esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Timepix3 Radiation Monitor
### Cluster-Level Calibration  
Ideally, the calibration curve of the Timepix chip would be obtained by depositing a known amount of energy in a single pixel and measuring the corresponding ToT response. However, as illustrated in Figure 5 for the case of alphas, single particle hits on the sensor lead to the formation of multi-pixel clusters, spreading the deposited energy over several adjacent pixels. This is particularly relevant when the deposited energy is of the order of hundreds of keV or more (i.e., when performing the calibration for relatively high energies), as the number of pixels per clusters increases with the deposited energy.  
Figure 6: Illustration of the pixel-level spectral response of the Timepix, with regions of distinct dependency [24]. The maximum ToT response is limited by the 10-bit registry at 1024 ADC units (here, at 25 ns), while the lower detection limit is operationally set (here, at \(E_{thres}=5.3\) keV).  
In the present paper, the calibration of the Timepix3 Radiation Monitor is performed using clusters formed by protons and alphas with known energy that are stopping within the sensors, thereby depositing all their energy within the depleted region, with a fraction lost in the passive layers in front. To perform the pixel-level calibration from multi-pixel clusters, one has to work under the assumption that all pixels are operating within the same linear response (i.e., Region B), by summing the ToTs for all the \(N\) pixels:  
\[\textit{ToT}_{cl}=\sum_{i}^{N}\textit{ToT}_{i}=\sum_{i}^{N}f(E_{i})=\sum_{i}^{ N}(a\cdot E_{dep,i}+b)=a\cdot\sum_{i}^{N}E_{dep,i}+\sum_{i}^{N}b=a\cdot E_{ dep}+N\cdot b \tag{1}\]  
where \(\textit{ToT}_{i}\) (\(E_{i}\)) is the per pixel ToT (energy) value, \(a\) and \(b\) are the linear coefficients parametrising the ToT vs. energy curve in Region B, \(E_{dep}\) is the total energy deposited (the known beam energy minus the energy lost in the front layers), and \(N\) is the number of pixels per cluster (which increases with \(E_{dep}\)). The assumption of having the same linear response for all pixels is tested in Section 5.3, especially for high-energy depositions, for which saturation effects can occur. In fact, the choice of operating the Timepix3 Radiation Monitor with partial bias is intended to avoid concentrating the energy deposition in the centre of the clusters by enhancing the charge sharing, thus ensuring that the conditions of Region B can be met in most (if not all) pixels in the cluster. If these conditions are not met, deviations of the measured calibration curve from Equation (1) can be expected, especially for a high \(E_{dep}\) per pixel. Finally, once the calibration curve parameters are established, the Timepix3 Radiation Monitor can be used to obtain energy deposition measurements simply by inverting the expression in Equation (1).
# Time Series Methods and Business Intelligent Tools for Budget Planning--Case Study
## 2 Methods of Quantitative and Value Strategic Planning of Budget
### Forecasting Methods
#### 2.1.4 Arima  
ARIMA (Autoregressive Integrated Moving Average) is a time series analysis technique for forecasting future values on the basis of historical data.  
This method is effective when the time series tend to trend or vary seasonally. ARIMA has three main components:  
* Autoregression (AR): models the relationship between current and past series values, where the value is determined as a linear combination of past observations and random errors.
* Differentiation (I): transforms the time series into a stationary form by calculating the differences between successive values, thus eliminating the trend and the seasonality.
* Moving Average (MA): models the relationship between the current value and errors in previous times, removing the influence of these errors on the predictions.  
ARIMA models are defined by three parameters: \(p\) (lag number in AR models), \(d\) (differentials) and \(q\) (lag number in MA models). The appropriate values for these parameters are selected by analyzing graphs [33].  
The article [34] examines the use of ARIMA models to forecast the spread of the COVID-19 pandemic. The study shows how ARIMA models, despite their limitations in dealing with complex and dynamic scenarios, can be effective in forecasting the short-term evolution of the pandemic, which is of crucial importance for public health strategies. Although the results show high prediction accuracy in the short run, prediction errors are found to increase with the length of the prediction horizon. The article highlights challenges related to COVID-19 data quality in Brazil but shows the potential of ARIMA models for short-term forecasting, suggesting the need for further research to improve long-term forecasting.  
The article [35] analyzes the application of the ARIMA model in forecasting Gross Domestic Product (GDP). A detailed review of the literature is carried out, which shows that two models--ARIMA (2, 2, 2) and ARIMA (3, 1, 1)--are commonly used in the studyof treasury bills. In addition, the use of ARCH and GARCH models in GDP analysis is proposed. The expenditure approach is recommended as a method of measuring GDP.  
