esa-sceva/satcom-chunk-collection huggingface dataset:
# ILRS Reference Point Determination Using Close Range Photogrammetry
## 2 Mathematical Background
### Reference Point Determination
Thus, the coordinates of \(\mathbf{P}_{i,k}\) are indicated as an explicit function of the unknowns, and a Gauss-Markov model can be used to estimate the parameters. However, as shown by [PERSON] et al. [26], the resulting normal equation matrix is rank-deficient, because the observed object coordinates are insensitive to define the orientation of the telescope frame. The rank deficiency becomes \(D=1\), if the trajectories of at least two markers \(\mathbf{p}_{i}\) are observed, and concerns the rotation around the elevation axis with angle \(\omega_{k}\). The problem is similar to the free adjustment discussed in Section 2.1, and the remaining rank deficiency can be solved by introducing the NNR condition  
\[0=\sum\left(z_{i}^{0}dy_{i}-y_{i}^{0}dz_{i}\right). \tag{19}\]  
This condition is equivalent to Equation (14) and solves the rotation deficiency concerning the rotation around the \(x\)-axis of the telescope frame. Here, \(y_{i}^{0}\), \(z_{i}^{0}\) are the approximation values of \(\mathbf{p}_{i}\), and \(dy_{i}\), \(dz_{i}\) are the estimated corrections. A detailed derivation of the refer  
Figure 2: Invariant reference point (IRP) transformation model that combines the grey dash-dotted depicted telescope fixed frame and the light red dashed depicted Earth-fixed reference frame. An arbitrary marker \(\mathbf{p}_{i}\) is shown in several \(k\) telescope orientations. The green star marks the reference point \(\mathbf{P}_{\text{IRP}}\). ence point determination model and possible alternative datum conditions are discussed by [PERSON] et al. [26; 27].
# Finding Optimal Stations Using Euclidean Distance and Adjustable Surrounding Sphere
## 4 Proposed Methods  
In this study, finding optimal stations' main target goal is achieving the maximum spatial coverage while still preserving appropriate overlapping areas. The maximum spatial coverage designed can be realized through the optimal placement of the stations in the city. Moreover, maintaining a relevant overlapped area can enhance effectiveness for more reliable and accurate data collection. Accordingly, in this section, we proposed finding the stations based on the Euclidean Distance and the k-nearest neighbor's algorithm (k-NN). The proposed methods framework consists of four main methods and two evaluation criteria, as shown in Figure 2.  
In the proposed framework, our input consists of a map and parameters. The map of the study area is divided into a square of continue grid. A centroid of each square grid is a point that consists of latitude-longitude coordinates. For simple understanding throughout this paper, we use the term \"location\" (\(L_{i}\)), \(i=1\dots\)\(N\) to represent the centroid of a grid. The parameters consisted of (i) a grid index at the center of the map, (ii) a specified number of  
Figure 2: The framework of proposed methods.  
stations, and (iii) the diameter length of the surrounding sphere of each station as described in Section 6.1. Next, we give stations in area \(A\), which consist of \(\eta\) stations. The value \(S=\{S_{j},\ldots,S_{\eta}\}\) is a list of stations. Each station \(S_{j},j=1\ldots\)\(\eta\) is at a location in our study area. All of the input will use to calculate by our proposed methods.  
M1: Euclidean Distance (ED) and M2: Fixed Surrounding Sphere are the initial method for finding the next stations. The M3 method is a combination of previous methods. The M4 is an upgraded version of M3. Our evaluation criteria, coverage percentage (COV), and weighted coverage degree (WCD) are used to evaluate the results from the methods then return the optimal network. The comprehensive methods are described in the following sections.
