esa-sceva/satcom-chunk-collection huggingface dataset:
# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 2 Material and Methods
### Outlier Detection and Interpolation  
The EC data were screened for statistical outliers and sensor failures and thereafter interpolated to fill data gaps [27]. Data was excluded under unfavorable weather conditions, such as during rain events, low wind turbulence and wind directions opposite to the sonic sensor direction [28], and time periods with high rH [29]. The internal sonic and IRGA warning flags and automated gain control were used to identify low-quality data. Data were screened for outliers using empirical set limits and the interquartile range (IQR) procedure, where outliers were defined as 1 st/3 rd Quartile \(\pm\) 3.5 * IQR. Then the data were interpolated with an inverse weighted time average procedure [30]. The F\({}_{\rm c}\) was separated for day- and nighttime (F\({}_{\rm c\,day}\), F\({}_{\rm c\,night}\)) and F\({}_{\rm c\,night}\) (i.e., nighttime respiration) was interpolated using the relationship between air temperature and CO\({}_{2}\) fluxes [31], while F\({}_{\rm c\,day}\) was gap filled with daily light response curves [32]. Approximately 31%-55% of the 15-min F\({}_{\rm c}\) data was excluded, and F\({}_{\rm c\,night}\) was more affected than F\({}_{\rm c\,day}\). Approximately 29%-42% of the F\({}_{\rm c}\) data was interpolated. Daily values were calculated as the daily average multiplied by the number of samples per day.  
Daily net ecosystem production (NEP) was calculated as the sum of daily F\({}_{\rm c\,day}\) and F\({}_{\rm c\,night}\). Here the arithmetic sign convention is positive for F\({}_{\rm c}\) from the atmosphere to the surface. The NEP was partitioned into GPP and RE. The GPP was calculated as the difference between NEP and RE. Daily RE was calculated as the sum of F\({}_{\rm c\,night}\) and daytime respiration (Rd). Rd was derived from the non-linearrelationship between incoming photosynthetic active radiation (IPAR) and F\({}_{\text{c day}}\) using light response curves, such as the rectangular hyperbola function [32]:  
\[F_{c\ day}\ =\ \frac{AQY*IPAR*\ P_{g\ max}}{P_{g\ max}+AQY*IPAR}\ -\ Rd \tag{1}\]  
where: F\({}_{\text{c day}}\) = daytime CO\({}_{2}\) flux (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); AQY = apparent quantum yield (\(\mu\)mol CO\({}_{2}\)\(\mu\)mol PAR\({}^{-1}\)), P\({}_{\text{g max}}\) = maximum gross photosynthesis rate (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)); IPAR = photosynthetic active radiation (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); Rd = mean daytime respiration (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)).  
IPAR was assumed to be 45% of incoming shortwave radiation [5] and was converted to photon flux density with 1 W m\({}^{-2}\) = 4.6 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)[33].  
Daily NEP, RE, GPP and Rd values were screened again for outliers and interpolated using the inverse weighted time average procedure. Seasonal values were calculated as seasonal averages multiplied with number of days.
## 5 Conclusions  
Conventional SSI methods suffer from a deficiency in the estimation of spectrum details, which often results in a degradation of the quality of reproduced speech signals. In the SSI system presented herein, research efforts were dedicated not only to estimating the spectral envelope, but also to preserving the harmonic structure, particularly for the voiced regions of speech. To this end, harmonic enhancement was employed. This was different from the previous method with harmonic enhancement in that the coefficients of the harmonic enhancement filter were arranged in a manner that would minimize the distance from the original speech. The whole spectrum was obtained through a DNN, which was trained to minimize the distance. Another unique aspect of the proposed method is the adoption of an objective function that includes both MSE and perceptual distance. The perceptual distance was computed similar to the method used to calculate the PESQ.  
The effectiveness of the proposed method was confirmed in experiments, which generated both objective and subjective results that were superior to those of previous methods. There is room, however, for further improvement. For example, the pitch period used for harmonic enhancement was estimated using an open-loop optimization framework. Since the quality of the reproduced speech is more important than the accuracy of pitch estimation, a closed loop optimization method that iteratively minimizes the perceptual difference between actual speech signals and those that are reproduced would be helpful in improving the quality of the synthesized speech signals. Our future studies will focus on these issues.  
Figure 8: Average MOS scores with error bars for each method. (**Left**): Intelligibility. (**Right**): Naturalness.  