The article [36] examines the frequency of fires in China. Their regularity allows the SARIMA model to be used to predict when fires start to burn. The results presented in the article demonstrate the effectiveness of the method used.  
In summary, the above examples of the application of forecasting methods show their usefulness in various spheres of life and the economy. The results of these methods can be extremely useful in creating budgets, whether for individual households, businesses, or other large organizations.
## 4 Results
### Acquisition and Tracking of GNSS Signals in Real Time  
The objective of the acquisition and tracking tests was to confirm the receiver's capability to process GNSS signals and derive navigation solutions in real-time. The receiver underwent testing using the test setup described in Section 4.1. We assessed the performance of the receiver using signals received at nominal power and using weak signals with a \(C/N_{0}\) down to 20 dB-Hz. The OCXO was used for these tests. The test results presented in this section were obtained using the GESTATI testbed rooftop GNSS antenna facility [46]. Using the Tallysman TW8825 active antenna and a variable attenuator in outdoor conditions could yield similar results. Each test was conducted over a period of 10 min.  
The test procedure is outlined in Table 14. Steps 1 and 2 verify the receiver's ability to process Galileo E1b/c signals in high-sensitivity mode concurrently with Galileo E5a and GPS signals in normal-sensitivity mode. Steps 3 and 4 confirm the receiver's ability to process Galileo E1b/c signals at a \(C/N_{0}\) down to 20 dB-Hz. The OCXO was used for these tests.
## 2 Model of Beam-Column Subassemblage
### Evaluation of Concrete Axial Shortening \(\Delta_{c,ex}\)  
In Equations (3) and (4), the concrete axial shortening \(\Delta_{c,ex}\) is a crucial value for accurate deformation evaluation, which has not been considered in previous studies [17; 55]. \(\Delta_{c,ex}\) is then evaluated based on the force equilibrium condition between the tendons and the concrete. Because \(T_{t,t}\) and \(T_{t,c}\) are constant along the debonded tendon length (\(l^{\prime}\)), \(C_{c}\) should be equal at arbitrary beam section in the PC zone. By considering the linear distribution of bending moment along the beam, the constant compressive resultant force of concrete and its linear stress-strain models given in Section 2.1, the concrete strain distribution along the PC zone can be expressed as shown in Figure 7. Accordingly, \(\Delta_{c,ex}\) can be calculated by Equation (7) as the integral of the concrete strains (\(\epsilon_{nx}\)) at the extreme compression fiber. The detail derivation of Equation (7) can be found in Appendix A. Based on \(\Delta_{c,ex}\), \(\epsilon_{t,c}\) and \(\epsilon_{t,t}\) obtained from the analytical model, evaluation methods for the beam strength and deformation at the ultimate flexural limit state are proposed in the next chapter.  
\[\Delta_{c,ex}=\epsilon_{n}l\frac{x_{n}}{3(d_{t}-\beta d)-x_{n}}\ln\left|\frac {3(d_{t}-\beta d)-x_{n}}{x_{n}}\frac{l^{\prime}}{l}+1\right| \tag{7}\]  
where \(l\) is the distance from the beam-column interface to the inflection point, \(d\) is the distance between the tendon positions on the compressive and tensile sides, and \(\beta\) is a coefficient defined by Equation (8).  
\[d_{t,t}=\beta d=dT_{t,c}/(T_{t,c}+T_{t,t}) \tag{8}\]  
where \(d_{t,t}\) is the distance from the tendon position on the tensile side to the position of tensile resultant force of tendons. The coefficient \(\beta\) makes it possible to consider the difference between the tendon forces. As the tendon force on the tensile side (\(T_{t,t}\)) becomes larger relative to that on the compressive side (\(T_{t,c}\)), the value of \(\beta\) decreases and the position of tendon forces shifts to the tensile side. \(\beta\) equals 0.5 when the tendons have same tensile forces.
