\)s) & 0.4, 0.8 & 0.4, 0.8 & 0.8, **1.6**, **3.2** & 0.8, 1.6, 3.2 \\ \hline MIMO technology & MIMO & MU MIMO: DL, 4 users & MU MIMO: UL \& DL, 8 users & MU MIMO: UL \& DL, 8 users \\  & & 4 users & **8 users** & 16 users \\  & & & **OFDMA: UL \& DL** & OFDMA: UL \& DL \\ \hline Power saving & PSM & PSM & **TWT** & TWT \\ \hline Coding & BCC (mandatory) & BCC (mandatory) & BCC (mandatory) & BCC (mandatory) \\  & LDPC (optional) & LDPC (optional) & LDPC (mandatory) & LDPC (mandatory) \\ \hline Nominal data rate & 0.6 & 6.93 & **9.6** & 40 \\ (Gb/s) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: PHY and MAC layer specifications for Wi-Fi 4 to Wi-Fi 7.  
more efficiently. This mechanism additionally provides modifications in coordinated beamforming.
## 3 Results  
### Participant Characteristics  
The data from 29 participants were reported and analyzed (male = 15, female = 14). Independent \(t\)-tests revealed that the men exhibited significantly (\(p<0.05\)) greater height, weight, body mass index, lean mass, brachial artery diameter, and ATT than the women. The women, however, exhibited significantly (\(p<0.05\)) greater values of body fat percentage compared to the men (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Resting Variables** & \(\mathbf{r}\) & **Lower-Upper Cl95\%; \(\mathbf{p}\)** \\ \hline
**Total (n = 29)** & & \\ FBF and BABF & \(-0.043\) & \(-0.40\) to \(0.33\); \(0.823\) \\ O\({}_{2}\)Hb and BABF & \(0.217\) & \(-0.16\) to \(0.54\); \(0.259\) \\ HHb and BABF & \(0.187\) & \(-0.19\) to \(0.52\); \(0.332\) \\ HHb and BABF & \(0.221\) & \(-0.16\) to \(0.54\); \(0.249\) \\ HbDiff and BABF & \(0.219\) & \(-0.16\) to \(0.54\); \(0.254\) \\
**Men (n = 15)** & & \\ FBF and BABF & \(0.393\) & \(-0.15\) to \(0.75\); \(0.148\) \\ O\({}_{2}\)Hb and BABF & \(0.191\) & \(-0.36\) to \(0.64\); \(0.495\) \\ HHb and BABF & \(0.303\) & \(-0.25\) to \(0.71\); \(0.273\) \\ Hb and BABF & \(0.269\) & \(-0.28\) to \(0.69\); \(0.332\) \\ HbDiff and BABF & \(0.004\) & \(-0.51\) to \(0.52\); \(0.988\) \\
**Women (n = 14)** & & \\ FBF and BABF & \(-0.196\) & \(-0.66\) to \(0.37\); \(0.501\) \\ O\({}_{2}\)Hb and BABF & \(0.228\) & \(-0.35\) to \(0.68\); \(0.432\) \\ HHb and BABF & \(0.069\) & \(-0.48\) to \(0.58\); \(0.814\) \\ Hb and BABF & \(0.174\) & \(-0.39\) to \(0.65\); \(0.552\) \\ HbDiff and BABF & \(0.303\) & \(-0.27\) to \(0.72\); \(0.292\) \\ \hline FBF, forearm blood flow; BABF, brachial artery blood flow; O\({}_{2}\)Hb, oxygenated hemoglobin; HHb, deoxygenated hemoglobin, HHb, total hemoglobin; HbDiff, hemoglobin difference. Cl95\% = 95% confidence interval. \(p\leq 0.05\) is significant. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relationships between resting brachial artery blood flow and oxygenation parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Resting Variables** & **Baseline 1** & **Baseline 2** & **Baseline 3** & **Baseline 4** & **ICC2,1** & \(\mathbf{p}\) & **SEM** \\ \hline
**Total (n = 29)** & & & & & & \\ FBF & \(1.74\pm 0.65\) & \(1.73\pm 0.66\) & \(1.68\pm 0.62\) & \(1.75\pm 0.59\) & \(0.919\) & \(0.491\) & \(0.367\) \\ O\({}_{2}\)Hb & \(0.10\pm 0.08\) & \(0.10\pm 0.07\) & \(0.11\pm 0.10\) & \(0.09\pm 0.09\) & \(0.758\) & \(0.453\) & \(0.085\) \\ HHb & \(0.06\pm 0.06\) & \(0.06\pm 0.05\) & \(0.06\pm 0.06\) & \(0.05\pm 0.06\) & \(0.877\) & \(0.825\) & \(0.041\) \\ tHb & \(0.16\pm 0.14\) & \(0.15\pm 0.12\) & \(0
## 4 Results
In the case of savings in terms of the amount of material used in semi-finished products without reduced machining allowances, the relationship is reversed because for all leaf dimensions the value of the allowance is the same in a given construction. In each case there is also a slight saving in the use of HDF for cladding. Depending on the construction and size of the leaf, it is about 0.03 to 0.07 m\({}^{3}\)/1000 pieces. Also, the overall material savings of variant 1 are slightly greater than variant 2 because they apply to the longer pieces closer to the edge of the door. Nevertheless, in this variant there are also some savings of the frame material, mainly wood, due