**Funding:** Korea Evaluation Institute of Industrial Technology (KEIT) under Industrial Embedded System Technology Development(R&D) Program 20016341.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** The author is grateful to the members of the bio-signal processing lab at Konkuk University for participating in several experiments.  
**Conflicts of Interest:** The author declares no conflict of interest.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
###### Abstract  
Gallium and indium are crucial metals in various industries, such as the medical and telecommunication industries. They can find applications as pure metals, alloys and alloy admixtures, oxides, organometallic compounds, and compounds with elements such as nitrogen or arsenic. Recovery of these two metals from waste is an important issue for two main reasons. First, gallium and indium are scattered in the Earth's crust and their minerals are too rare to serve as a primary source. Second, e-waste contributes to the rapidly growing problem of Earth littering, as its amount increased significantly in recent years. Therefore, it is essential to develop and implement procedures that will enable the recovery of valuable elements from waste and limit the emission of harmful substances into the environment. This paper discusses technological operations and methods that are currently used or may be used to produce pure gallium and indium or their oxides from waste. The first step was described--waste pretreatment, including disassembly and sorting in several stages. Then, mechanical treatment as well as physical, chemical, and physicochemical separations were discussed. The greatest emphasis was placed on the hydrometallurgical methods of gallium and indium recovery, to be more precise on the extraction and various sorption methods following the leaching stage. Methods of obtaining pure metals or metal oxides and their refining processes were also mentioned.  
gallium; indium; recovery; WEEE; secondary resources; leaching; sorption; extraction; separation; recycling 2024
## 2 Materials and Methods
### Biomechanical and Metabolic Data
Transformations were applied to convert marker data from the global to each joint's local coordinate system, ensuring accurate joint angle calculations. Each trial captured up to 60 consecutive strides to minimize kinematic and kinetic time series interruptions. However, in cases where data gaps were present due to marker occlusion or sensor signal loss, we used interpolation techniques to fill these gaps. Specifically, the cubic spline method was employed, as it has been shown to effectively reduce errors in linear and nonlinear kinematic calculations when managing missing data points during cyclic locomotor activities [18; 19]. Given the focus on nonlinear measures, no filtering was applied to the marker trajectories to preserve the original data characteristics. This approach ensured the calculated variables' integrity and maintained the stride-averaged time series consistency across trials. After the pre-processing of the kinematic and kinetic data, six nonlinear measures (LyE\({}_{\text{R}}\), DFA, ApEn, CD, SpEn, LyE\({}_{\text{W}}\)) were applied to 13 gait variables to quantify the complexity, regularity, and dynamic stability of human gait patterns. Nonlinear measures were calculated using established algorithms with specific parameters. The LyE\({}_{\text{R}}\) and LyE\({}_{\text{W}}\)[15; 20] used [PERSON]'s and [PERSON]'s [16; 17; 18; 19] algorithms (embedding dimension \(m=5\), time delay \(\tau=1\), [16; 17; 18; 19]); DFA calculated a scaling exponent \(\alpha\) over windows of 4 to 60 strides [21; 22; 23; 24; 25; 26]; the ApEn [27; 28; 29; 30] and SpEn [32] used \(m=2\) and the tolerance \(r=0.2\)\(\times\) the data's standard deviation [33]; and the CD estimated the degrees of freedom with embedding dimensions \(m=2\) to \(m=10\)[31]. Analyses were performed using MATLAB R2024b custom codes. These measures were used to understand the relationship between gait dynamics and the metabolic cost during walking, helping to identify how these nonlinear characteristics influence energy expenditure. In particular, the nonlinear value for each measure was calculated for the sagittal plane joint angles (hip, knee, and ankle), velocities (hip, knee, and ankle) and moments (hip, knee, and ankle), the position and velocity of the center of mass in the sagittal plane, and the magnitude of the GRF in the vertical and anterior-posterior directions. We focused on the sagittal plane, as it captured key joint angles, moments, and velocities most relevant to forward progression and the metabolic cost while maintaining computational efficiency, given our sample size.
## 3 Objective Function  
A dimensionless balancing index \(\beta_{i}\) can be used to define the optimization's objective function. This kind of expressions has been previously used in [14, 24], and more recently in [20]. As shown in Equation (32), the first balancing index is defined by the relation of the root mean square (rms) of the reaction of the optimized linkage (\(rms(^{o}Reaction)\)) with respect to the rms value of the reaction of the original linkage (\(rms(Reaction)\)), both considered over a period of time T.  