###### Abstract  
Alternative processes have been proposed for selective separation of fluorite and gangue minerals (carbonates and silicates) present in fluorspar ores. Calcination and gravity separation processes are methods that have low efficiency and high cost. Flotation is a chemical process that becomes important when high ore grades are required; however, the selectivity is inhibited by the superficial similarity of the chemical composition of minerals. Accordingly, interactions between dissolved ionic species of fluorite, carbonates, and silicates with some reagents under determinate conditions obstruct the flotation process. In order to optimize the flotation process of a Spanish fluorite ore, this research uses a mathematical model. In this study, the variables were the dose of potato starch, quebracho tree, white dextrine, oleic acid, and sodium silicate. On the other hand, the factors studied were the law of carbonates, silica, and fluorite, in addition to the metallurgical recovery of fluorite. The statistical technique of factor analysis that relates the variables and factors allowed to the optimization of the reagent dosage. Maximum metallurgical recovery was achieved without sacrificing the fluorite grade. The mathematical model adjusts satisfactorily to the results with a correlation coefficient of 91.58% for metallurgical recovery and 98.51% for fluorite grade. Optimizing the process 60.45% of metallurgical recovery and 68.99% of fluorite grade are achieve in the roughing step, using a dosage of 1.68 g\(\cdot\)kg\({}^{-1}\) of potato starch, 0.86 g\(\cdot\)kg\({}^{-1}\) of quebracho tree, 1.25 g\(\cdot\)kg\({}^{-1}\) of dextrin, 3 g\(\cdot\)kg\({}^{-1}\) of oleic acid, and 0.85 g\(\cdot\)kg\({}^{-1}\) of water glass.  
DOE; flotation; fluorite; metallurgical recovery; optimization; slimes
# Neighbor Session Solutions for Integrated Routing Protocols
## 2 Dual-Stack IP Routing Protocols
### Open Shortest Path First  
Open Shortest Path First (OSPF) represents link state routing protocols. The OSPFv2 standard, which is still in use today, was released in 1998 and supports only the IPv4 protocol, while the message headers are directly embedded in the IPv4 protocol.  
Because OSPF was not designed to support multiple network protocols, the OSPF architecture does not allow for easy expansion. Therefore, support for IPv6 networks was added until the new version of the OSPFv3 protocol was released. It uses the same principle of creating adjacencies, exchanging topological information, and calculating shortest paths as its predecessor, but it also uses other types of LSA structures, and some are called differently. To transmit information, it uses the IPv6 protocol, into which it directly inserts the headers of its packets.  
The original OSPFv3 standard only supported the IPv6 protocol, but in 2010, RFC 5838 was released, which added support for IPv4 routing information to OSPFv3. This has made it possible to carry IPv4 and IPv6 routing information within a single process, but adjacency must be established separately for each protocol. In both cases, transport is ensured using the IPv6 protocol. At the same time, packets belonging to individual neighborships are distinguished in the OSPF packet header by a field called Instance ID [36; 37].
###### Abstract
This deviation can be caused by various factors, including data collection, data manipulation, surgical guide fabrication, and inaccurate surgical guide position as well as errors in the drilling [13; 14]. For instance, the data collection of guided surgery may have an error inaccuracy from the superimposition method. The post-surgical CBCT, if not superimposed properly, can lead to measurement error. These data are often further analyzed statistically in different plans and can be subjected to multiple comparisons. Different guide fabrication methods, 3D printing or milling, along with guide positioning intramurally and experience of surgeons in the placement of drills, can also affect the accuracy of implant placement. These factors may affect the angulation, depth, and diameter of the implant drilling, or the placement of an implant that converts into the 3D deviation of the implant position [15; 16]. However, there have been limited studies that show the impact of static surgical guides fabricated with different 3D printing technologies explicitly according to the accuracy of the implant position. Clinicians currently have no idea if these printers would produce the same accuracy for their implant guides and often choose a printer without knowing the accuracy of printed products. Thus, the study aimed to evaluate the deviation of implant position among the SLA, DLP, and Polyjet 3D printer as well as the total processing time of these 3D printers. The outcome would assist in 3D printer selection among these common printing technologies. The null hypothesis was that there were no differences in the accuracy of implant position among the static surgical guides produced by SLA, DLP, and Polyjet.