## 2 Materials and Methods
### Volatile Organic Compounds Analysis  
For the VOC analysis, equivalent volumes of products of the same type (i.e., kefir, yazhenka, amasi and mahewu) were pooled together. The volatile compounds were extracted by solid phase micro extraction (SPME) and analyzed by gas chromatography coupled to mass spectrometry (GC-MS, GS 2010 gas chromatograph (Shimadzu, Kyoto, Japan) directly attached to a GCMS-QP 2010 mass spectrometer (Shimadzu, Kyoto, Japan)). SPME extractions were performed using 50/30 mm thick PDMS/DVB (polydimethylsiloxane/divinylbenzene) fibers (Supelco, Bellefonte, PA, USA) and 40 mL flasks fitted with mininert valves (Supelco, Bellefonte, PA, USA) that were inserted above the gas-phase bottle for extraction for 60 min at 40 \({}^{\circ}\)C. Desorption was conducted at 250 \({}^{\circ}\)C for 3 min. The Optima-1 column (25 m \(\times\) 0.25 mm, Supelco, Japan), precalibrated with retention index standards (Sigma, St. Louis, MO, USA) of C8 and C32 aliphatic hydrocarbons, was initially held 2 min at 70 \({}^{\circ}\)C, then heated to 90 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C per min, to 160 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C per min, and finally heated to 280 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C per min, which was held for 5 min; at the following temperatures: injector, 230 \({}^{\circ}\)C; interface, 205 \({}^{\circ}\)C; detector, 200 \({}^{\circ}\)C. Helium with a flow rate of 0.7 mL\(\cdot\)min\({}^{-1}\) and a flow pressure of 1:20 was used as a carrier gas. The total analysis time was 25 min. Mass detection was carried out under the TIC registration mode in the mass acquisition range (m/z) from 45 to 450 (Supplementary Materials Figures S4-S7). The relative intensities (further relative abundances) of VOC were obtained by normalization on the total intensity of the assigned peaks. All experiments were performed in triplicate.  
Volatile compounds were identified by comparing their experimental spectra with those from the National Institute of Standards and Technology (NIST/EPA/NIH Mass Spectral Database (NIST 11), Gaithersburg, MD, USA). Only the assignments of peaks with \(\geq\)90% confidence were considered reliable and reported here.  
The data about flavors of individual compounds were extracted from FlavorDB [25]. For the concise descriptions of flavor, the flavor wheel developed by Foodpairing company was adopted [26]. For the graphical data representations \"ggplot2\" (donut charts) and \"eulerr\" (area-proportional Euler diagram) R packages were used. The color pallets were taken from RColorBrewer and Polychrome R packages.
# In-Band Pumped Thulium-Doped Tellurite Glass Microsphere Laser
## 1 Introduction
When considering continuous wave (CW) generation under CW pump, a solution can be found by solving a system of many nonlinear algebraic equations (of the order of the number of modes) obtained by taking \(\mathrm{d}/\mathrm{d}t=0\). However, we have proposed another semi-analytical approach allowing us to solve only a single nonlinear algebraic equation using exhaustive search for each mode (or a combination of modes) and to find stable CW generation regimes [29]. Here, to describe CW lasing in the 1.9-2 \(\upmu\)m range in a Tm-doped microsphere, we use the same approach which becomes very simple for in-band pump.
# Analysis of _In Vivo_ Plant Volatiles Using Active Sampling and TD-GC\(\times\)GC-TOFMS
## 4 Discussion  
In this work, a reusable _in vivo_ plant sampling system based on active sampling onto a TD tube is described. This system permits the analysis of whole, living plants. SPME has frequently been used to sample the headspace of plants [9; 14; 16; 17; 18]. However, SPME has limitations, such as a limited surface area and a bias simultaneously against both highly volatile and heavier semi-volatile peaks [15]. It must be noted that plant identifications were based upon the identification tags provided by the garden center from which the plants were purchased. Consequently, we do not know the exact variety of tomato, mint, and blueberry that were used. However, from a point of view of demonstrating the methodology, the exact varieties of the plants are not important.  
This approach can be adapted to plants of varying sizes by altering the sizes of the glass chamber and Teflon(tm) plate and potentially adjusting the flow of air supplied to the plant. For these experiments, it was observed that plants became visibly distressed (drooping leaves) when lower supply air flows were used. Reasons for the plant stress could include the accumulation of oxygen in the chamber or a decreased supply of CO\({}_{2}\). A further stressor could also be the accumulation of plant VOCs in the atmosphere of the chamber. Increasing the air flow to 300 mL/min appeared sufficient to keep the plants content within the sampling chamber. Users must be aware of this parameter to avoid stressing plants and biasing results. The system can also potentially be adapted for other plant sampling scenarios to target specific plant organs by adjusting which parts of the plant are isolated from the surrounding environment (e.g. flowers, leaves, fruits, soil, etc.). For targeting specific classes of compounds of plant volatiles, users can select sorbent beds for the TD tubes that will best trap the compounds of interest.
## 4 Performance Evaluation and Discussion  
To assess the validity and superiority of the proposed approach, the software GPS (Global Positioning System) L1 receiver is conducted. The input signal of the software receiver is the IF signal, which is generated by a satellite navigation signal simulator. The frequency and sampling rate of the IF signal are 1.42 MHz and 10 MHz, respectively. The signal resolution is set to 4-bit. Nine channels have been simulated in the software receiver for the visible satellites.  
