esa-sceva/satcom-chunk-collection huggingface dataset:
# ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network
## 3 Method  
### Linear Quantization  
Linear quantization is a linear mapping of FP32 data to lowbit data (as shown in Figure 1), which can be defined as follows:  
\[\begin{split} q=clip(round(\frac{x}{s})+z,-2^{n-1},2^{n-1}-1)\\ s=(x_{max}-x_{min})/2^{n}\\ z=-round((x_{max}+x_{min})/(2\times s))\end{split} \tag{1}\]  
\(x\) is the floating-point data that needs to be quantized. \(s\) is called the quantization scale and \(z\) is the quantization zero. \(n\) represents the bit width of the low-bit data. \(round(\cdot)\) is a rounding operation, such as rounding up, rounding down and round-to-nearest. \(x_{min}\) and \(x_{max}\) are the minimum and maximum clipping thresholds. Through the \(clip(\cdot)\) truncation operation, the quantized data are limited to \([-2^{n-1},2^{n-1}-1]\). That is, the floating-point data less than \(x_{min}\) will be mapped to \(-2^{n-1}\), while the floating-point data greater than \(x_{max}\) will be mapped to \(2^{n-1}-1\). The low-bit data \(q\) can be mapped back to floating-point data \(x_{q}=(q-z)\times s\), which is also called fake quantization. Usually, \(x\) and \(x_{q}\) are not equal, and the difference between them is called quantization deviation, which can be measured by L1 distance, L2 distance, KL distance, cosine similarity, etc.
## 4 Results and Discussion
### Overall Appearance and Qualitative Observations  
Printed samples exhibited distinct visual and tactile differences, reflecting the inherent characteristics of each technology. FDM builds--whether using PLA or ABS--featured visible layer lines on vertical and curved surfaces, producing a semi-matte appearance (Figure 1a). These layer lines are a direct result of the extrusion process, where molten thermoplastic is deposited in discrete layers. In contrast, SLA parts appeared smoother and more uniform, exhibiting a glossy finish immediately after post-curing (Figure 1b). A 10 mm scale bar is included in each image to provide dimensional reference. Cross-sectional micrographs (not shown here for brevity) confirmed that SLA's layers were more closely bonded than the extruded beads characteristic of FDM, contributing to its superior surface quality.
## 2 Dynamic Modeling and Problem Formulation
### Prescribed Performance Function  
To constrain the transient and steady-state performances of the system error \(e(t)\), define \(e(t)\) satisfies strictly the following inequality [45; 46]:  
\[-lp(t)<e(t)<hp(t) \tag{8}\]  
where \(\rho(t)=(\rho_{0}-\rho_{\infty})e^{-mt}+\rho_{\infty}\) is the selected positive decreasing and bounded prescribed performance function, \(\rho_{0}\), \(\rho_{\infty}\) and \(m\) are positive design parameters, and \(\rho_{\infty}=\lim\limits_{t\to\infty}\rho(t)<\rho_{0}\), \(l\) and \(h\) are positive parameters to be designed.  
To simplify the designing of prescribed performance controller, a strictly monotonic increasing function \(S(z(t))\) is introduced to transform the performance constrained problem into an unconstrained stabilization problem.  
\[S(z(t))=\frac{h\exp(z)-l\exp(-z)}{\exp(z)+\exp(-z)} \tag{9}\]  
Consequently, condition (8) can be rewritten as  
\[e(t)=\rho(t)S(z(t)) \tag{10}\]and the transformed error \(z(t)\) can be derived as  
\[z(t)=S^{-1}\bigg{(}\frac{e(t)}{\rho(t)}\bigg{)}=\frac{1}{2}\ln\frac{e(t)/\rho(t)+l }{h-e(t)/\rho(t)} \tag{11}\]  
The time derivative of \(z(t)\) results in  
\[\dot{z}(t)=r\bigg{(}\dot{e}(t)-\frac{\dot{\rho}(t)e(t)}{\rho(t)}\bigg{)} \tag{12}\]  
where \(r=\frac{1}{2\rho(t)}\Big{(}\frac{1}{z(t)+l}-\frac{1}{z(t)-h}\Big{)}\).  