to the reduction of their length while maintaining the transverse dimensions. Figure 10: Material savings obtained by reducing the machining allowance in construction 4. In order to verify the hypothesis concerning a significantly lower consumption of raw materials while reducing allowances, a box plot presenting the medians and deviations from them was used. It was found that at the significance level of 0.95, there are no statistically significant differences between the original material wear and the wear covered by modifications for all door widths for most structures, variants, and materials. However, significantly lower material consumption was observed after reducing the allowances for: pine wood variant 1 (construction 1), MDF (construction 3), and pine wood variant 1 (construction 4). Pine wood variant 1 (construction 1) does not deviate from the median, which means that the results are homogeneous across all door widths. It follows that reduced machining allowance in the variant 1 (reduced width of stiles) is most advantageous (Figure 11). Considering the annual production volume of each door construction (Table 5) for the year 2020 allowed us to calculate the overall material savings. At least 43 m\({}^{3}\) of pinewood, 4 m\({}^{3}\) of oak wood, and 10 m\({}^{3}\) of MDF can be saved annually from the reduction of machining allowances in variant 1 alone. For wood, the global savings are even greater. The values calculated according to Equation (1) are 57.3 m\({}^{3}\)/year and 5.3 m\({}^{3}\)/year for pine and oak wood, respectively. In variant 2, particleboard savings are at least 30 m\({}^{3}\) per year. Door leaf production in 2020 has not yet reached the design target of 800 pieces/shift within 600 shifts/year. Therefore, the target production of 480,000-500,000 pieces/year will at least double all material savings resulting from the reduction of machining allowances. The estimated total reduction in wood material consumption may eventually reach 150-200 m\({}^{3}\)/year. The savings shares of different types of wood-based raw materials are shown in Figure 12.  
Figure 11: Box plots of median values of material consumption.
## 5 Change Rule of Displacement, Velocity, and Acceleration
### Density of Soil Layer  
When the density of soil layer changes, the change rules of displacement, velocity, and acceleration at different measuring points of civil air defense construction caused by vehicle vibration are shown in Figure 19. The results of displacement, velocity, and acceleration peak values at mid\(-\)span measuring points are shown in Table 5. The change rules of displacement, velocity, and acceleration peak values at mid\(-\)span measuring points are shown in Figure 20. When the density of soil layer is 1750, 1800, 1850, and 1900 kg\(\cdot\)m\({}^{-3}\), the peak value of roof mid\(-\)span displacement of civil air defense construction is 26.623, 27.3133, 28.0004, and 28.6927 mm, respectively, the peak value of velocity is 5.1417, 5.2883, 5.4267, and 5.57 mm\(\cdot\)s\({}^{-1}\), respectively, and the peak value of acceleration is 31.1389, 31.9167, 32.5278, and 33 mm\(\cdot\)s\({}^{-2}\), respectively. The results show that the peak values of roof mid\(-\)span displacement, velocity, and acceleration increase with the increase in the density of soil layer. With the increase in the density of soil layer by 2.86%, 5.71%, and 8.57%, the corresponding peak value of roof mid\(-\)span displacement increases by 2.59%, 5.17%, and 7.77%, respectively, the peak value of velocity increases by 2.85%, 5.54%,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Thickness of Overburden Layer (m)** & **Displacement (mm)** & **Velocity (mm\(\cdot\)s\({}^{-1}\))** & **Acceleration (mm\(\cdot\)s\({}^{-2}\))** \\ \hline
3 & 12.3189 & 2.4217 & 21.8611 \\
4 & 11.3611 & 2.1867 & 18.4167 \\
5 & 10.1895 & 1.9557 & 16.275 \\
6 & 8.7635 & 1.6773 & 13.9222 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of peak displacement, velocity, and acceleration at the mid\(-\)span measuring point when the thickness of overburden layer changes.  
