esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Pantograph Slide Wear Model Based on Energy Dissipation
## 5 Data Analysis of Pantograph Slide Wear Model
### Sensitivity Analysis of Pantograph Slide Wear Model  
To quantify the impact of key parameter fluctuations on the model output and more accurately evaluate the model's stability under real-world operations, this study included a local sensitivity analysis on the wear model (Equation (13)). Three core parameters--the contact pressure \(P\), current \(I\), and sliding speed \(v\)--were selected for \(\pm\)10% perturbations around the baseline operating conditions (\(P\) = 90 N, \(I\) = 200 A, \(v\) = 100 km/h), with variations in the predicted wear rate calculated as follows:  
\[S_{x}=\frac{\Delta m/m}{\Delta x/x} \tag{14}\]  
Here, \(S_{x}\) denotes the sensitivity coefficient of parameter \(x\) (dimensionless), \(\Delta m\) represents the variation in the predicted wear volume (unit: mg), m is the predicted wear volume under baseline conditions, \(\Delta x\) is the perturbation of parameter \(x\), and \(x\) is the baseline value of parameter \(x\) (where \(x\) takes the values of \(F\), \(I\), or \(v\)).  
The results, derived from the calculations, are presented in Table 3.  
As shown in Table 3, the sensitivity coefficient of the current intensity \(I\) (0.97) exceeds that of the contact pressure \(F\) (0.79) and sliding speed \(v\) (0.93), indicating that the model is most sensitive to current perturbations. When the current fluctuates in practical scenarios, the model outputs may exhibit significant deviations, reflecting poor anti-interference capabilities for this parameter. Secondly, the low sensitivity of the contact pressure \(F\) suggests strong model robustness to withstand moderate perturbations. Using average sensitivity \(<\)1 as the benchmark for anti-interference capabilities, all parameters (\(I\), \(F\), \(v\)) meet the criterion. Overall, the model demonstrates strong anti-interference capabilities and good stability.
# Situational Awareness and Problems of Its Formation in the Tasks of UAV Behavior Control
## 2 UAV as a Controllable Dynamic System Operating under Uncertainty Conditions
### Requirements for UAV Behavior Control Algorithms
In this regard, it is reasonable to specify the components of the elements explicitly \(\[PERSON]^{CS}\), \(\[PERSON]^{AS}\) and \(\Phi^{IS}\):  
\[\[PERSON]^{CS}=\[PERSON]^{CS}_{SA}\cup\[PERSON]^{CS}_{CF},\quad\[PERSON]^{AS}=\[PERSON]^{AS}_{SA}\cup \[PERSON]^{AS}_{CF},\quad\[PERSON]^{IS}=\[PERSON]^{IS}_{SA}\cup\[PERSON]^{IS}_{CF}, \tag{16}\]  
where \(\[PERSON]^{CS}_{SA}\), \(\[PERSON]^{AS}_{SA}\), and \(\[PERSON]^{IS}_{SA}\) are sets of algorithms forming situational awareness; and \(\[PERSON]^{CS}_{CF}\), \(\Phi^{AS}_{CF}\), and \(\Phi^{IS}_{CF}\) are sets of algorithms forming control actions on UAV. Our article is mainly devoted to the formation of the approach to obtaining the elements \(\Phi^{CS}_{SA}\), \(\Phi^{AS}_{SA}\), and \(\Phi^{IS}_{SA}\). No less important, of course, is the problem of forming elements \(\Phi^{CS}_{CF}\), \(\Phi^{AS}_{CF}\), and \(\Phi^{IS}_{CF}\) that provide the generation of control actions for the UAV. In our opinion, the most promising approach for solving this problem is the approach based on machine learning methods, in particular, reinforcement learning methods and deep learning methods [77; 78; 79; 80]. This problem, however, requires separate consideration. Besides the elements \(\Phi^{AS}\) and \(\Phi^{IS}\), the definitions (6) and (7) also include \(\Psi^{AS}\), \(\Gamma^{AS}\), \(\Psi^{IS}\), \(\Gamma^{IS}\), \(\Omega^{IS}\), and \(\Sigma^{IS}\) elements. They provide adaptability and learnability of \(\Phi^{AS}\) and \(\Phi^{IS}\) elements, and goal adjustments and generation in the \(\mathbb{IS}\) system. The creation of all these elements is a highly complex problem, which also requires separate consideration. In addition to the above elements and the problems associated with their formation, we should mention another problem related to the behavior control of the CS, AS, and \(\mathbb{IS}\) class systems, including UAVs. Although it plays a supporting role in terms of controlling the behavior of the system in question, this problem is no less important than those mentioned above. We are talking about the problem of forming models of UAV behavior and models of the environment of UAV functioning, to provide the solutions to problems of making control decisions, and to problems of perception of the situation and assessment of the situation. We considered this problem and possible approaches to its solution in [72; 76].