**Lemma 1**[47]: _For any constant \(\tau>0\) and variable \(B\in R\), there exists equation \(\underset{B\to 0}{lim}\frac{\tanh(B/\tau)}{B}=0\); Let \(\theta\{\epsilon||e|>0.8814\tau\}\), then for \(\forall\epsilon\in\theta\), the following inequality holds_  
\[1-2\text{tanh}^{2}\Big{(}\frac{\epsilon}{\tau}\Big{)}<0 \tag{13}\]
## 4 Methodology
### Analysis Strategies
#### 4.6.1 Scenario #1  
The study aimed to determine whether a TT&C-only solution can meet the current operational requirements of TDRS if all initial conditions are matched with the operational solution. To achieve this goal, TDRS orbits were reconstructed using the same initial conditions, including initial state vectors, a priori TT&C bias and its covariance, and coefficient of solar radiation pressure, Cr, as used in the operational solution. The reconstructed orbits were then compared to the operational solution to investigate any differences (see Section 5.1.1).
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 2 Models Developed for Network Coverage and Capacity Problems
### Wireless Interference Considerations
In practice, usually specific (gNodeB) cells communicate at a different frequency, thus limiting interference at locations that are covered by multiple gNodeB nodes. By reducing the number of customer locations that are covered by more gNodeB nodes, we reduce the potential wireless interferences. This can also be achieved with a modification of the parameters of the gNodeB nodes (transmission power, etc. ); however, it is advisable to consider this problem also in the process of selecting the appropriate positions for deployment.
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 1 Introduction
### Research Question  
In the presence of numerous flood management approaches, measures, criteria, plans, and strategies, it is difficult to choose the most appropriate flood management practice; however, choosing the most efficient and appropriate strategies is the most critical step toward flood management. With the increasing number of new concepts and terminologies, the following main concern arises:  
_How can an effective and efficient flood management practice be developed under the prevailing conditions of a floodplain?_  
The following sub-questions are formulated to build the investigation structure of the main research question:  
1. How can an appropriate measure/scheme of measures for flood management be chosen? 1. What are the basic elements of flood management? 2. What is the role of different measures in flood risk reduction?
2. What is risk-based flood management, and where does it stand when there are so many modern concepts, theories, approaches, and strategies? 1. Among so many understandings and definitions of risk, which one is best, and why should it be considered for flood management? 2. How can different flood management measures and approaches be compared while they have different targets and different outcomes? 3. Is a detailed assessment of all possible combinations of measures required in order to design a more effective scheme for a floodplain?  
The research presented through this manuscript addresses the basic questions that decision-makers have while choosing flood management measures. Furthermore, fundamental principles of flood management have also been identified in order to clarify essential theories, techniques, and solutions for flood prevention. Herein, specific guidelines are discussed for an optimal flood management approach.
## 2 Materials and Methods
### Empirical Safety Stock Estimation
#### 2.2.2 Historical Simulation (HS)  
Historical simulation is used as the most popular and efficient method [38]. The main characteristics of this method are that it is relatively simple to set up, it does not provide any assumption about the form of distribution and its dependence on the availability of data and the sample size.  
While predicting the future implies inherent uncertainty, a point forecast can be very useful to quickly describe the general expected path with less complexity. The first step before dimensioning SS is computing the point forecast. Then, this value is used with the forecast error for the quantile forecasting approaches. The SES method is used in this work to find the point forecasts [36] and is formulated as mentioned in Equation (9). To initialize this method, the value of the smoothing parameter \(\alpha^{\prime}\) and the initial value of Equation (9) is optimized via minimizing the in-sample MSE.  
\[\mathrm{F_{t+h}}=\alpha^{\prime}\mathrm{y_{t}}+\big{(}1-\alpha^{\prime}\big{)} \mathrm{F_{t}} \tag{9}\]  
where  
\(0<\alpha^{\prime}<1\),  
h is the forecasting horizon.  
Note, that the lead time demand forecast \(\mathrm{F_{L}}\) is expressed as mentioned in Equation (10).  
\[\mathrm{F_{L}}=\sum_{h=1}^{L}\mathrm{F_{t+h}}=\mathrm{L}\mathrm{F_{t+1}}. \tag{10}\]  
Then, the empirical quantile method is a simple approach to estimating SS. It is based on the empirical distribution of historical data on the lead time forecast errors as expressed according to Equation (11).  
\[\mathrm{SS}=\mathrm{Q(CSL)}-\mathrm{point\ forecast} \tag{11}\]  
where \(\mathrm{Q(CSL)}\) is the quantile of empirical distribution at CSL.