To evaluate the performance, four tracking methods are used in the experiments.  
Method 1: the proposed AKF-based tracking method;  
Method 2: the conventional PLL tracking method (the PLL's bandwidth is 15 Hz and the update period is 4 ms);  
Method 3: the standard KF tracking;  
Method 4: the STKF (strong tracking Kalman filter) tracking method.
## 3 Results and Discussion
### Grid and Time-Step Independence Study  
A series of tests were done to check the grid independence of the solution for three different mesh grids including 1.3, 1.8 and 2.3 millions of tetrahedral elements. As shown in Figure 5a, the mesh grids with 1.8 and 2.3 million elements provide undistinguishable results. The grid with 1.8 million elements shown in Figure 6 was then selected for all simulations. The grid was refined close to the tube walls, where higher gradients are expected, with a refinement ratio of 1.1. Additionally, some tests were done to evaluate the appropriate time-step. Results obtained with three time-steps, namely 0.5, 1 and 2 s, are compared in Figure 5b. The temporal evolutions of the liquid fraction appear quite similar for the two smaller time-steps. Finally, the time-step is fixed to 1 s in all simulations. The maximum number of iterations per time step was fixed to 100.
## 3 Architecture
### Data Transfer Time and Energy Model  
Suppose that subtask \(u\) is the predecessor of subtask \(m\), that is to say \(R_{ku,m}=1\), we know that:  
(1) If subtask \(u\) and subtask \(m\) are executed in the same location (both are on the edge cloud or on the device), so we have \(D_{ku}=D_{km}\). We assume there is no data transmission overhead between the two subtasks, so we have \(T_{ku,m}=0\) and \(E_{ku,m}=0\).  
(2) If the locations where subtask \(u\) and subtask \(m\) are executed are different (one is on the edge cloud and the other is on the device), we have \(R_{ku,m}=1\), \(D_{ku}=0\), and \(D_{km}=1\). When the subtask \(u\) is executed on the mobile device and the subtask \(m\) is executed on the edge cloud, computation result transmission delay \(T_{ku,m}\) is generated, and it can be calculated by Equation (5), which depends on the result data amount \(e_{ku,m}\) and the transmission rate \(B_{s}\).  
\[T_{ku,m}=\frac{e_{ku,m}}{B_{s}} \tag{5}\]  
Furthermore, the energy consumption \(E_{ku,m}\) for transmitting the result data is shown as follows:  
\[E_{ku,m}=T_{ku,m}p_{s} \tag{6}\]  
(3) If subtask \(u\) and subtask \(m\) are executed in different locations, we have \(R_{ku,m}=1\), \(D_{ku}=1\) and \(D_{km}=0\). When subtask \(u\) is executed on the edge cloud and subtask \(m\) is executed on the mobile device, this results in a data receiving delay, which depends on the amount of data transmission \(e_{ku,m}\) and the data transmission rate of the result \(B_{r}\). Thus, the data receiving delay \(T_{ku,m}\) between the two tasks can be expressed as follows:  
\[T_{ku,m}=\frac{e_{ku,m}}{B_{r}} \tag{7}\]  
The energy consumption \(E_{ku,m}\) for receiving the computation result data is shown in the following:  
\[E_{ku,m}=T_{ku,m}p_{r} \tag{8}\]
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 3 Related Work
### Time Series Prediction Algorithm  
#### Traditional Time Series Model  
Temporal variation modeling has been a central focus in time series analysis, evolving from classical statistical methods to advanced deep learning approaches. Traditional models like ARIMA, Holt-Winter, and Prophet rely on pre-defined temporal patterns, which often fail to capture the complexity of real-world data. Recent advances in deep learning have introduced diverse frameworks, such as MLP, TCN, and RNN-based models, and Transformers, each leveraging unique mechanisms to encode temporal dependencies [16].  
MLP-based methods process temporal information through fixed-layer parameters, while TCNs use convolutional kernels to identify variations across time [17]. RNN-based approaches utilize recurrent structures to implicitly capture temporal relationships through state transitions [18]. Transformers, with their attention mechanisms, excel at discovering temporal dependencies and handling intricate patterns by leveraging innovations like AutoCorrelation (Autoformer) and seasonal-trend decomposition (FEDformer) [19; 20; 21].  
Building on these developments, recent research explores multi-periodicity and temporal 2D variations, introducing task-general models that transcend specific analysis tasks. These approaches leverage computer vision techniques to enhance temporal modeling, marking a significant step toward unified solutions for complex time series challenges [22].
