esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SPME Process
### Extraction and Desorption Techniques
_Desorption_ can be proceed in two different ways; static mode, by dipping the fiber into the mobile phase or solvent for a specified period, and dynamic mode, where the analytes are desorbed into a flowing mobile phase [65]. It is important to identify desorption solutions that completely remove the analytes from the fiber, eliminating carry over, but that do not damage the sorbent material or degrade the analytes of interest [74]. In the case of a GC coupling the analytes are released by _thermal desorption_, for which the SPME fiber is inserted into the GC inlet and heated to temperatures that increase analyte volatility sufficient for their release. The analytes are physically released from the sorbent through heating whereby the volatility of the target compounds is increased. The carrier gas flow rate and the injector temperature are critical factors for effective thermal desorption [75]. High desorption temperatures effect rapid transfer of the target analytes from the injector to the chromatographic column, but may reduce the stability of the sorbent and lead to bleeding of the polymeric material [73]. Therefore the thermal stability of the fiber coating determines the upper desorption temperature limit [74]. Combined with an HPLC interface desorption is performed by solvent extraction in the desorption chamber [12], namely _liquid desorption_. Liquid desorption is conducted using a small volume of suitable solvents to transfer the target analytes to the analytical instrumentation [74]. Desorption of the analytes can also be conducted using a polar organic solvent, such as methanol or acetonitrile [65]. This approach is often used in combination with liquid chromatography (LC) [65]. For thermally labile compounds liquid desorption has the advantage of not requiring elevated temperatures compared to thermal desorption [74]. Liquid desorption is the combination of SPME with HPLC where desorption is achieved using an appropriate extraction solvent in a desorption chamber. Individually designed desorption interfaces have been reported [51, 76]. There is a commercially available device that enables desorption of all analytes directly into the LC injector [65]. Another form of desorption is conducted by means of _laser techniques_. SPME in combination with matrix-assisted laser desorption/ionization mass spectrometry was reported [74; 77].
## 2 System Model and Problem Description
### System Model and Clustering Approach
It is clear that \(\mathcal{G}_{k}[n]\subseteq\mathcal{G}\) based on the class of the person \(k\). In addition, the number of persons associated with any geofence \(g\) at the time interval \(n\) is denoted as \(K_{g}[n]\), where the set of these persons is defined as \(\mathcal{K}_{g}[n]=\{1,\ 2,\ \ldots,\ K_{g}[n]\}\). Consequently, the proposed user-centric clustering approach is subject to the following requirements,  
\[\beta_{kg}[n]=1,\forall k\in\mathcal{K},\forall g\in\mathcal{G}_{k}[n],\forall n \in\mathcal{N}, \tag{2}\]  
\[\beta_{kg}[n]\in\{0,1\},\forall k\in\mathcal{K},\forall g\in\mathcal{G},\forall n \in\mathcal{N}, \tag{3}\]  
\[\sum_{g\in\mathcal{G}_{k}[n]}\beta_{kg}[n]=G_{k}[n],\forall k\in\mathcal{K}, \forall n\in\mathcal{N}, \tag{4}\]  
\[1\leq\sum_{g\in\mathcal{G}_{k}[n]}\beta_{kg}[n]\leq G,\forall k\in\mathcal{K}, \forall n\in\mathcal{N}, \tag{5}\]  
\[\sum_{k\in K_{g}[n]}\beta_{kg}[n]=K_{g}[n],\forall g\in\mathcal{G},\forall n \in\mathcal{N}, \tag{6}\]  
where Equation (2) illustrates that the binary indicator \(\beta_{kg}[n]\), is equal to one for all geofences in the defined cluster for the person \(k\) at the time interval \(n\). Meanwhile, Equation (3) shows that the indicators \(\beta_{kg}[n],\forall k\in\mathcal{K},\forall g\in\mathcal{G},\forall n\in \mathcal{N}\), should have binary values. Equation (4) indicates that the number of associated geofences in the cluster of the person \(k\) at the time interval \(n\) is \(G_{k}[n]\), and the inequality in Equation (5) shows the limits on the number of associated geofences in the defined cluster for each person \(k\) at each time interval \(n\) based on the authorization classes of persons. In addition, Equation (6) indicates that the number of persons associated with the geofence \(g\) at the time interval \(n\) is \(K_{g}[n]\). The manager of the workplace can control the density of the persons during working hours by setting the values \(K_{g}[n],\forall g\in\mathcal{G},\forall n\in\mathcal{N}\). Define \(p_{kg}[n]\) to represent the geofence presence probability (GPP) for each person \(k\) at the time interval \(n\). The GPP defines the probability that the person \(k\) coexists inside the geofence \(g\) at the time interval \(n\). Clearly, GPP can be changed during the working hours and can be defined by the manager of the workplace. Then, the probability \(p_{kg}[n]\) can be defined for each time interval \(n\) as follows,  