# An Axial Compression Transformer for Efficient Human Pose Estimation
## 4 Results
### Ablation Study  
To investigate the impact of the pooling kernel size k in the Local Enhancement Module on both prediction accuracy and computational cost, we conducted ablation experiments on the MPII dataset. The aim was to identify the optimal balance between resource consumption and model performance. As shown in Table 4, we varied the matrix compression size during the pooling stage within the Local Enhancement Module. The results demonstrate that as the pooling kernel size k decreases, both the computational cost and the parameter count gradually increase, accompanied by a corresponding improvement in the performance metrics. Specifically, when k = 4, \(GFLOPs^{T}\) is reduced by 45.3% with a 0.9% decrease in accuracy. When k = 3, \(GFLOPs^{T}\) is reduced by 43.8% with a 0.4% decrease in accuracy. When k = 2, \(GFLOPs^{T}\) is reduced by 39.0% with a 0.2% decrease in accuracy. Based on this analysis, we conclude that k = 2 achieves the optimal balance between computational cost and accuracy, representing the most favorable combination.  
To verify the influence of different compression degrees in axial compression on the results and obtain the optimal compression size, ablation experiments were conducted  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **k** & **\#Params** & **GFLOPs** & **GFLOPs\({}^{\textbf{T}}\)** & **Mean** \\ \hline TokenPose-L/D6 & - & 21.49 M & 11.85 & 0.64 & 90.2 \\ ACPose-L/D6 & 4 & 21.49 M & 11.56 & 0.35 (\(-\)45.3\%) & 89.4 (\(-\)0.9\%) \\ ACPose-L/D6 & 3 & 21.53 M & 11.57 & 0.36 (\(-\)43.8\%) & 89.8 (\(-\)0.4\%) \\ ACPose-L/D6 & 2 & 21.66 M & 11.60 & 0.39 (\(-\)39.0\%) & 90.0 (\(-\)0.2\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different size pooled cores on MPII datasets.  
Figure 4: Experimental comparison of ACPose and TokenPose methods.  
on the MPII dataset. The experiments mainly focused on adjusting the parameter h in the axial compression module to investigate the impact of the compression degree on the performance metrics. As shown in Table 5, as h decreases, the compression degree gradually increases, the computational cost and the parameter amount gradually decrease, and the accuracy metrics remain basically consistent. Specifically, when h is 36, \(GFLOPs^{T}\) is reduced by 10.9%; when h is 24, \(GFLOPs^{T}\) is reduced by 29.7%; and when h is 12, \(GFLOPs^{T}\) is reduced by 39.0%. Based on the above analysis, we believe that when h is 12, a balance between computational cost and performance is achieved, making it the optimal compression choice.