# Magnetic Field Penetration Depth in Various Materials and Applications
## 3 Results
### Penetration Depth  
The penetration depth of the magnetic field in (nm) in type II superconducting materials is shown in Table 2, i.e., the penetration applied in the studies reviewed is very low. Figure 7: Number of studies found in the review by application area. The mass of the charge carrier (m) in type II superconductors is usually close to the mass of the free electron, whose value is about 9.10938356 \(\times\) 10\({}^{-31}\) kg. The elementary electron charge (e), a fundamental physical constant whose value is about 1.602 \(\times\) 10\({}^{-19}\) C. (coulombs), is that carried by the charge carriers (Cooper pairs). The vacuum permeability (\(\mu_{0}\)) is a physical constant describing the ability of the vacuum to admit the passage of a magnetic field; its value is \(4\pi\times 10^{-7}\) N/A\({}^{2}\) (Newtons per ampere squared). The superfluid carrier density (\(\rho\)s) is the number of Cooper pairs per unit volume; its value is in the range of 10\({}^{27}\) to 10\({}^{28}\) m\({}^{-3}\). However, these parameters are fundamental in the theory of superconductivity, as they determine key properties such as London's penetration depth \(\lambda\) in (nm), which is calculated using Equation (7), where \(\lambda\) (lambda) defines the distance at which the magnetic field can penetrate the superconductor. These values can vary depending on the chemical composition, electronic structure, and state of the material. Niobium (Nb) can be either a type I or type II superconductor, depending on the purity and condition of the material. However, it is generally considered a type II superconductor when in its impure form or as an alloy. VT6 is a titanium alloy composed mainly of titanium (Ti), aluminum (Al), and vanadium (V). However, it is not considered a type II or type I superconductor at conventional temperatures.
## 6 Results and Discussion
### Airflow Simulations
#### 6.2.2 Effect of Face Air Quantity  
The effect of face air quantity on the airflow pattern in the longwall test gallery is presented in Figure 13. An increase in face air quantity minimizes the issue of low airflow in the walkway and the space between simulated coal face and sheared body. High face velocity also helps in making the flow more uniform in the walkway.  
Figure 12: Average velocity gradient at planes perpendicular to the airflow direction in the test gallery with face air quantity at 23.23 m\({}^{3}\)/s and scrubber capacities at 0%, 50%, and 100%.
## 3 Results and Discussion
### Low-Temperature Property Analysis  
#### 3.3.1 Bbr  
The bending beam rheometer test (BBR) can evaluate the creep characteristics of bitumen at low-temperatures, mainly by creep stiffness (\(S\)) and creep rate (m-value). In general, a larger \(S\) value implies more tensile stress, and a smaller m-value corresponds to less stress relaxation. So, the small \(S\) value and large m-value are desirable for bitumen. From Figure 9, the incorporation of layered silicates significantly enhanced the \(S\) value while reducing the m-value, demonstrating that layered silicates are deficient in the low-temperature cracking-resistance of bitumen, which has been reported [18]. Among these modified bitumens, the REC-modified bitumen had the largest \(S\) value, followed by OREC-modified bitumen, MMT-modified bitumen, and OMMT-modified bitumen which ranked lowest. The opposite trend was found for m-value results. It is evident that REC impairs the low-temperature properties of virgin bitumen more obviously than MMT, regardless of organic treatment. This is likely due to the relatively great size (including the interlayer space and aspect ratio) of pristine REC, leading to stiffness of bitumen at low temperatures. Furthermore, in comparison with pristine layered silicate, the organic REC is less efficient at decreasing the low-temperature cracking resistance of bitumen, and OMMT exhibits even less efficiency than the OREC. Therefore, it can be concluded that, organic treatment to pristine layered silicate helps in the low-temperature cracking resistance of layered silicate-modified bitumen.  
Figure 8: The analysis results for Figure 7: (**a**) Mass loss at 160 \({}^{\circ}\)C; (**b**) final residual mass.  
Figure 7: The TG result for bitumen samples: (**a**) Virgin bitumen; (**b**) MMT-modified bitumen; (**c**) REC-modified bitumen; (**d**) OMMT-modified bitumen; (**e**) OREC-modified bitumen.
# Randomized Feature and Bootstrapped Naive Bayes Classification
## 4 Datasets
### Baseline Classification Methods  
This section presents the baseline classification methods utilized for performance comparison, specifically random forest (RF) and k-nearest neighbors (KNN), outlining their theoretical foundations and practical implementations. These methods serve as benchmarks to evaluate the effectiveness and robustness of the proposed RFB-NB model.  