\[0\leq p_{kg}[n]\leq 1,\forall k\in\mathcal{K},\forall g\in\mathcal{G},\forall n \in\mathcal{N}, \tag{7}\]  
\[\sum_{g\in\mathcal{G}}p_{kg}[n]=1,\forall k\in\mathcal{K},\forall n\in \mathcal{N}, \tag{8}\]  
where Equation (7) indicates that the probability \(p_{kg}[n]\) is a positive value between \(0\) and \(1\). Meanwhile, Equation (8) illustrates that the sum of GPP of all geofences for each user \(k\) at each time interval \(n\) is equal to one.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 4 Results
### Assimilation
#### 4.2.1 Local Comparison  
Following the validation of the S1 SSM products against in situ measurements, the influence of assimilating the S1 SSM products alone, together with LAI, and assimilating LAI alone on the simulated 0.01-0.04 m SSM and 0.2-0.4 m RZSM (WG2 and WG5, respectively) was investigated.  
The impact of the assimilation was first evaluated on WG2 for the three assimilation experiments by comparing them with the OL outputs for the three study regions. Very little or no impact on the simulated WG2 was found (with respect to the score values presented in Table S2). A larger impact of assimilation was observed on WG5. To better visualize the impact of the assimilation experiments on WG5, a relative local mean value was calculated for each analysis as well as for the in situ measurements for each of the stations in the Montpellier region using Equation (2).  
Figure 7 illustrates the relationship between the relative mean local interannual differences (Equation (2)) of the observed RZSM from SMOSMANIA and those from the OL and analysis simulations for the Montpellier region. In 2018, the in situ measurements showed wetter conditions compared to 2017, as the relative differences were greater than 0, while 2019 was drier. The interannual variability was well represented in both the model and the analysis at the 0.3 m soil depth. There was a difference in scale for the relative differences coming from the model and the in situ measurements. This difference was due to the spatial and temporal resolution of the two sources of information. The joint assimilation of SSM and LAI satellite products led to a slightly better correlation with respect to the OL (\(R\) = 0.70 and 0.66, respectively). The values of the RMSD and the MB scores were improved.
# A Dynamic Model Updating Method with Thermal Effects Based on Improved Support Vector Regression
## 5 The Numerical Examples
### Thermophysical Parameters Identification of Aluminum Alloy Plate
The deviation between the updated temperature distribution and the real temperature distribution is almost negligible in each temperature range. Therefore, the thermophysical parameter identification method proposed in this paper can obtain a consistent temperature distribution with the real distribution. Assuming that the variation in the heat transfer coefficient with temperature dependence satisfies a higher-order polynomial form, which is shown in Figure 5, the simulation result indicates that the method proposed in this paper has higher accuracy than the traditional parametric model updating method. According to the fitting results obtained in MATLAB, the variation in the real heat transfer coefficient with temperature dependence basically satisfies the cubic polynomial, which has the following function form:  
\[y=(-5.0553e-6)t^{3}+0.0036t^{2}-0.3363t+242.242 \tag{33}\]  
Table 7 shows the design space of identified parameters. Table 8 shows the comparison results of temperature distributions based on parametric updating method and functional updating method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Nodes Number**} & **Real Temperature** & **Initial** & \multirow{2}{*}{**Deviation**} & **Updated** & **Deviation** \\  & **(°C)** & **Temperature (°C)** & & **Temperature (°C)** & & **Deviation** \\ \hline
