esa-sceva/satcom-chunk-collection huggingface dataset:
# Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias
## 4 Impact of the Timing Group Delay Bias
### Single Point Positioning
Figure 9: RMS of 3D positioning error at the B1I/B3I frequency for Scheme 1 (**top**) and Scheme 2 (**bottom**). As pointed out by [PERSON] et al. (2013) and [PERSON] et al. (2014), the unmodeled bias will be absorbed by the other parameters and positioning residuals. The satellite-dependent TGD bias will also affect the clock parameter and post-fit observation residuals in SPP. Figure 11 plots the histogram distribution of the corresponding SPP residuals at MCHL. It is shown that when the TGD bias is corrected, the distribution of the post-fit residuals is more centered at zero and the overall RMS of the residuals decreases from 1.32 and 0.67 m to 1.06 and 0.61 m for B1I/B3I and B1I, respectively. Figure 11: SPP residuals of B1I/B3I (**left**) and B1I (**right**) SPP at station MCHL (Mitchell, 26.36\({}^{\circ}\)S, 148.15\({}^{\circ}\)E). Figure 10: Horizontal and vertical positioning error of B1I/B3I (**left**) and B1I (**right**) SPP at station MCHL (Mitchell, 26.36\({}^{\circ}\)S, 148.15\({}^{\circ}\)E).
## 5 In-Line Application of LIBS in Industrial Production  
The major strengths of the LIBS technique are its robustness and versatility and its capability for rapid and stand-off multi-element detection. This enables LIBS to be used for in-line chemical sensing in industrial production under harsh conditions. Laboratory-based element analytical techniques (e.g., LA-ICP-OES/MS) are more accurate than in-line compatible techniques such as LIBS. However, in-line techniques allow for continuous sampling, the reduction of sampling errors, and the real-time detection of rapid changes in the materials composition. Hence, analytical data obtained by a less accurate but continuously performed in-line measurement may have advantages over more accurate data measured in the laboratory that are less representative. In the following we present some recent examples for in-line, at-line, and in-situ measurements of various materials (alphabetical order).
## 2 Materials and Methods
### Feature Reduction and Main Clustering
#### 2.2.1 Convolutional Autoencoder
The subsequent decoding steps are essentially given by the inverse of the encoding stage and used to reconstruct the original \(20\times 20\) dimensional data arrays representing the input light spectra from their reduced feature vectors. The in- and output data can be compared and the layers of the neural network and, thus, the feature representation can be optimized by minimizing the corresponding mean squared error (MSE) loss function.
# Recent Developments in Low-Power AI Accelerators: A Survey
## 4 Results
### Neuromorphic Accelerators  
Another interesting aspect that can be observed in the gathered data is the popularity of SNNs (Spiking Neural Networks) as an acceleration target. In Table 5, we list all low-power SNN accelerators gathered in this survey. Regarding the popularity of low-power SNN accelerators, we can observe from the table that it has not changed much over the four years, staying mostly the same. Even before 2019, many accelerators were designed for SNN acceleration. An example of this is IBM's TrueNorth from 2015 [16], which accelerates rate-based SNNs with a power consumption of around 70 mW. For more details on neuromorphic computing and hardware, we refer the reader to the surveys by [PERSON] et al. [21] and [PERSON] et al. [20]. With regard to the power consumption, SNN accelerators tend to use much less power than its ANN-based counterparts. Calculating the mean and median power used in the accelerators in Table 5 results in a mean power of 0.9 W and a median power of 0.2 W for SNN accelerators, as compared to 1.7 W mean power and 0.5 W median power of non-SNN low-power accelerators. Looking closer at the acceleration targets in Table 5, two of the accelerators stand out from the rest, i.e., Tianjic [61; 62] and NEBULA [51]. Tianjic [61; 62] is a many-core AI research chip developed at Tsinghua University. It supports inference on SNN, ANN, and SNN-ANN hybrid models, where SNN-ANN hybrid models are models with mixed spiking (SNN) and non-spiking (ANN) layers. ANN, in this instant, refers to a variety of common neural network models (CNN, RNN, MLP), with arbitrary activation functions made possible with a look-up-table. Tianjic is able to accelerate more than one model at a time, due to its many-core architecture and decentralized structure. NEBULA [51], developed at the Pennsylvania State University, is a spin-based, neuromorphic inference accelerator that accelerates SNN, ANN, and SNN-ANN hybrids, similar to Tianjic, barring the simultaneous execution of multiple models and the restriction on using ReLU as the only activation function. NEBULA accelerates one single network partitioned into spiking (SNN) and non-spiking (ANN) layers in the hybrid mode that preserves the low-power benefits of SNNs, with the additional advantage of reduced latency of SNNs over ANNs. NEBULA makes use of an ultra-low power spintronics-based Magnetic Tunnel Junction (MTJ) design for its neuron cores, instead of the traditional CMOS technology, to reduce the required voltage to be in the range of mV, instead of V.  