# A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts
## 3 The Multiple Criteria Decision Analysis Framework
### Recommending Alternatives to Reject and as Solution
#### 3.3.1 Recommending based on the Alternatives/Criteria Predicted Importance
: _Let \(CM_{dm_{k}}\) be the consistency matrix of the decision-maker \(dm_{k}\), where (\(F_{cc}\) stands for \(F_{CriterionConsistency}\)):_  
\[\bullet\quad CM_{dm_{k}}=\left[ \begin{array}{cccc}F_{CC}(dm_{k},c_{1},a_{1 })&F_{CC}(dm_{k},c_{2},a_{1})&\cdots&F_{CC}\Big{(}dm_{k},c_{p},a_{1}\Big{)}\\ F_{CC}(dm_{k},c_{1},a_{2})&F_{CC}(dm_{k},c_{2},a_{2})&\cdots&F_{CC}\Big{(}dm_{ k},c_{p},a_{2}\Big{)}\\ \vdots&\vdots&\ddots&\vdots\\ F_{CC}(dm_{k},c_{1},a_{m})&F_{CC}(dm_{k},c_{2},a_{m})&\cdots&F_{CC}\Big{(}dm_{ k},c_{p},a_{m}\Big{)}\end{array} \right]\]  
**Definition 22**. : _Let \(F_{AlternativeConsistency}(dm_{k},a_{i})\) be a function which returns the sum of all consistency values for an alternative \(a_{i}\)for the decision-maker \(dm_{k}\) :_  
\[F_{AlternativeConsistency}(dm_{k},a_{i})=\sum_{j=1}^{\langle C\rangle}F_{CriterionConsistency}\Big{(}dm_{k},c_{j},a_{i}\Big{)} \tag{6}\]  
If we apply the \(F_{AlternativeConsistency}\) function to the alternatives presented in _selectedAltsToReject_ list for each decision-maker we will get:  
\(\bullet\quad F_{AlternativeConsistency}(hurry,nosh)=-15\);  
\(\bullet\quad F_{AlternativeConsistency}(george,nosh)=-8\);  
\(\bullet\quad F_{AlternativeConsistency}(jane,nosh)=-15\). If we apply the \(F_{AlternativeConsistency}\) function to the alternatives presented in _selectedAltsToPropose_ list for each decision-maker we will get:  
\(\bullet\quad F_{AlternativeConsistency}(hurry,zingara)=0\);  
\(\bullet\quad F_{AlternativeConsistency}(george,zingara)=-6\);  
\(\bullet\quad F_{AlternativeConsistency}(jane,zingara)=0\);  
\(\bullet\quad F_{AlternativeConsistency}(hurry,haiPalace)=0\);  
\(\bullet\quad F_{AlternativeConsistency}(george,haiPalace)=-9\);  
\(\bullet\quad F_{AlternativeConsistency}(jane,haiPalace)=0\). **Definition 23**. : _Let \(F_{Consistency}\) be a function which returns the average of \(F_{AlternativeConsistency}\) for all decision-makers:_  
\[F_{Consistency}(a_{i})=\frac{\sum_{k=1}^{\langle DM\rangle}F_{AlternativeConsistency }(dm_{k},a_{i})}{\langle DM\rangle} \tag{7}\]  
In our example, \(F_{Consistency}(nosh)=-12,667\) and it would be the alternative proposed to be rejected. Regarding the alternatives to propose as a solution, \(F_{Consistency}(zingara)=-2\) and \(F_{Consistency}(huiPalace)=-3\), \(zingara\) would be proposed as a possible solution.
# Methods of Atmospheric Coherence Length Measurement
## 1 Introduction
A smaller value of \(r_{0}\) indicates a more severe disturbance of atmospheric turbulence [12]. Therefore, an essential part of studying the impact of atmospheric turbulence on FSO is the measurement of the atmospheric coherence length. The currently existing methods for measuring the atmospheric coherence length can be divided into two categories: one is the retrieval of \(r_{0}\) based on the atmospheric refractive index structure parameter \(C_{n}^{2}\), and the other is the coherence length \(r_{0}\) direct measurement method. This paper presents a comprehensive review with respect to the atmospheric coherent length measurement methods. Sections 2 and 3 outline both measurement categories, respectively. A summary is presented in Section 4.