Two objective indexes will be considered taking in account the reactions of the ShF and ShM. As long as they represent the ratio between the reactions of the original mechanism and the optimized one, the result will present the achieved optimization. The value is in the range (0, 1). The nearest this value is to 0, the best balance is achieved. Otherwise (a value close to 1), the result is almost the same that the one obtained without balancing.  
The first balancing index \(\beta_{SHF}\) is shown in Equation (32).  
\[\beta_{ShF}=\frac{rms(^{o}ShF)}{rms(ShF)} \tag{32}\]  
where the root mean square of the ShF (\(rms(ShF)\)) is given by:  
\[rms(ShF)=\sqrt{\frac{1}{N}\sum_{k=1}^{N}(ShF_{ik}^{2}+ShF_{jk}^{2})} \tag{33}\]  
and the root mean square of the original ShF (\(rms(^{o}ShF)\)) is a constant value obtained from calculating the root mean square of the ShF of the four-bar linkage without any added counterweight.  
\[rms(^{o}ShF)=\sqrt{\frac{1}{N}\sum_{k=1}^{N}(^{o}ShF_{ik}^{2}+^{o}ShF_{jk}^{2})} \tag{34}\]  
By substituting Equations (33) and (34) in Equation (32), the balancing index (\(\beta_{SHF}\)) can be expressed as:  
\[\beta_{ShF}=\sqrt{\frac{\sum_{k=1}^{N}(ShF_{ik}^{2}+ShF_{jk}^{2})}{\sum_{k=1}^ {N}(^{o}ShF_{ik}^{2}+^{o}ShF_{jk}^{2})}} \tag{35}\]  
Similarly, the second objective index can be calculated when the ShM is considered as the reaction. So, \(\beta_{ShM}\) can be written as:  
\[\beta_{ShM}=\sqrt{\frac{\sum_{k=1}^{N}ShM_{k}^{2}}{\sum_{k=1}^{N}{}^{o}ShM_{k} ^{2}}} \tag{36}\]  
where \(ShM\) is the shaking moment of the optimized linkage when using the added counterweights and \({}^{o}ShM\) is a constant value that represents the shaking moment of the unbalanced linkage.  
The optimization objective is to minimize \(\beta_{ShF}\) and \(\beta_{ShM}\) considering boundary limits while ensuring that the coordinates of the centers of mass (\(x_{cn}\) and \(y_{cn}\)) and the thickness (\(t_{cn}\)) of each counterweight are dimensions that can be used in the mechanical context. Therefore, the boundaries for optimization are limited according to:  
\[x_{cn}^{min}\leqslant x_{cn}\leqslant x_{cn}^{max} \tag{37}\]  
\[y_{cn}^{min}\leqslant y_{cn}\leqslant y_{cn}^{max} \tag{38}\]  
\[t_{cn}^{min}\leqslant t_{cn}\leqslant t_{cn}^{max} \tag{39}\]
## 5 Open Issues and Future Work
Additionally, it will be crucial to develop governance frameworks for data ownership and access rights, particularly for sensitive or personal data generated by IoT devices. This approach could help define roles and responsibilities among users, operators, and developers to maintain ethical standards across the platform.
## 3 Robust Clustering with Local Contextual Information (RC_LCI)
### Energy Formulation  
In this subsection, the energy function \(F\) is constructed on the basis of the expression of the noise-removed image, bias field, and real image. A standard K-means algorithm can be used to classify pixels in the entire image domain and, thus, \(F\) is defined by  
\[F(b,J)=\int_{\Omega}\left|I_{w}(x)-b(x)J(x)\right|^{2}dx, \tag{17}\]  
where \(I_{w}(x)\) is the denoising image after using the anisotropic weighting scheme. The expressions of \(b(x)\) and \(J(x)\) described in Equations (15) and (16) can be introduced to minimize this energy function effectively. Then, the energy function can be re-expressed as an equation with respect to three independent variables \(\mathbf{w}=(w_{1},\cdots,w_{N})^{T}\), \(\mathbf{c}=(c_{1},\cdots,c_{N})^{T}\), and \(\mathbf{u}=(u_{1},\cdots,u_{N})^{T}\); so, \(F\) is given by  
\[F(b,J)=F(\mathbf{w},\mathbf{c},\mathbf{u})=\int_{\Omega}\lambda_{i}\left|I_{w }(x)-\mathbf{w}^{T}G(x)\sum_{i=1}^{N}c_{i}u_{i}(x)\right|^{2}dx, \tag{18}\]  
where \(\lambda_{i}\) is the weighting coefficient for the \(i\)-th cluster center. Since exchanging the order of the integrals and summations does not change the equation and \(u_{i}\) is a binary membership function in each \(\Omega_{i}\), the energy equation can be re-presented as  
\[F(\mathbf{w},\mathbf{c},\mathbf{u})=\int_{\Omega}\lambda_{i}\sum_{i=1}^{N} \left|I_{w}(x)-\mathbf{w}^{T}G(x)c_{i}\right|^{2}u_{i}(x)dx. \tag{19}\]