Although popular in 2019 and 2020, the hybrid models have not gained traction in research in the later years, i.e., no more hybrid SNN-ANN accelerators were published in 2021 and 2022. Below, we go through some notable low-power SNN accelerators from the last four years.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 5 Results and Discussions
### Actual and Maximum Allowed Working Hours  
\[H_{i}=\sum_{t\in T}x_{it}\cdot S_{i}\quad\forall i\in N \tag{3}\]  
The total actual working hours, \(H_{i}\), for each staff member \(i\in N\), is calculated as the sum of the product of a decision variable \(x_{it}\) and a scale factor \(S_{i}\), with overall time slots \(t\in T\). Here, \(x_{it}\) could represent whether staff member \(i\) is working at time slot \(t\), and \(S_{i}\) could be a scale factor, such as the number of hours staff member \(i\) is expected to work.  
Figures 9 and 10 provide a visual representation of staff members' actual working hours, categorized into regular (blue bars) and overtime hours (red bars). The \(x\)-axis enumerates the staff members, while the \(y\)-axis quantifies the working hours. In these visualizations, we compared the staff members' actual and maximum work hours for the  
Figure 8: Patient assignment for the second model procedure.  
Figure 7: Patient assignment for the first model procedure.  
two model procedures. A dual-bar chart shows the outcomes for each staff member. Each pair's blue bar signifies the staff member's actual working hours. The red bars indicate the staff member's maximum work hours. Moreover, this side-by-side comparison presents staff work-hour allocation and compliance. It also reveals possible disparities between actual work commitments and recommended maximum limitations, enabling optimal staff management. These depictions provide complete knowledge of staff use for resource allocation and management. By analyzing the distribution and contrast between regular and overtime hours, these figures are diagnostic tools to identify potential inefficiencies in staff scheduling and workload allocation. Also, the figures elucidate the balance between operational needs and compliance, facilitating targeted interventions where discrepancies are identified.
## 3 Results and Discussion
### Method Validation of the Developed HPLC Analytical Method  
For quality control of CSBPT based on compounds **1-18**, the optimized analysis method was verified by evaluating factors such as system suitability, linearity, LOD, LOQ, and precision (repeatability, and intra- and inter-day precisions). The values of the system suitability factors \(k^{\prime}\), \(\alpha\), \(N\), \(Rs\), and \(Tf\) were calculated to be 3.23-13.80, 1.02-1.18, 247,354.40-1,161,300.13, 3.85-36.85, and 1.01-1.22, respectively (Table S3). The \(r^{2}\) values of compounds **1-18** calculated from the corresponding calibration curves were \(\geq\)0.9996, which confirmed the excellent linearity of the analysis assay. The LOD and LOQ values of compounds **1-18** were 0.07-0.36 \(\mathrm{\SIUnitSymbolMicro mL}\) and 0.21-1.10 \(\mathrm{\SIUnitSymbolMicro mL}\), respectively. Regression equation, \(r^{2}\), LOD, and LOQ data are presented in Table 1. The extraction recovery of compounds **1-18** for evaluation of the accuracy of the developed assay was 97.03-104.04%, and the RSD (%) value was <3.00 (Table 2). The RSD values of repeatability, and intra- and inter-day precisions of compounds **1-18** were 0.01-3.09%, showing good precision results (Table 3). These data confirmed that the developed HPLC assay was suitable for the quality control of CSBPT. Figure 1: HPLC chromatograms of the standard solution (**A**) and test solution for quantification of 70% methanol extract of Cheongsangbangpung-tang (CSBPT) sample (**B**). Geniposide (**1**), coptisine Cl (**2**), _prim-_O-glucosylcimifugin (**3**), berberine Cl (**4**), liquiritin apioside (**5**), liquiritin (**6**), ferulic acid (**7**), narirutin (**8**), 5-O-methylvisammioside (**9**), hesperidin (**10**), arctigenin (**11**), baicalain (**12**), oxypeucedanin hydrate (**13**), wogonoside (**14**), baicalein (**15**), arcitin (**16**), glycyrrhizin (**17**), and pulegone (**18**). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Analyte**} & \multirow{2}{*}{\begin{tabular}{c} **Detection** \\ **(nm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Linear Range** \\ **(\#g/mL)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Regression Equation** \({}^{\mathbf{a}}\) \\ **y = \(ax+b\)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **\(r^{2}\)** \\ \end{tabular} } & \multirow{2}{*}{**LOD (\(\mu\)g/mL)**} & \multirow{2}{*}{**LOQ (\(\mu\)g/mL)**} \\ \hline  
446.27\(x+\) 1845.86 & 1.0000 & 0.07 & 0.22 \\ \hline \hline \end{tabular}  
\({}^{\mathrm{a}}\)\(y\): peak area of compounds; \(x\): concentration (\(\mu\)g/mL) of compounds.  