## 3 Results and Discussion
### Impedimetric Measurement of Cell Growth on the rGO-AuNP/ITO Electrode
{}_{Cells}\) and \(\left|\right.Z\left|\right. {}_{No\ Cells}\) are the impedance magnitudes with and without cells, respectively. Figure 5: Fluorescent microscopic images of HEK293/GFP cells cultured for 40 h on **(a)** bare ITO, (b) AuNP/ITO, (c) rGO–AuNP/ITO (scale bar: 100 μm), and (d) fluorescent intensity of the cells on each electrode (column: Average, bar: Standard deviation, \(n=4\)). The data are presented as averages (symbol) and standard deviation (bar) (\(n\) = 4). When the cells adhered to the electrode surface in the beginning of the cultivation (<20 h), the impedance values were weakly increased for all electrodes and a difference in values between each group of electrodes was not observed. However, the value of the cells on the rGO-AuNP/ITO electrode increased significantly when compared to the bare, rGO-, and AuNP-deposited ITO electrodes after 20 h of cultivation time dominated by cell proliferation. The normalized impedance magnitude of the cells then reached a saturation level (>52 h) when the cells proliferated sufficiently on the limited electrode surface. The saturated \(\mid\)\(Z\)\(\mid\)\({}_{norm}\) of the rGO-AuNP/ITO electrode was 3.44 \(\pm\) 0.16 at 54 h cultivation, while the value of bare, AuNP-, and rGO-deposited ITO electrode was 2.48 \(\pm\) 0.15, 2.61 \(\pm\) 0.18, and 3.01 \(\pm\) 0.25, respectively. The results indicated that the HEK293/GFP cells adhered and grew more efficiently on the rGO-AuNP-deposited ITO electrode when compared to the  
Figure 6: (**a**) Impedance magnitude without or with HEK293/GFP cells cultured for 48 h on the bare or rGO–AuNP-deposited ITO electrode with respect to frequency, (**b**) Normalized impedance magnitude at 21.5 kHz measured during cell culture on the bare, AuNP-, rGO-, and rGO–AuNP-deposited ITO electrodes (symbol: average, bar: standard deviation, \(n\) = 4). bare AuNP- or rGO-deposited ITO electrode. The higher saturated value of cell impedance makes it possible to implement the impedimetric cell-based assay with a broader measurement range. Thus, the rGO-AuNP-deposited ITO electrode can be utilized for label-free and real-time cell-screening assays with a wider dynamic range.
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results
### Performance Evaluation
#### 4.1.2 Indoor Coverage and Signal Propagation
The public service was already unavailable in some parts of the university complex (buildings A, B, and C). In other parts, the service was on the border of the measuring device's sensitivity. The heat map shows cold signal places throughout the whole building. Further, the average loss rate on this floor increased to 10% for the public network. This is the border value for most of the current applications [64]. Figures 13 and 14 provide results for both networks on the first floor. We experienced very deteriorated communication conditions. The building C is in the basement (below ground level). Other floors are above ground. For this reason, the building C shows the worst results. The private network RSSI ranged from \(-\)113 to \(-\)117 dBm (with no signal in the upper-right corner of the building C--marked as N/O). In the other parts of the campus, the RSSI values ranged from \(-\)89 to \(-\)115 dBm. The parameters were stable, and the loss rate increased to 5%, which still allows using it in most of the metering applications requiring (\(>\)90%) availability [64]. However, the public service was unavailable in the building C, and most of the other parts were on the border of sensitivity, close to \(-\)120 dBm. The loss rate grew to 12%, which is unacceptable for most of the current communication applications. The lowest floor, where very deep indoor conditions were experienced, shows the most significant differences between the private and the public network. Both networks show significantly decreased service quality. Figure 7: Private network coverage and signal propagation on the seventh floor. Figure 8: Public network coverage and signal propagation on the seventh floor. Figure 9: Private network coverage and signal propagation on the fifth floor. Figure 11: Private network coverage and signal propagation on the third floor. Figure 10: Public network coverage and signal propagation on the fifth floor. Figure 12: Public network coverage and signal propagation on the third floor. Figure 13: Private network coverage and signal propagation on the first floor. The presented results illustratively demonstrate the specifics of the public network's coverage. The deep middle of the buildings is mostly poorly covered, while the edges feature a higher RSSI benefiting from multiple gateways located around. Meanwhile, our results show that it is possible to cover the whole complex by one single private gateway. For use cases with a higher number of end-nodes, the multiple-gateway solution should be used, i.e., we could add another gateway to the building A or C to improve the network parameters. This shows another advantage of the private network. We can add more gateways to boost the performance where and when needed. Unfortunately, this level of flexibility is hard to achieve when being served by a public network.
## 2 Preliminaries  
### Notations  
We write \(\log_{q}\) to represent the logarithm to the base \(q\), and simply log when the base is \(q=2\). We denote the natural logarithm by \(\ln\).  