## 5 Conclusions  
In this study, the stability of the online SPM system was analyzed in detail and the convergence conditions were derived when the relative error of the secondary path was large. The system can still converge stably at the start-up stage of the system operation or when the secondary path changes suddenly. The step-size upper bound of the ANC filter changes with the relative error of the auxiliary path, which can avoid the instability of the system caused by secondary path errors to a great extent. In addition, based on the model, a suitable step size for the ANC filter can be applied for various types of input signals. Finally, the steady-state MSE not having a closed-form expression is explained. Compared with the existing literature methods, the controller of the system is fitted online in all operating stages, without the need for offline fitting for a period of time, and when the mutation of the secondary path causes the relative error of the fitting filter to be large, the system can still have good stability. The simulation results verify the effectiveness of the model, ensure the stability of the system under different conditions, and have stronger robustness. The experimental results show that the ANC system had a noticeable noise reduction effect for different types of noise signals, and the noise reduction amount for more complex narrowband noise signals can reach 15 dB. The system can establish an effective quiet space in the local space, with an average noise reduction of approximately  
Figure 10: Spatial sound pressure distribution before and after noise reduction. (**a**) ANC off; (**b**) ANC on; (**c**) noise reduction amount; (**d**) mean value distribution of sound pressure on the same horizontal plane.  
15 dB and a maximum of 20 dB. These results have practical significance for other complex online secondary path modeling and local space noise reduction.  
Conceptualization, methodology, Y.L., Z.L., and W.Z.; software, validation, investigation, Y.L., Z.L., and Q.X.; writing--original draft preparation, Y.L.; resources, writing--review and editing, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the national natural science foundation of China, grant number 61573073.  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors would like to thank [PERSON] for his valuable advice and the relevant data on this research.  
The authors declare no conflict of interest.
## 3 Results
### Effect of Carum carvi on In Vivo Antioxidant and Hepatoprotective Activities  
2.1 Effect of _Carum carvi_ on Liver Glutathione (Reduced) Levels, Lipid Peroxidation, and Cytochrome P450  
From the data (Table 2), it can be observed that acetaminophen treatment resulted in elevated levels of CYP 450 content as compared with normal control rats. However, _Carum carvi_ (D1: \(p\) < 0.01; D2: \(p\) < 0.001) treatment restored the levels of cytochrome P450 levels in the liver of rats. In comparison with control group animals, MDA adduct generation was significantly increased by acetaminophen treatment (14.03 \(\pm\) 1.15, \(p\) < 0.001). Both doses of _Carum carvi_ (D1: \(p\) < 0.01; D2: \(p\) < 0.001) were found to be significantly effective in lowering lipid peroxidation induced by acetaminophen (Table 2). GSH levels presented a significant decline (\(p\) < 0.001) in acetaminophen group (0.031 \(\pm\) 0.004) in comparison with control group animals (0.056 \(\pm\) 0.01). _Carum. carvi_ treatment restored the acetaminophen-induced depletion in GSH levels (D1: 0.046 \(\pm\) 0.004, \(p\) < 0.01; D2: 0.052 \(\pm\) 0.005, \(p\) < 0.001, (Table 3). Animals in Group V, i.e., only _Carum carvi_, did not show any noteworthy variation in any of the parameters in comparison to the control rats. Figure 3: Ferric reducing antioxidant potential of _Carum carvi_. Representative graph showing concentration-dependent ferric reducing potential of _Carum carvi_. All samples were analyzed in triplicate. 2.2 Effect of _Carum carvi_ on Intracellular Antioxidant and Phase II Drug-Metabolizing Enzymes such as Quinone Reductase (QR), Glutathione Reductase (GR), and Glutathione S-Transferase (GST) Activity  
Increased resistance against acetaminophen-induced hepatotoxicity can be attributed to phase II enzymes such as QR and GST. Our results showed (Table 2) that administration of _Carum carvi_ at D1 and D2 doses to rats led to a substantial increase (\(p<0.001\)) in QR activity in comparison with rats treated with acetaminophen. Hepatic tissues from acetaminophen treatment groups exhibited a substantial elevation (\(p<0.001\)) in GR activity (Table 3) as compared with control rats. Both doses of _Carum carvi_ treatment resulted in significant restoration (D1: \(p<0.01\); D2: \(p<0.001\)) of GR activity in rat liver tissues. A significant decrease (\(p<0.001\)) in GST activity was observed in acetaminophen-treated rats in comparison to the control animals. A substantial elevation in activity of GSH was found in animals with _Carum carvi_ treatment (D1: \(p<0.01\); D2: \(p<0.001\)) in contrast to animals treated with acetaminophen (see Table 3).