\end{table} 
Table 1: Linear range, regression equation, \(r^{2}\), limit of detection (LOD), and limit of quantification (LOQ) values for HPLC analysis of compounds **1–18** (\(n=3\)).
## 4 Experimental Evaluation
### Quantitative Analysis
#### 4.4.1 Word Alignment Setting  
The word alignment settings require additional steps to manually align visual streams in word resolution and perform a cross-modal fusion of text and video on the time step of word alignment. In this study, the experimental results of each method in two datasets of MOSI and MOSI are shown in Tables 3 and 4, respectively. Compared with other baselines, the Accuracy and F1-Score of the UA-BFET model are only lower than those of the MSAF model with word alignment settings.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 4 Results
### Analysis of Estimated Drifts in TOC Products
#### 4.3.7 Summary Results
In the case of near-nadir observations, the NRMSE exceeds the threshold mostly for forest sites--and to a lesser extent, for cropland, shrubland and bare soils--in most of the spectral bands, especially in the SWIR band. These results are in line with the previously reported statistical analysis (Figures 12-15), as well as with the simulation study. When observations from all angular conditions are retained, the impact of the drift is not statistically significant for all considered cases. Only over forest sites for the NIR and SWIR bands the NRMSE is close to the threshold value, while for all other classes and spectral bands, the impact of the orbital drift is largely within the statistical dispersion of the data. Yet, in the Blue band over shrubland and bare soils, NRMSE exceeds the threshold. This is mainly due the extremely low reflectance signal for this band and the very low IQR values for these land cover classes (see Figure 15). These large NRMSE values for the Blue band are therefore noisy and cannot be related to the orbital drift, which was demonstrated, both in the simulation and analysis of real data, to be stronger over vegetated sites and for the infrared bands. Finally, for NDVI, no statistically significant orbit-related effect is observed for any land cover class or angular conditions, as already presented and discussed in the previous paragraph (see Figure 16).
## 3 Results  
### ROC and AUC  
To examine how well LDA adapts to the self-acquired dataset and evaluate its performance, we compare three binary classifiers (pair 1--\"Left hand vs. Right hand\", pair 2--\"Right hand vs. Feet\", and pair 3 \"Feet vs. Left hand\") of four methods. Receiver operator character (ROC) curves and area under the curve (AUC) of three LDA models combined with four feature extraction are shown in Figure 5. It is found that the ROC curves of all three classifiers are close to the top left corner and the AUC values are over 0.93. Noticeably, the proposed method follows a similar pattern compared to other methods, and even surpasses the FBCSP (0.99 compared to 0.98 in pair 1), although it is not significant. Additionally, the \"Left hand vs. Right hand\" pair performance is surprisingly higher than the other two (0.99 and 0.98 compared to 0.93 and 0.94). Besides ROC curves and AUC values, the confusion matrix is also an indication of the suitability of the models.  
Figure 5: ROC curve and AUC of three binary classifiers for three pairs, “Left hand vs. Right hand”, “Right hand vs. Feet” and “Feet vs. Left hand” respectively left to right. Four feature extraction algorithms are in different colors.  
To further examine the performance of the four frameworks, heat maps are generated based on a confusion matrix (Figure 6). Actual labels are compared with predicted labels generated by 4 methods with a heat bar indicating the number of trials. We find that the performance of the proposed method surpasses conventional FBCSP in the \"Left hand vs. Right hand\" pair. Furthermore, the 1 st pair classifications (left column) of the four methods are better than those of the 2 nd and 3 rd pair (middle and right column), around 20-30 trials. The accuracy of each pair for each method is computed by dividing the number of correctly classified samples (the darker blue diagonal) by the total within one confusion matrix. The result above is well supported by the ROC curve and AUC in Figure 5. Taken together, we can conclude that these methods are highly effective for the subject. The accuracy and F1-score of both datasets are compiled in the following subsections for a more transparent verification.  
The averaged ROC curves and AUC values are depicted in Figure 7. There are no significant discrepancies between curves as well as the AUC values between methods. However, pair 2 and pair 3 have higher AUC values than pair 1.  