32 & 288.45 & 286.00 & 0.85\% & 288.41 & 0.013\% \\
86 & 258.61 & 251.25 & 2.85\% & 258.52 & 0.035\% \\
151 & 220.74 & 209.00 & 5.32\% & 220.67 & 0.032\% \\
216 & 180.32 & 167.00 & 7.39\% & 180.37 & 0.028\% \\
281 & 136.89 & 125.00 & 8.69\% & 136.96 & 0.051\% \\
346 & 90.78 & 83.00 & 8.57\% & 90.97 & 0.21\% \\
411 & 43.79 & 41.00 & 6.37\% & 43.80 & 0.23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison among real temperature, initial temperature and updated temperature. Figure 5: The cubic polynomial fitting curve of real heat transfer coefficient. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Coefficient** & **Cubic Order** & **Polynomial Order** & **First Order** & **Constant** \\ \hline Upper & \(-4\times 10^{-6}\) & 0.003 & \(-0.3\) & 230 \\ Lower & \(-6\times 10^{-6}\) & 0.004 & \(-0.4\) & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The range of design space of identified parameters. Table 8 shows that the accuracy of the identification temperature based on the functional updating method of nodes 32, 86, 151, 216 and 281 is higher than the accuracy based on the parametric updating method, while the accuracy of the identification temperature based on the functional updating method of nodes 346 and 411 is lower than the accuracy based on the parametric updating method. In general, the identification results based on the functional updating method have higher precision. Figure 6 shows the comparison histogram of the identification temperature deviations obtained by the two methods above.
## 3 Datasets and Evaluation Metrics
### Datasets
#### 3.1.3 Datasets of HDR Captured with Mobile Devices  
In the field of processing HDR images captured by mobile devices, the Google HDR+ dataset [52] stands out for containing 3640 sequences of images captured in burst mode with smartphones Nexus 6, 5X, and 6P (Google LLC., Mountain View, CA, USA). The images were obtained from multiple frames with different exposures, which allows for the generation of a final HDR image with less noise and greater dynamic range. This dataset is widely used in research aimed at improving the quality and computational efficiency of HDR image processing on mobile devices.  
Mobile HDR [86] consists of 251 sequences of LDR images captured by smartphones at 4K resolution accompanied by their corresponding HDR bracketing images. The images were captured in a wide range of lighting conditions, covering both day and night, making this dataset particularly relevant for studies focused on HDR reconstruction on mobile devices.
## 2 Materials and Methods
### Equipment Instrumental Analysis  
#### 2.4.1 Fourier-Transform Infrared Spectroscopy (FTIR)  
To identify the type of polymeric coating, infrared spectra were acquired using an ATR (attenuated total reflectance)-FTIR spectrometer (ATR-PRO-ONE, FTIR 4700, Jasco, Tokyo, Japan) equipped with a diamond optical crystal in the region from 4000 to 650 cm\({}^{-1}\). The analysis was done on both surfaces, internal and external side, of each sample by covering the entire crystal surface. The ATR-FTIR spectrometer was controlled by the software Spectra Manager (version 2), and the identification was performed by using the KnowItAll 17.4.135.B software to compare the sample spectra obtained with several commercial databases related to polymers (IR Spectral Libraries of Polymers & Related Compounds from Bio-Rad Laboratories, Inc., Philadelphia, PA, USA).
## 5 Results and Discussion
### Allocation Model  
After labeling the overdue loans, what is still missing is to allocate them to the officers available, following the prioritization rules. In order for that to be possible, an integer programming model was created to optimize the number of loans per officer. This was also presented in [1].  
Figure 8: Visual representation by the PCA method of the three clusters present in the resample after analyse the centers [1].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cluster** & **Percentage of Loans Recovered** & **Amount of Defaulting** & **Average of an SMS Feature** \\ \hline
**0** & 51.77\% & 124,178,220.00 & 59,024,711.00 \\
**1** & 50.50\% & 66,444,430.00 & 70,482.00 \\
**2** & 67.77\% & 89,303,870.00 & 94,007,441.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Criteria results per each cluster [1].  