## 2 Materials and Methods
### Statistical Analysis  
Quantitative continuous variables are expressed as mean \(\pm\) standard deviation or median and range, whereas categorical values are reported as absolute counts and percentages. The interobserver reliability for MRI was calculated using the Cohen kappa coefficient. Kappa values of 0.01-0.20, 0.21-0.40, 0.41-0.60, 0.61-0.80, 0.81-0.99, and 1 represented slight, fair, moderate, substantial, almost perfect, and perfect agreement, respectively. Data were presented as a percentage or mean (\(\pm\)standard deviation) and median (interquartile range). Continuous variables were tested for normality using the Kolmogorov-Smirnov test. The association of each parameter and the diseased status at the follow-up (i.e., tumor persistence/recurrence or post-CRT changes) was tested using the Student's t-test or Mann-Whitney U-test for independent samples, as appropriate. For the parameters with statistically significant association with the diseased status at follow-up, a cut-off value to discriminate post-CRT changes with respect to tumor persistence/recurrence was calculated using receiver operating characteristic (ROC) curve analysis. In particular, sensitivity and specificity were calculated for the entire spectrum of values, and cut-offs were chosen as the values with the highest combination/multiplication of sensitivity and specificity. The area under the ROC curve was considered as a measure of the overall performance of each parameter (diagnostic accuracy) to discriminate the diseased status at follow-up. The analyses were performed using the SPSS\({}^{\text{\text{\text
## 4 Results and Discussion
### Computational Cost Analysis  
As part of our study, we examined the computational cost of each classifier on the ARAS multi-resident activity dataset. We conducted training and testing to determine the amount of time each classifier required to complete both tasks. Figure 6 displays the time taken by each classifier to finish training and testing. The chart indicates that the LSTM took the longest time, followed by the GRU, to accomplish the training and validation necessary to achieve the same level of performance as the other classifiers. In contrast, the GNB classifier required the least amount of time to complete the training and testing process.  
The above discussion shows that the GRU-based model identified anomalies more precisely than the other classifiers, although it had the second-highest computational cost. The reason behind this is that the human activity data are temporal, and one activity is dependent on the other activities that happened in the past. So, the information from past  
Figure 5: The figures presented here demonstrate the relative performance of various models on the House B data from the ARAS dataset. The sub-figures (**a-f**) display the accuracy, precision, recall, F-I, macro F-1, and weighted F-1 scores, respectively, obtained by applying ten machine learning and deep learning models at different splitting ratios for training and testing sets. These ratios include 80:20, 70:30, and 60:40 for training and testing.  
Figure 6: Comparative analysis of the run time of different classifiers from training to testing. The run time is computed in seconds.  
activities is needed to decide the present activities. The working approach of the GRU model is consistent with the requirement of accessing the information from the past during training and testing. That is why the GRU-based model outperformed the other models in terms of the overall accuracy to identify anomalies in human activities.
## 4 Conclusions  
In summary, substantial information, including AES/XPS spectra, element, and chemical state surface distribution and 3D profiling, could be gathered for trichlorosilane (1H,1H,2H,2H-perfluorodecyltrichlorosilane, FDTS) SAM patterns when Auger electron microscopy (AES) and X-ray photoelectron spectroscopy (XPS) characterization were applied to investigate this material collectively. AES is a highly surface-sensitive characterization technique with superior spatial resolution, which provided elemental information on the FDTS SAM film pattern. For XPS with mapping capability, a smaller X-ray beam of 10 \(\upmu\)m should be selected, allowing substantial chemical state information on FDTS SAM film patterns to be collected with decent spatial resolution. Precise two-dimensional (2D) FDTS SAM film patterns were reconstructed through corresponding elements and chemical state feature peaks by AES and XPS. Additionally, three-dimensional (3D) FDTS SAM film patterns were reconstructed layer by layer through gas cluster ion beam (GCIB) etching and mapping. For the FDTS SAM film patterns fabricated by semiconductor fabrication techniques, these characterization results demonstrated that FDTS SAM film patterns based on the vapor deposition method are highly uniform because the vacuum and precise gas-delivery system efficiently suppresses the environmental effects and guarantees process repeatability. The SAM-based microstructures produced by the vapor-based method served as excellent samples to evaluate the analytical capabilities of the AES and XPS techniques for measuring a film's thickness in a few nanometers, as well as the material profile of the film. Eventually, the AES and XPS techniques could be used for metrology in the semiconductor process for high-quality SAM microstructures and nanostructures.  