Figure 6: Performance of four feature extraction algorithms for three pairs in terms of confusion matrix of a random subject. Each row depicts the classification of each method and each column depicts each pair.
# Adsorption of Ammonium, Nitrate, and Phosphate on Hydrochars and Biochars
## 2 Materials and Methods
### Adsorption of NH\({}_{4}\)+, NO\({}_{3}\)\({}^{-}\), and PO\({}_{4}\)\({}^{3-}\)
#### 2.3.3 Adsorption Isotherms  
In this experiment, we added the BCs, HCs, and AC (0.5 g) to 250 cm\({}^{3}\) Erlenmeyer flasks containing NH\({}_{4}\)+, NO\({}_{3}\)\({}^{-}\), and PO\({}_{4}\)\({}^{3-}\) solutions. The solutions had varying concentrations of 25, 50, 100, 200, and 500 mg dm\({}^{-3}\), and the pH was kept constant at 7. The suspensions underwent agitation on a shaker operating at 200 rotations per minute and a temperature of 25 \({}^{\circ}\)C for a duration of 24 h. The filtration process involved the use of a paper filter with a pore size of 0.5 micrometres to filter the suspensions. The filtrates were subjected to the measurement of NH\({}_{4}\)+, NO\({}_{3}\)\({}^{-}\), and PO\({}_{4}\)\({}^{3-}\) concentrations.  
The adsorption data of the NH\({}_{4}\)+, NO\({}_{3}\)\({}^{-}\), and PO\({}_{4}\)\({}^{3-}\) on the BCs, HCs, and AC were analysed using the Langmuir and Freundlich isotherm models. The Langmuir model is described in Equation (2) [25]:  
\[\frac{C_{e}}{q_{e}}=\frac{1}{K_{L}q_{m}}+\frac{C_{e}}{q_{m}} \tag{2}\]  
where \(C_{e}\) is the equilibrium concentration (mg\(\cdot\)dm\({}^{-3}\)), and constant \(q_{m}\) (mg\(\cdot\)g\({}^{-1}\)) and \(K_{L}\) are the characteristics of the Langmuir equation (dm\({}^{3}\cdot\)mg\({}^{-1}\)) and can be determined from the linearised form (plots of \(C_{e}\)/\(q_{e}\) vs. \(C_{e}\)). The Freundlich model is expressed according to Equation (3) [26]:  
\[lnq_{e}=lnK_{F}+\frac{1}{n}lnC_{e} \tag{3}\]where \(K_{F}\) is the Freundlich adsorption capacity (mg\(\cdot\)g\({}^{-1}\)), and 1/\(n\) is the Freundlich constant. The above equation can be linearised to calculate the parameters \(K_{F}\) and \(n\) (plots of log \(q_{e}\) vs. log \(C_{e}\)).  
Studies of the thermodynamics and the effect of temperature are presented in Section 3.6. The experiments were conducted as follows: the BCs and HCs were studied at 20, 35, and 50 \({}^{\circ}\)C using initial NH4\({}^{+}\), NO3\({}^{-}\), and PO4\({}^{3-}\) concentrations of 100 mg\(\cdot\)dm\({}^{-3}\) at pH 7. Each sample was filtered through a 0.5 \(\upmu\)m paper filter, and the concentrations of NH4\({}^{+}\), NO3\({}^{-}\), and PO4\({}^{3-}\) were measured.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was carried out with SPSS v.28 (Copyright(r) 2013 IBM SPSS Corp., Armonk, NY, USA). The normality of the sample was checked using the Kolgomorov-Smirnov test. Continuous variables were analyzed using the mean and standard deviation and the frequency and percentages of categorical variables. Subsequently, parametric tests were used to measure the differences between independent samples (ANOVA); the chi-square test was used to analyse the differences in percentages. To study the characteristics of participants associated with frailty status and falls and to stratify according to these variables, we applied Random Forest regression using ensemble learning and describing the confusion matrix and metrics: precision (positive predictive value), recall (sensitivity), F1-score (average of precision and recall) and specifity.  
We chose the Random Forest regression algorithm due to its strengths in handling complex, nonlinear relationships and its robustness against overfitting, which was particularly important given the variety of gait and frailty variables in our study. Additionally, Random Forest helps us gauge the importance of each variable, which is crucial for interpreting our results. Prior to this, we used principal component analysis (PCA) and factor analysis to reduce dimensionality and uncover the data structure, finding that the first two principal components explained a significant amount of variance. We also applied multinomial logistic regression, which identified significant variables but showed lower accuracy compared to Random Forest. The Random Forest model's superior performance in classification accuracy and its ability to manage intricate interactions made it the most appropriate choice.