# Recent Research Progress in Surface Ligand Exchange of PbS Quantum Dots for Solar Cell Application
## 2 Solid-State Ligand Exchange
### Solvent-Polarity-Eengineered Halide Passivation  
Unsaturated dangling bonds induced from the large surface-to-volume ratio of CQD materials can introduce undesired trap states within the bandgap of CQDs. Atomic halide ligands have been recently used for PbS CQD surface passivation due to their high electronegativity and significant improvement of air stability of PbS CQD solids [23; 24; 25]. Among halides, [PERSON] et al. reported solution-processed iodine treatment on PbS CQDs showing improved surface passivation. By increasing diffusion length, the thickness of the light absorption layer in a device can be increased without affecting the charge extraction. A certified _PCE_ record of 9.9% was achieved by this approach [26]. However, iodine can cause uncontrollable CQD fusing due to its highly reactive characteristic. Other studies reported that more iodine, using methylammonium iodide (MAI) as a milder iodine source, can be introduced into the PbS CQD surface without the damaging effects of fusion. For the solution-phase ligand exchange of oleic acid-capped PbS CQDs, a cosolvent strategy was adopted to create a mixture of miscible solvents, which were toluene and dimethylformamide (DMF) [27]. The cosolvent strategy for tuning the solvent polarity simultaneously enables the dispersion of MAI and PbS CQDs. Therefore, OA-capped PbS CQDs are reacted with iodide anions solvated in DMF, allowing the combination of iodide anion ligands on the PbS CQD surface in the solution-phase. The solution-phase ligand exchange can be explained by the equation: PbS[Pb(OA)\({}_{2}\)] + 2 MA\({}^{+}\)I\({}^{-}\)\(\rightarrow\) PbS(PbI\({}_{2}\)) + 2 MA\({}^{+}\)OA\({}^{-}\). \({}^{1}\)H NMR spectroscopy measurements show new peaks that are consistent with the formation of an iodide ligand shell that dynamically interacts with the OA- removed from the vacant site. MAI solution pretreatment results in the replacement of OA- with approximately 20% to iodide, which is three times higher than that of I\({}_{2}\) treatment, confirmed by quantitative measurements of X-ray photoelectric spectroscopy (XPS) (Figure 4d). After the treatment of MAI and I\({}_{2}\), PL quantum yield (QY) increases up to 15%-29% and 19%, respectively, and the improvement of iodide incorporation leads to enhanced surface passivation (Figure 4a,b). Additionally, MAI pre-treatment improves the colloidal stability of PbS CQDs by the partial elimination of the oleate ligands. The partial removal of ligands, indicating the remaining oleates, is important to stabilize PbS CQDs in octane. When all of the ammonium ligands are completely removed through the above procedures, Au electrodes are employed on the top of the final PbS CQD thin films to allow the ohmic contact. As a result, 10.6% _PCE_ in PbS CQD solar cells was achieved by improved surface passivation, and a series of results contributed to the surface engineering of PbS CQDs, such as using stoichiometry tuning and surface doping.
# Seismic Response Study of L-Shaped Frame Structure with Magnetorheological Dampers
## 5 Analysis and Interpretation of Results
### Results Analysis on Torsional Vibration Control of the Structure  
Contrasts of the horizontal displacement response of the top node of the L-shaped frame structure without MRDs are plotted in Figure 13. As shown in Figure 13, the X directional displacement time history responses of Nodes 153 and 141 are significantly different, and the Y directional displacement time history responses of Nodes 144 and 141 are also different. As shown in Figure 13a, during the excitation of the Taft wave, the X directional maximum displacements of Nodes 153 and 141 are 71.79 and 48.20 mm, and the ratio is 1.489; the Y directional maximum displacements of Nodes 144 and 141 are 53.77 and 44.52 mm, and the ratio is 1.208. As shown in Figure 13b, during the excitation of the  
Figure 12: The max shear forces and bending moments of the columns of every floor of the structure with and without MRDs.  
Kobe wave, the X directional maximum displacements of Nodes 153 and 141 are 89.56 and 71.93 mm, and the ratio is 1.245; the Y directional maximum displacements of Nodes 144 and 141 are 69.62 and 62.01 mm, and the ratio is 1.123. The results show that the L-shaped fracture without MRDs has obvious torsional vibration in the horizontal plane.  
Contrasts of the horizontal displacement response of the top node of the L-shaped frame structure with MRDs are plotted in Figure 14. As shown in Figure 14, the X directional displacement time history responses Nodes 153 and 141 and Y directional displacement time history responses of Nodes 144 and 141 are almost identical. As shown in Figure 14, during the excitation of the Taft wave, the X directional maximum displacements of Nodes 153 and 141 are 42.36 and 37.25 mm, and the ratio is 1.140; the Y directional maximum displacements of Nodes 144 and 141 are 40.26 and 37.25 mm, and the ratio is 1.081. As shown in Figure 14, during the excitation of the Kobe wave, the X directional maximum displacements of Nodes 153 and 141 are 57.41 and 57.55 mm, and the ratio is 0.998; the Y directional maximum displacements of Nodes 144 and 141 are 57.79 and 51.92 mm, and the ratio is 1.113. The results show that, compared with the L-shaped frame structure without MRDs, the torsional vibration of the structure with MRDs is significantly reduced.  
Figure 13: Contrasts of the horizontal displacement response of the top node of the structure without MRDs.  
It can be seen from Figure 7 that the center of mass has different positions from the center of rigidity in the L-shaped frame structure; it directly leads to the obvious torsional vibration response under the action of seismic waves. Since the multi-floor L-shaped frame structure is a shear-type structure, many MRDs are arranged at the bottom floor of the structure, which can effectively increase the shear rigidity and torsional rigidity of the overall structure. This is the main reason why the multi-dimensional vibration and torsional vibration response of the L-shaped frame in this article are effectively controlled after setting the MRDs.