The first action is transforming the output from the clustering model into an input for this model. Thereunto, three variables were created to provide such information:  
\[W_{i}=\begin{cases}1&\text{if the loan $i$ is part of Cluster 0}\\ 0&\text{otherwise}\end{cases}\]  
\[Y_{i}=\begin{cases}1&\text{if the loan $i$ is part of Cluster 1}\\ 0&\text{otherwise}\end{cases}\]  
\[Z_{i}=\begin{cases}1&\text{if the loan $i$ is part of Cluster 2}\\ 0&\text{otherwise}\end{cases}\]  
In addition, there is one more piece of information that comes from the clustering model--the prioritization of the loans. So, three weights were created, which will be associated with each cluster to add the value matching this prioritization scale, where \(\gamma\) (weight of the loans belonging to Cluster 2) \(>\alpha\) (weight of the loans belonging to Cluster 0) \(>\beta\) (weight of the loans belonging to Cluster 1).  
The goal of this allocation model is to maximize the total weight of the loans assigned to an officer and the mathematical model is:  
Maximize \[\sum_{i=1}^{n}\sum_{j=1}^{m}(\alpha W_{i}+\beta Y_{i}+\gamma Z_{i})x_{ij}\] Subject to \[\sum_{i=1}^{n}x_{ij}=1, \text{for each loan $j$}\] \[\sum_{j=1}^{n}x_{ij}\leq k, \text{for each officer $i$}\] \[x_{ij}\in\{0,1\}, \text{for $i=1,\cdots,n$ and $j=1,\cdots,m$}\]  
where \(x_{ij}=1\) if loan \(j\) is assigned to officer \(i\), 0 if not. Firstly, the objective function indicates the maximization of the allocation of most priority loans to the officers. Then, the first set of constraints ensures that every loan is assigned to only one officer and the second set of constraints ensures that every officer does not have more assigned loans than their capacity.
# Mutated Channelrhodopsins with Increased Sodium and Calcium Permeability
###### Abstract  
(1) Background: After the discovery and application of _Chlamydomonas reinhardtii_ channelrhodopsins, the optogenetic toolbox has been greatly expanded with engineered and newly discovered natural channelrhodopsins. However, channelrhodopsins of higher Ca2+ conductance or more specific ion permeability are in demand. (2) Methods: In this study, we mutated the conserved aspartate of the transmembrane helix 4 (TM4) within Chronos and _P_SchR and compared them with published ChR2 aspartate mutants. (3) Results: We found that the ChR2 D156H mutant (XXM) showed enhanced Na+ and Ca2+ conductance, which was not noticed before, while the D156C mutation (XXL) influenced the Na+ and Ca2+ conductance only slightly. The aspartate to histidine and cysteine mutations of Chronos and _P_SchR also influenced their photocurrent, ion permeability, kinetics, and light sensitivity. Most interestingly, _P_ScR D139H showed a much-improved photocurrent, compared to wild type, and even higher Na+ selectivity to H+ than XXM. _P_ScR D139H also showed a strongly enhanced Ca2+ conductance, more than two-fold that of the CatCh. (4) Conclusions: We found that mutating the aspartate of the TM4 influences the ion selectivity of channelrhodopsins. With the large photocurrent and enhanced Na+ selectivity and Ca2+ conductance, XXM and _P_ScR D139H are promising powerful optogenetic tools, especially for Ca2+ manipulation.  