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 2 Literature Review
In this design, all possible IBFD communications are considered except the source-based TNFD. This MAC considered IUI during TNFD communications. A MAC protocol designed in [18] named Interference Free Full Duplex (IFFD) with power control MAC for WLAN, where only the AP is FDN and other nodes are HD capable. Thus, BFD communications are not possible by this MAC. Moreover, this MAC always analyses the transmission power of the transmitter and transmission range between the transmitter and receiver to mitigate IUI, which increases the complexity and computational load. The Full Duplex Multi-channel MAC (FD-MMAC) is proposed to mitigate the hidden terminal problem in the multi-channel for removing the controlling signals [19]. It considers the multi-channel exposed terminal problem. In this MAC protocol, the receiver node continuously transmits beacon packets during the whole period of data transmission. These beacon packets do not contain any kind of user information. Thus, this MAC cannot utilize FD potentiality completely based on user data communications. Moreover, due to the continuous beacon packet transmission by the receiver node during data transmission, this design is not enabled to perform the TNFD communications.
## 4 Result
### The Influence of Cutting Parameters on Cutting Frequency
#### 4.2.3 The Influence of Cutting Depth H on Cutting Frequency  
Taking a series of numerical simulation schemes with \(\
u=35\) mm/s and \(a=20^{\circ}\) as an example, the variation in cutting force with time at different cutting depths is shown in Figure 15. At each cutting depth, the trend of the individual tangential force is similar to that shown in Figure 11. The larger the cutting depth, the larger the amplitude of the tangential force, which is consistent with previous research [16]. Additionally, as the cutting depth increases, the number of oscillations of the cutting force decreases significantly, indicating that the cutting frequency also decreases. This trend is consistent with the phenomenon observed in actual cutting tests [21].  
Subsequently, to study the quantitative effect of cutting depth on cutting frequency, the distribution of cutting frequency with cutting depth at each cutting speed is shown in Figure 16. At the same cutting speed, the cutting frequency decreases nonlinearly with an increase in cutting depth, and the greater the depth, the more slowly the cutting frequency decreases. For example, when the cutting speed is 55 mm/s, as the cutting depth increases from 0.5 mm to 1.0 mm, the cutting frequency decreases from 81.37 Hz to 55.17 Hz, which is a reduction of 26.2 Hz. As the cutting depth increases from 2.5 mm to 3.0 mm, the cutting frequency decreases from 24.22 Hz to 18.73 Hz, a reduction of 5.49 Hz, which is only 20% of the previous decrease. Additionally, with a decrease in cutting speed, the influence of cutting depth on cutting frequency diminishes. For example, when the cutting speed is 15 mm/s, for the same changes in cutting depth as in the above example, the cutting frequency decreases by 8.51 Hz and 1.69 Hz, respectively, which is only about 30% of the change observed when the cutting speed is 55 mm/s.  
Figure 15: The variation in tangential cutting force with time under different cutting depths in the schemes of \(a=20^{\circ}\) and \(\
u=35\) mm/s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(H\) (mm) & **Fitted Equation** & \(R^{2}\) &
\begin{tabular}{c} **The Slope of the** \\ **Fitted Equation \(k\)** \\ \end{tabular} \\ \hline
0.5 & y = 1.53x & 0.99 & 1.53 \\ \hline
1.0 & y = 0.99x & 0.99 & 0.99 \\ \hline
1.5 & y = 0.71x & 0.99 & 0.71 \\ \hline
2.0 & y = 0.50x & 0.96 & 0.50 \\ \hline
2.5 & y = 0.43x & 0.92 & 0.43 \\ \hline
3.0 & y = 0.34x & 0.99 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The fitting relationship between \(f_{c}\) and \(v\) of the different cutting depth in the schemes of \(a=20^{\circ}\) and \(h=1.0\) mm.