## 3 Results and Discussion
### CO\({}_{2}\) Emission
Transporting ethanol fuel from the processing plant to Kawaihae port emits 401.93 kg of CO\({}_{2}\) per day. Total daily CO\({}_{2}\) emission resulting from transporting feedstock and ethanol is approximately 48,147.70 kg. This results in a total of 19,371.72 tons of CO\({}_{2}\) emission per year. This emission amount is much less than the PSD significant permitting requirement of 75,000 tons of CO\({}_{2}\) equivalent per year. As a result, it is not considered a major stationary source of pollution. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Content** & **Unit** & **Values** \\ \hline Harvesting operations & & \\ Daily fuel consumption & Gallon/day & 637.54 \\ CO\({}_{2}\) emissions per day & Kg/day & 6429.04 \\ CO\({}_{2}\) emissions per year from harvesting operations & Tons/year & 2586.66 \\ \hline Transporting/hauling operations & & \\ Transporting routes & & \\ Length of the first segment & Miles & 26.69 \\ Length of the second segment & Miles & 1.69 \\ Length of the third segment & Miles & 25.67 \\ Daily feedstock transporting & & \\ Number of trips & Round-trips & 217 \\ CO\({}_{2}\) emissions from the first segment per day & Kg/day & 38,856.36 \\ CO\({}_{2}\) emissions from the second segment & Kg/day & 2460.37 \\ Total daily CO\({}_{2}\) emissions & Kg/day & 41,316.73 \\ Daily ethanol transporting & & \\ Number of trips & Round-trips & 3 \\ CO\({}_{2}\) emissions on the second segment & Kg/day & 13.63 \\ CO\({}_{2}\) emissions on the third segment & Kg/day & 388.30 \\ Total daily CO\({}_{2}\) emissions & Kg/day & 401.93 \\ CO\({}_{2}\) emissions per day from transporting operations & Kg/day & 41,718.66 \\ CO\({}_{2}\) emissions per year from transporting operations & Tons/year & 16,785.22 \\ \hline Total daily CO\({}_{2}\) emission (including harvesting and transporting operations) & Kg/day & 48,147.70 \\ Total yearly CO\({}_{2}\) emission (including harvesting and transporting operations) & Tons/year & 19,371.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Estimation of CO\({}_{2}\) emission.
## 4 Results and Discussion
### Mechanical Specific Energy Analysis  
Figure 13a-c, respectively, show the mechanical specific energy (MSE) and average cutting force across various circumstances, employing circular, axe, and triangular prism cutters as the primary row. It should be noted that the arrangement of the scenario markers follows the same sequence as that in the cutting force legend. Additionally, Figure 13d presents the aggregate values of both the cutting force and MSE for the optimal solution, wherein three different cutters occupy the front row. The data depicted in Figure 13a indicates that the minimum aggregate values of cutting force and MSE arise when the front row is assembled with a cylindrical cutter and the rear row is exclusively composed of axe cutters. Similarly, Figure 13b shows the most effective combination being an axe cutter in the front row teamed with a mix of triangular prism cutters and axe cutters. On the other hand, Figure 13c demonstrates that the triangular prism cutter in the front row combines best with an axe cutter. Reviewing data across all figures, Figure 13d acknowledges the superior choice to be an axe cutter in the front row and a triangular prism cutter in the rear row when compared with the previous three combinations.  
Figure 14a-c delineate the contact stress contours in optimal conditions, corresponding to the front row setup using cylindrical, axe, and triangular prism cutters, respectively. Comprehensive evaluation of these contours reveals the least overall cutter stress with an axe cutter in the front row, followed by moderately increased stress when the triangular prism cutter occupies the front row. The highest cutter stress is observed when the cylindrical cutter is placed in the front row.  
Figure 12: Different cutting forces with triangular prism cutters in the front row.  
The rationale behind these observations lies in the aggressive nature of the axe cutter, which swiftly releases stratigraphic stress upon contacting rock. As a result, the rear row of cutters can fracture the rock with relative ease. However, the cutter experiences the highest stress levels in the rear row, thereby making an axe cutter the optimal choice for the front row to minimize total cutter stress.  
Figure 14: Contact stress contour: (**a**) Circular cutter in the front row; (**b**) Axe cutter in the front row; (**c**) Triangular-shaped cutter in the front row.  
Figure 13: Comparison of mechanical specific energy and mean cutting force. (**a**) The first set of schemes; (**b**) The second set of schemes; (**c**) The third set of schemes; (**d**) Comparison of best schemes.