Conceptualization, G.J. and S.L.; methodology, S.L.; software, H.Z.; validation, S.L., H.Z. and Z.L.; formal analysis, S.L.; investigation, G.J., S.L.; resources, J.X.; data curation, G.F.; visualization, W.L., Q.L.; supervision, X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2021 YFF0600904).  
Not applicable.  
We also would like to thank [PERSON] and [PERSON] at CoreTech Integrated Limited for their advice and technical assistance with the AES and XPS experiments.  
The authors declare no conflict of interest.  
Figure 9: (**a**) Etching curve of four chemical states (C 1s, F 1s, O 1s, and Si 2p) using oxygen gas cluster ion beam (GCIB). (**b**) XPS 3D profiling with a gas cluster ion beam (GCIB), alternating with XPS detection (one layer etching, one layer XPS).
## 2 Methods
### POD Strategy of BDS-3 Satellite Based on Several Dual-Frequency Measurement Combinations  
Important aspects and detailed description regarding GFZ solution, CODE solution, B1 CB2a, B1 CB2b and B1 IB3I combinations in this contribution are summarized in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **GFZ Solution** & **CODE Solution** & **B1 CB2a, B1 CB2b and B1 IB3I Combinations** \\ \hline Platform & EPOS & \begin{tabular}{c} Bernese GNSS Software \\ version 5.3 \\ \end{tabular} & \begin{tabular}{c} Modified Bernese GNSS Software \\ version 5.2 \\ \end{tabular} \\ \hline Stations used for satellite POD & 160 stations & \begin{tabular}{c} 140 stations, with 90 stations \\ supporting BDS-3 \\ \end{tabular} & \
\begin{tabular}{c} 129 stations, all supporting BDS-3 \\ \end{tabular} \\ \hline Signals used for BDS-3 POD & B1 IB3I & B1 IB3I & B1 CB2a, B1 CB2b and B1 IB3I \\ \hline Data differencing strategy & Undifferenced & Double differenced & Double differenced \\ \hline Data coverage & 24 h arc & 72 h arc & 72 h arc \\ \hline Sampling rate & 300 s & 180 s & 180 s \\ \hline Elevation angle cut-off & 7\({}^{\circ}\) & 3\({}^{\circ}\) & 3\({}^{\circ}\) \\ \hline Weighting & Elevation-dependent weighting & Elevation-dependent weighting &
\begin{tabular}{c} Elevation-dependent weighting, \\ and 6 mm threshold value for \\ carrier phase residuals \\ \end{tabular} \\ \hline A prior orbits & Rapid products & Rapid products & Based on broadcast ephemeris \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BDS-3 satellite POD strategy and important aspects for GFZ solution, CODE solution and B1 CB2a, B1 CB2b and B1 IB3I combinations. The Bernese GNSS Software version 5.2, developed by CODE, has been updated to support BDS-3 POD in this contribution. Similar to Galileo MEOs which hold a rectangular shape, ECOM is not capable of appropriately modeling the solar radiation pressure for BDS-3 satellites. Hence, the same as CODE AC, ECOM-2 is used in this contribution. Five signals of BDS-3 have been chosen, and three types of dual-frequency measurement combinations, i.e., B1 CB2a, B1 CB2b and B1 IB3I are employed in BDS-3 POD processing. It should be emphasized that, except for the different signals used, other strategies are the same for BDS-3 POD based on B1 CB2a, B1 CB2b and B1 IB3I combinations in this contribution. The BDS-3 POD procedure based on B1 CB2a, B1 CB2b and B1 IB3I combinations are summarized in Figure 2. For GFZ and CODE final orbits, only B1 IB3I combination is used, considering the compatibility with BDS-2.