We use bold lowercase letters (e.g., \(\mathbf{v}^{n}\)) for vectors and bold uppercase letters for matrices (e.g., \(\mathbf{M}^{m\times n}\)). We may drop the dimension in the superscript whenever it is clear from the context. Sometimes, we represent a matrix as a vector of column (vectors) (e.g., \(\mathbf{M}^{m\times n}=[\mathbf{m}_{1}\mathbf{m}_{2}\ldots\mathbf{m}_{n}]\) where each \(\mathbf{m}_{i}\) is an \(m-\)length vector). The \(i\)th co-ordinate of \(\mathbf{v}\) is denoted by \(v_{i}\).  
Given a vector \(\mathbf{x}=\sum_{i=1}^{n}x_{i}\mathbf{m}_{i}\) with \(x_{i}\in\mathbb{Q}\), the representation size of \(\mathbf{x}\) with respect to \(\mathbf{M}\) is the maximum of \(n\) and the binary lengths of the numerators and denominators of the coefficients \(x_{i}\).  
We denote the volume of a geometric body \(A\) by \(\operatorname{vol}(A)\).
# A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
## 3 Database Model  
This section describes the spatio-temporal database model that has been designed to efficiently store and manage data represented at different spatial scales and changing over time, while being able to keep track of the source and quality of the single attributes. The implementation of the database model is based on the free and open-source relational database management system PostgreSQL. It supports most SQL constructs, including sub selects, transactions, and user-defined types and functions as well as many standard data types, including date/time types. For spatial functions, the PostGIS extension is applied. It adds support for geographic objects to the PostgreSQL database. PostGIS follows the OpenGIS Simple Features Specification for SQL, with version >2.0 supporting both vector and raster objects and any related spatial query capabilities. Also, topological models are supported to handle objects with shared boundaries. To deal with the multi-representation of spatial objects, a bottom-up approach is followed in which datasets of different resolutions are linked by using additional attributes, which identify the corresponding objects in the lower levels of detail. A bi-temporal representation of time has been implemented, where a trigger function allows database transactions to be logged along with the transaction time and optional additional information for user-defined tables and attributes. The temporal support has been implemented on the spatial object level to allow for a more detailed description of the temporal variability within objects with a finer granularity.  
The database model is structured into three schemas. The object schema (Figure 2 left) contains the main description of the database objects, their spatial reference, and representation at different resolutions. In addition to the spatial properties, the object details are characterized by attribute and qualifier values, which are defined within the taxonomy schema (Figure 2 right). The history schema (Figure 3) holds the general structure for logging database transactions and provides, therefore, the transaction time component of the bi-temporal data model. In the following, the taxonomy implementation, the multi-resolution spatial support, and the multi-temporal extension are explained in further detail.
## 7 Calculation of the Pyroelectric Effect for a Waveguide in Lithium Niobate  
We will calculate the electric voltage arising on the faces of the Y-splitter under the influence of the pyroelectric effect when the temperature of the crystal changes by 1 K. In a first approximation, we will consider a Y-splitter as a flat capacitor, the capacitance of which is determined by the relation \(C~{}=~{}\varepsilon_{33}\varepsilon_{0}S/d\), where \(\varepsilon_{33}\) is the relative permittivity in the Z-direction and \(d\) is the width of the sample in the Z-direction. For lithium niobate, \(\varepsilon_{33}\) = 30 [13] for low frequency voltage applied. It should be noted that in the calculations of microwave devices and optical devices, \(\varepsilon_{33}\) decreases to a value of about 5 units based on the value of the refractive index for lithium niobate. The product of \(\varepsilon_{33}\) and the dielectric constant \(\varepsilon_{0}\) gives the absolute dielectric constant of lithium niobate in the system of SI units, measured in \(F/m\).  
The charge that appears on the faces of the Y-splitter under the influence of the pyroelectric effect is calculated by the formula \(Q~{}=~{}\gamma\cdot\Delta T\cdot S\), where \(\Delta T\) is the change in temperature of the sample and \(S\) is the area of the side face. Then the ratio \(U~{}=~{}\gamma\cdot\Delta T\cdot d/\varepsilon_{33}\varepsilon_{0}\) is true for the voltage that arises on the faces of the Y-splitter. For \(\Delta T\) = 1 K, we will obtain the voltage value at the faces of the integrated optical circuit phase modulator \(U~{}=~{}5.5\cdot 10^{2}~{}V/K\).  