## 5 Discussion
### Uncertainties and Future Work  
Overall, the proposed Informer model achieved competitive performance for rice yield prediction across the Indian Indo-Gangetic Plains, which indicated that the Informer model had great potential to predict crop yield. Furthermore, interpreting deep learning models can demonstrate model reliability by evaluating input feature importance and analyzing hidden features. However, our study still has some limitations that need to be improved. First, the current study only considers the climate and satellite factors to predict crop yield due to the scarcity of field management data on the regional scale. Still, other features, such as soil properties, tillage, and fertilization rates, are also closely related to rice yield. Our study could reduce the prediction accuracy due to the lack of considering the above features. We should further consider more related features to improve the predictive performance as more high-quality data related to crop yield become available in the future. In addition, we should consider more high-resolution data, such as data based on drones, and develop the high-resolution-based model in the future, which can make the Informer model have better performance. Second, although we have interpreted the machine learning and deep learning models based on feature importance and attention values, we ignore model uncertainty analysis in this study. The estimation of uncertainty is critical for evaluating the model's confidence in predicting crop yield, which has been suggested by some previous studies [11; 60]. Therefore, we should further analyze the uncertainty of models to quantify the reliability of deep learning models in future studies. Third, we determined the optimal parameters of the Informer model by trying multiple trials and optimizing training loss, which may limit the performance of the Informer model. For future studies, automatedly searching optimal parameters will improve the accuracy of the Informer model in predicting yield. Fourth, this study used districtwise Kharif rice yield data over 16 years because smaller-scale yield statistics were not available, which caused the training sample size to be small. The small training set can make it difficult for deep learning methods to train well. Thus, more detailed statistics on crop yield can improve the performance of deep learning methods. Finally, in multiple testing years, the Informer model has outperformed the other four widely used machine learning models. However, it is difficult for the Informer model to predict crop yield under extreme scenarios, such as heatwaves, floods, and droughts. With the rise in global temperatures, the crop yield will be greatly affected by heat stress in summer, especially in the Indian Indo-Gangetic Plains [37; 61]. To deal with the above issues, combining the Informer model with crop growth models, such as the decision support system for agotechnology transfer (DSSAT) [62], can be considered to improve the predictive ability of the Informer model under environmental stress.
## 2 Materials and Methods
### IAA Production and HPLC-FL Analysis  
First tests were performed on bacteria grown on their specific cultural medium with three different amounts of tryptophan (Trp) (0, 200, 400 \(\upmu\)g mL\({}^{-1}\)), sterilized by filtration, and added after cooling to the specific medium. To grow all the bacteria together, the common medium T4 was used. To test auxins production, bacterial cultures in different combinations (i.e., single, couples, and four together) were grown in T4 medium with and without (control) the addition of Trp (200 \(\upmu\)g mL\({}^{-1}\)). Colony forming units and optical densities of each strain were recorded up to the stationary phase. Dry weight of bacterial biomass was obtained by keeping 10 mL cultures at 80 \({}^{\circ}\)C up to constant weight.  
Supernatants were collected after centrifugation at 5000\(\times\)\(g\) for 30 min, were adjusted to pH 2, and the resultant solution was eluted through a C-18 Sep-Pak cartridge [30]. The cartridge was washed with distilled water and IAA was eluted with methanol containing 10 \(\upmu\)g mL\({}^{-1}\) butylated hydroxytoluene (Sigma, St. Louis, MO, USA). The methanol solution with IAA was evaporated under vacuum and auxin was dissolved in 400 \(\upmu\)L methanol for HPLC analysis according to the method of [PERSON] et al. [30] modified as follows. Reverse-phase HPLC analyses (Pharmacia biotech P-900) were performed using a column C-18 (5 \(\upmu\)m) (4.6 \(\times\) 250 mm, Alltech). The gradient phase was 25 min, with 10-35% (\(v/v\)) acetonitrile containing 1% (\(v/v\)) acetic acid and a flow rate of 1 mL min\({}^{-1}\).  
A fluorescence detector (Jasco FP-1520) was set up with an excitation wavelength of 280 nm and an emission wavelength of 350 nm. The standard stock solutions (purchased from Sigma) were prepared in methanol. Standards were injected as a single solution as well as a mixture of all standards, and corresponding peak areas were recorded. The detection limit was determined by the analysis of samples with known concentrations of analyte and by establishing the minimum level at which the analyte can be reliably detected (LOD = 8 pmol; LOQ = 28 pmol) [31]. The concentration of the compound was determined using external standards. IAA concentration was calculated according to a calibration curve made with the standard (R\({}^{2}\) = 0.999; y = (x \(-\) 0.5804)/0.0893) and results were expressed as nmol mg dry weight\({}^{-1}\).