Figure 17: Peak value variation rule of different measuring points when the thickness of overburden layer changes (**a**) displacement, (**b**) velocity, and (**c**) acceleration.  
Figure 18: The law of mid\(-\)span peak value changing with thickness of overburden layer (**a**) displacement, (**b**) velocity, and (**c**) acceleration.  
and 8.33%, respectively, and the peak value of acceleration increases by 2.50%, 4.46%, and 5.98%, respectively.
## 3 Materials and Methods
### Nutrient Concentration and Composition Prediction
#### 3.3.1 Single-Nutrient Concentration Prediction
}\) ModelEN\({}_{\text{x}}\) = Develop Elastic Net regression using FS\({}_{\text{x}}\) with parameters in Table 5 \(\text{c.}\) ModelSW\({}_{\text{x}}\) = Develop Polynomial regression using FS\({}_{\text{x}}\) with parameters in Table 5 \(\text{d.}\) ModelSW\({}_{\text{x}}\) = Develop Stepwise regression using FS\({}_{\text{x}}\) with parameters in Table 5 \(\text{e.}\) ModelRR\({}_{\text{x}}\) = Develop Ridge regression using FS\({}_{\text{x}}\) with parameters in Table 5 \(\text{f.}\) ModelLS\({}_{\text{x}}\) = Develop Lasso regression using FS\({}_{\text{x}}\) with parameters in Table 5 \(\text{g.}\) ModelLR\({}_{\text{x}}\) = Develop Linear regression using FS\({}_{\text{x}}\) with parameters in Table 5 \(\text{d.}\) End For Output: ModelEN\({}_{\text{Ca}}\), ModelEN\({}_{\text{Mg}}\), ModelEN\({}_{\text{K}}\), ModelEN\({}_{\text{P}}\), ModelEN\({}_{\text{N}}\), ModelPN\({}_{\text{Ca}}\), ModelPN\({}_{\text{Mg}}\), ModelPN\({}_{\text{K}}\), ModelPN\({}_{\text{P}}\), ModelPN\({}_{\text{N}}\), ModelSW\({}_{\text{Ca}}\), ModelSW\({}_{\text{Mg}}\), ModelSW\({}_{\text{K}}\), ModelSW\({}_{\text{P}}\), ModelSW\({}_{\text{N}}\), ModelSW\({}_{\text{Ca}}\), ModelSW\({}_{\text{Mg}}\), ModelSW\({}_{\text{K}}\), ModelSW\({}_{\text{P}}\), ModelSW\({}_{\text{N}}\), ModelRR\({}_{\text{Ca}}\), ModelRR\({}_{\text{Mg}}\), ModelRR\({}_{\text{K}}\), ModelRR\({}_{\text{P}}\), ModelRR\({}_{\text{N}}\), ModelLLS\({}_{\text{Ca}}\), ModelLSMg\({}_{\text{P}}\), ModelLS\({}_{\text{K}}\), ModelLS\({}_{\text{P}}\), ModelLS\({}_{\text{N}}\), ModelLR\({}_{\text{Ca}}\), ModelLR\({}_{\text{Mg}}\), ModelLR\({}_{\text{K}}\), ModelLR\({}_{\text{P}}\), ModelLR\({}_{\text{N}}\). ```  
**Algorithm 1**Single-nutrient concentration prediction  
In relation to Algorithm 1, the process for single-nutrient concentration prediction, outlined in Algorithm 1, involves applying Min-Max normalization to the nutrient concentration dataset and setting an 80% training ratio. For each of the five feature sets (FS1 to FS5) detailed in Table 3, the algorithm loads the respective features and employs six regression models (Elastic Net, Polynomial, Stepwise, Ridge, Lasso, Linear), each with its parameters specified in Table 4. The result is a set of trained models for predicting nutrient concentrations (Ca, Mg, K, P, N) denoted by prefixes such as ModelEN\({}_{\text{Ca}}\), ModelEN\({}_{\text{Mg}}\), and so on. The models are developed using various regression techniques tailored to each feature set, creating a comprehensive framework for nutrient concentration prediction.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Manual Fruit Count
#### 2.3.2 Sampling Approaches
Fruit load estimates for orchard rows were within 10% of the harvest fruit number for 11 of 14 orchards. When a measured attribute that is highly correlated with fruit loading is available, further improvements are obtainable using more sophisticated sampling designs. Ranked Set Sampling (RSS) is one such approach. [PERSON] et al. [26] employed this method with the input of fruit counts of 104 trees using RGB imagery from a UAV, with RS sampling of homogeneous subpopulations derived according to a ranking of the trees using ancillary measurements that correlated with fruit loading, i.e., the tree canopy projected area derived from the images. The estimated average fruit load was 145 fruit/tree with SD of 50 fruit/tree (CV = 36%) based on counts of 104 trees. Treating these 104 trees as the entire population and their contents as the true total fruit load, the variance of fruit load estimates was decreased by about half compared to RS. Sampling errors above the 10% threshold were produced significantly fewer times using RSS, regardless of the sample size, and the sample size could be reduced by 50%, from n = 10 for RS to n = 5 for RSS for the same error.