Thus, when the temperature of the Y-splitter changes to 1 K, a voltage of 550 V appears on its faces. In this case, the electric field strength \(E\) in the crystal can reach 1760 V/cm. When the crystal temperature changes by 100 K, which occurs at standard thermal cycles, the electric field strength can reach \(E\) = 1.76\(\cdot 10^{5}\) V/cm and lead to change of waveguide refractive index.  
The \(\Delta n_{pyro}\) caused by this electric field is calculated by formula \(\Delta n_{pyro}~{}=~{}-\frac{1}{2}r_{33}n_{E}^{3}\approx 0.003\) for \(\Delta T\) = 100 \({}^{\circ}\)C. For waveguide \"shutdown\" \(\Delta n=n_{2}-n_{1}\) need to achieve \(\Delta n_{crit}\) = 0.00085. In this case decrease of waveguide refractive index need to be \(n_{2}-(n_{1}+\Delta n_{crit})=0.00915\), but pyroelectric effect can give only 0.003. It has the same order of magnitude than \(\Delta n_{crit}\), but 3 times lower than we need to have for waveguide \"shutdown\". Now we have no exact explanation for this effect, but it could be connected with electrodes deposited near to the waveguide and redistributing the charges near these electrodes. Also, the pyroelectric coefficient of near-surface layer could be higher because of the difference of composition and properties between surface layer and bulk crystal. Additionally, \(\Delta n_{crit}\) calculations are very simple and do not take account of real refractive index distribution.
## 3 Variable Deployment Modeling Method
### Step \(\blacklozenge\): Create Variable Deployment Model  
After understanding which variants exist and which components and relations must be present in each variant, the Variable Deployment Model can be created. A Variable Deployment Model is a deployment model that has at least one conditional component or relation, which is only present if attached conditions, so-called _Variability Conditions_, are fulfilled. As a starting point, the Variable Deployment Model is developed for one variant and then incrementally extended with components and relations of other variants. During this process, conditions are assigned to these components and relations to ensure that they are only present in the respective variants. Variability Conditions are expressions based on _Variability Inputs_. In our motivating scenario, there is a single Variability Input \"mode\" that specifies if the application should be deployed for development or production. In contrast, in a more complex scenario, there could be various Variability Inputs, such as the legal location of the company that is responsible for this deployment or the current prices of cloud offerings. Thereby, the legal location might be used to select a compliant data storage while the prices are used to select the most cost-efficient deployment variant. Thus, Variability Inputs can be combined arbitrarily in complex conditions to model deployment variants that are highly dependent on the current context.  
Figure 2: Overview of the Variable Deployment Modeling Method and its five steps.
# Flowering Stage and Daytime Affect Scent Emission of _Malus ioeensis_ \"Prairie Rose\"
## 3 Discussion
### The Release Dynamics of the Floral Scent of M. ioensis \"Prairie Rose\"  
The release characteristics of aromatic compounds in different plants during flower development may be different. Studies have found that in order to maximize pollination opportunities, most flowers often begin to produce aroma when they are ready to receive pollen and the insects involved in pollination are most active [37; 38]. When the flowers are completely open, the highest number of different types and maximum amounts of volatiles are released, and after pollination, the aromas gradually weaken until they disappear [39]. In the current study, in the flowering stage of _M. ioensis_ \"Prairie Rose\", the flower compounds in the bud stage and the late flowering stage were more similar than that in other stages (dissimilarity 0.27), and the flower compounds in the initial flowering and flowering periods were the most similar (dissimilarity 0.21). \(\alpha\)-Cedrene, 2-(4-methoxyphenyl)ethanol, geranylacetone, and phenylethanol, played a major role in the four flowering stages.  
The type of plant pollinator also affects the composition of the diurnal and nocturnal floral scent release. In general, a flower that is mainly pollinated by day pollinators (such as bees) will produce more scent during the day; by contrast, flowers that rely mainly on moths for pollination will produce more volatile compounds at night [40; 41; 42]. _Malus_ species are mainly pollinated by bees, and flowers are generally in the best pollination state at approximately 10 am [43]. Our results showed that the floral scent release reached its peak between 10:00-12:00, which is consistent with the pollination biological characteristics of this species. The main volatile compounds of _M. ioensis_ \"Prairie Rose\" flowers, i.e., methylheptenone, phenylethanol, and 2-(4-methoxyphenyl)ethanol, were strongly released during the day to attract daytime pollininating insects.