Keywords: optogenetics; channelrhodopsins; sodium; calcium; DC gate +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
[25] proposed a calculation model for the surface subsidence of the excavation of a neighboring double-rectangle header tunnel, and [PERSON] et al. [26] carried out the calculation and analysis of the surface subsidence-uplift calculation for the excavation of rectangular header tunnels in layered strata. Using the complex function method of elasticity, [PERSON] et al. [27] proposed a method for determining the coefficients of the conformal transformation function for a non-circular cross-section tunnel. [PERSON] [28] presented a level solution of the ground stress and displacement in a non-circular tunnel excavation considering the gravity and lateral pressure of the ground. [PERSON] et al. [29] presented the optimized mapping function of the least-squares method for the excavation boundary of rectangular tunnels and the level solution of ground stress for a uniformly convergent situation. Based on the above literature, the Peck formula is an empirical method for studying surface subsidence, not a theoretical method. The stochastic medium method can only calculate the displacement of the surface plane, and there is no way to study the internal displacements and stresses of the stratum. Mindlin solution mainly studies the displacements and stresses under the action of a lateral force, especially the impact of the thrust of pipe jacking or shield. The virtual mirror method is mainly aimed at circular tunnels, and it is not suitable for other shapes. Therefore, for the theoretical analysis of the excavation problem of non-circular tunnels, using the complex function method has many benefits. In the early stage of pipe jacking tunnel excavation without grouting backfill, the complex function analytic theory can analyze the ground displacement and stress problem. Further research is needed regarding the complex function analytic theory of the impact of shallow rectangular pipe jacking excavation, especially for the more realistic non-uniform convergence of the excavation boundary. Of course, the complex function method also has certain limitations, mainly that it can only study two-dimensional plane strain problems. Three-dimensional problems, such as surface subsidence in the longitudinal section, cannot be calculated with the complex function method. However, they can be calculated with [PERSON]'s solution and the stochastic medium method. In this paper, the cross-section model for a shallow rectangular pipe jacking excavation is established; the stress condition of the ground boundary and the displacement condition of the excavation boundary are considered; the convergence mode for the excavation boundary is provided; and the analytical solution is derived using the approximate mapping function and potential function of the series form. Then, the displacements and stresses of the stratum are calculated, and the efficiency and accuracy of the solution are proved with the contrast verification of the finite element simulation and the engineering-measured data. Finally, the influence of the relevant theoretical and construction parameters is calculated and analyzed.
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things
## 6 Results of Random Topology
### Convergence Time
#### 6.1.2 Convergence Time of 40 Nodes  
After studying the convergence time behavior of the four algorithms, we found that the original algorithm also took the longest time to build up the DODAG. However, it took less time than the 20 nodes did. This leads us to conclude that when nodes become more numerous, the convergence time decreases because more surrounded nodes mean more neighbors to get the messages from; thus, it is faster to build the relations or communications between nodes.  
Although, as illustrated in Figure 7, we noticed that the elastic hop count algorithm reached the best convergence time since it depends on the hop count. When hop increases, the listen-only period also increases to control the DIO messages between nodes and to build up the DODAG. Then, the dynamic algorithm takes the best convergence time after the elastic hop count; then, the e-trickle algorithm took almost half the time that the original algorithm did.  
In conclusion, the best time is reached by the algorithm that can control the listen-only period in a good manner.
## 2 Related Work
### Energy Efficient Routing
#### 2.1.5 Multipath Routing Protocols  
Multipath routing protocols [20,21], avoid the depletion of nodes energy along the path having the lowest energy by sharing the load on several paths. As energy is taken into account, the path minimizing the energy consumed by an end-to-end transmission is usually selected as the primary path. Another path, called the secondary one, is used less frequently, with a probability inversely proportional to its energy cost.
## 2 Study Area and Data
### Mine Big Data
#### 2.2.1 UAS Imagery--Multispectral, LiDAR, Aeromagnetic
The key data parameters of each type of UAV image are summarized as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**MS600 Pro**} & \multicolumn{2}{c}{**LiDAR**} & \multicolumn{2}{c}{**QuSpin Rb OPM**} \\ \hline Effective pixels & 1.2 million & Measuring range & 450 m\#80\%, 0 kk; 190 m\#010\%, 100 kk & Resolution & 0.1 nT \\ \hline FOV & Horizontal: 49,6\({}^{\circ}\); vertical: 38\({}^{\circ}\) & Ranging accuracy & \(\pm\)2 cm (at 50 m) & Baseline error (200 Hz sampling) & 3 nT \\ \hline Typical width & 110 m \(\times\) 83 m\#h = 120 m & Point cloud density & 240,000 points/s & Weight & 1.2 kg \\ \hline Ground spatial resolution & 8.65 cm@h = 120 m & FOV & Horizontal: 70.4\({}^{\circ}\); vertical: 4.5\({}^{\circ}\) & Power Consumption & \textless{}10 w \\ \hline \multirow{4}{*}{Band range} & 450 nm@35 nm; 530 nm@27 nm; 650 nm@25 nm; 720 nm@10 nm; 840 nm@30 nm; 900 nm@35 nm & Positioning accuracy (IMU) & Horizontal: \textless{}5 cm; Vertical: \textasci{}10 cm \\ \cline{1-1} \cline{2-5} \cl