## 3G Deployment Stage  
Involving a MNO in the consortium could help extend telephony and broadband services to the entire population while generating revenues that would guarantee the economic and technical sustainability of the network in the medium and long term. However, the coverage of cellular services in Peru (and in many low and middle-income countries) does not usually reach rural communities with less than 400 inhabitants. Specifically, in Peru there are still 62,826 population centers without coverage as it is not profitable for operators to offer services in these communities using conventional deployment and business models. These models, exclusively based on satellite communications with high costs for backhauling, are not suitable for small communities offering reduced revenues that do not cover operating costs. However, it is difficult to design a solution without the operators because they have the licenses to operate in the cellular telephone bands, and they also have the knowledge and the necessary infrastructure to manage both the networks and the demand of the customers. For this reason, in 2013, the EHAS Foundation began to work on a new collaboration model that would allow the Napo network to be used to bring 3G telephony services (voice and data) to communities under 400 inhabitants.  
It was a complex challenge that required reducing the costs of rural deployments through new technological solutions and proposing innovative business models that would exploit synergies with public administrations and other rural actors, such as NGOs. an additional difficulty was the scarce information available on traffic demand and the income that could be expected in these communities, making it difficult to design business models. To address these issues, the consortium was expanded and included a Mobile Network Operator (MNO), which was Telefonica, a manufacturer of 3G equipment (IP Access), a university expert in 3G systems (Polytechnic University of Catalonia--UPC), and the Investment Fund in Telecommunications of Peru (FITEL), which depends on the Ministry of Transport and Communications of Peru. This consortium obtained funding from the European Commission to investigate these issues in what was known as the TUCAN3G project [17].
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 6 Effect on Food Quality Characteristics
### Effects on Physico-Chemical Properties of Foods  
The effects of PL processing on various physicochemical properties of food products like viscosity, pH, functional properties have also been studied by several researchers. [PERSON] et al. [75] treated catfish fillets by PL of 2.5-5J/cm\({}^{2}\) fluence and observed no effects on the shear forces. [PERSON] et al. [78] did not observe any effect of PL on firmness of strawberries either singly (160 s treatment) or in combination with UV-C (254 nm, 0.1 J/cm\({}^{2}\) and 120 s PL). [PERSON] et al. [52] observed an increase in milk temperature up to 38 \({}^{\circ}\)C via the continuous flow PL treatment of milk, which led to fouling as well as alterations in milk quality. [PERSON] et al. [110] noticed a slight decrease in viscosity, pH and density of milk after PL application. [PERSON] et al. [66] did not observe any significant change in \({}^{\circ}\)Brix and pH of apple juice after PL treatment. Eggs treated with PL showed no drastic effect on the quality of egg albumin and on the sensory and functional properties [89]. The firmness and pH of the fresh-cut avocado after PL application however decreased; also, textural properties of fresh-cut watermelon and mushrooms [43; 54; 94] were affected. PL also showed no impact on the soluble solids (in terms of \({}^{\circ}\)Brix), pH, and non-enzymatic browning index. However, there was slight effect on the color of apple juice [92]. A slight decrease in pH and \({}^{\circ}\)Brix and a rise in titratable acidity (in terms of g of anhydrous citric acid/100 g tomato) took place in tomato as a result storage of 20 days after PL treatment of 8 J/cm\({}^{2}\)[114]. Slight changes in cell wall integrity and weight decrease were observed in tomatoes after PL treatment of 8 J/cm\({}^{2}\)[115]. The physico-chemical properties are said to be affected in some studies and not in others. However, no conclusive results could be drawn, and it could be said that the effect of PL might be driven by product matrices.