(1) Random Forest Random forest is a prominent ensemble learning algorithm that constructs multiple decision trees during the training phase and merges their outputs to achieve improved prediction accuracy and control overfitting. Each decision tree is generated from bootstrapped samples of the training dataset, employing random subsets of features at each split. The final class prediction is made by aggregating the outputs through majority voting in classification tasks [51; 52; 53]. Formally, the classification prediction \(\hat{y}\) of RF for a given instance \(x\) is defined as  
\[\hat{y}=\underset{y\in Y}{\text{argmax}}\sum_{t=1}^{T}I(h_{t}(x)=y), \tag{26}\]  
where \(h_{t}(x)\) denotes the prediction of the \(t\)-th decision tree and \(T\) is the total number of decision trees. The RF classifier inherently accommodates non-linear relationships and interaction effects among features, demonstrating robustness and flexibility across diverse classification problems [54].  
(2) K-Nearest Neighbors K-nearest neighbors is a simple yet effective non-parametric instance-based learning algorithm, widely used in classification tasks due to its conceptual simplicity and intuitive decision-making process [55; 56; 57]. Given an unknown instance, KNN classifies it by identifying its \(K\) closest points from the training dataset based on a defined distance metric, typically Euclidean distance:  
\[d(x,x_{i})=\sqrt{\sum_{j=1}^{d}\left(x_{j}-x_{ij}\right)^{2}}, \tag{27}\]where \(d\) represents the dimensionality of the feature space, \(x\) is the instance to classify, and \(x_{i}\) is a training instance. The predicted class label \(\hat{y}\) is obtained by majority voting among the \(K\)-nearest neighbors:  
\[\hat{y}=\underset{y\in Y}{\text{argmax}}\underset{x_{i}\in N_{K}(x)}{\sum}\,I(y _{i}=y), \tag{28}\]  
where \(N_{K}(x)\) denotes the set of the \(K\)-nearest neighbors to instance \(x\) and \(y_{i}\) is the class label of neighbor \(x_{i}\). Despite its simplicity, KNN can effectively handle complex classification boundaries when properly tuned [58; 59; 60].
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 5 Discussion
### Consequences of the Observed Wind Structures on Air Quality
#### 5.2.1 High PM10 Concentration within the Passy Basin  
During the daytime, a continuous up-valley wind blows over the whole basin width. At a maximum mean velocity of 1.5 \(\mathrm{m}\)\(\cdot\)\(\mathrm{s}^{-1}\) over a mean duration of 5 \(\mathrm{h}\), pollutants would travel a maximum distance of 27 km upstream, which is similar to the valley length (23 km). Under very stable conditions, the \([PM10]\) Chamonix are below 50 \(\mathrm{\SIUnitSymbolMicro g}\)\(\mathrm{m}^{-3}\) (Figure 2). Therefore, it is probably that the polluted up-valley wind does not reach the upstream town and remains blocked by the Servoz sill. The current database does not allow for investigating the trajectory of the air masses beyond the Passy town but several hypotheses can be considered. The polluted air mass can recirculate at a higher level in the opposite direction or be evacuated along the basin sidewalls by the up-slope circulation.  
Under less stable conditions, the up-valley is more developed (not shown) and can flow towards Chamonix, leading to similar \([PM10]\) in the two towns. This is consistent with what is observed at the beginning or at the end of pollution events (Table 1 and Figure 2).  
During the night-time, the Chamonix pollutants may be vented out towards the Passy basin by the down-valley wind, leading to night-time \([PM10]\) at Chamonix that are lower than the daytime \([PM10]\) (not shown). The polluted down-valley wind would flow towards the Passy basin where the emissions of Chamonix can discharge. Depending on the jet buoyancy, it may either reach the ground or run higher, leaving only a weak back-and-forth transport in the layer 0-100 m caused by the wind oscillations. In both cases, the Chamonix PM10 are nevertheless advected within the Passy basin. They might incorporate into the low levels the following day due to the small convective mixing layer developing in the early afternoon over a few hundred of metres (up to about 500 m AVL during IOP1 as seen in Figure 3). As a result, the convective dilution efficiency within the basin would be reduced compared to the efficiency that would occur with clean air at altitude.  
Finally, because of its location at the junction of several tributary urbanized valleys, the basin is likely to collect their aerosol contributions at night. The degree of stability plays an important role by governing the communication between the Passy basin and the Chamonix valley during the day and thus the PM10 transfer from the Passy basin to the upstream Chamonix valley.