esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability
## 2 In-Frame Detection Based on Saliency Histogram
### Adaptive Segmentation and Centroid Localization  
The above-introduced saliency histogram \(\chi\) reveals that the gray levels with large bin values correspond to the salient regions. For the purpose of extracting the target regions in a single frame as  
Figure 2: Schematic diagram of amplitude transformation.  
correctly as possible, we need to remove those bins with remarkably small values through a simple threshold and generate a modified saliency histogram \(\chi^{\prime}\) as follows:  
\[\chi^{\prime}(k)=\begin{cases}\chi(k)&\text{if }\chi(k)>\chi_{*}\\ 0&\text{others}\end{cases} \tag{8}\]  
where \(\chi_{*}\) is an adaptive threshold calculated by [PERSON]'s method.  
According to this modification, these removed bins stand for the smooth background and parts of the clutters. As a result, we can make a binarization for the input IR image on the basis of \(\chi^{\prime}\) directly, and a binarized image BI can be obtained from Equation (9):  
\[\text{BI}(x,y)=\begin{cases}0&\text{if }\chi^{\prime}(I(x,y))=0\\ 1&\text{others}\end{cases} \tag{9}\]  
After binarization, it is highly possible that some of the extracted regions are incomplete due to the inhomogeneity of intensity. For \(\text{BI}(x,y)\), close operation [23] is utilized to smooth the contours and fill in the holes existing in target regions as  
\[\text{BI}_{\text{c}}=\text{BI}\blacksquare\text{M}=\left(\text{BI}\bigoplus M \right)\ominus M \tag{10}\]  
where \(\text{BI}_{\text{c}}\) is the resulting image after close operation; M is a 5 \(\times\) 5 sized structuring element; \(\blacksquare\) is a close operator; \(\bigoplus\) is a dilation operator, and \(\ominus\) is an erosion operator.  
Intended to avoid forming repeated trajectories of the same target, every region only needs to be represented by a single centroid precisely. A typical fast connected components labeling algorithm [24] is applied to label all of the candidate regions. For all of the connected regions with the same label, we use a gray-weighted criterion to calculate its centroid position as follows:  
\[\text{P}=\frac{\sum_{i=1}^{N}\text{p}_{i}\cdot\text{I}(\text{p}_{i})}{\sum_{i= 1}^{N}\text{I}(\text{p}_{i})} \tag{11}\]  
where P denotes the space coordinate of the centroid; \(\text{p}_{i}\) denotes the i-th pixel in each connected region; and N is the total pixel number of the region. By this procedure, every candidate target region is represented by an isolated point, which is called candidate point in the following contents.
## 4 Experiment Results and Discussion
### Evaluation of the Normalized Slip Sinkage  
To quantitatively assess the effect of surcharge mass and the relative density of the soil on slip sinkage, we assessed normalized slip sinkage, defined as the ratio of slip sinkage to static sinkage. Table 3 shows the measured static sinkage (\(z_{0}\)) under each model experiment condition. This value was used to calculate normalized slip sinkage. Figure 10 shows the relationship between normalized slip sinkage (\(z/z_{0}\)) and slip displacement (\(j\)).  
Figure 10: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Vertical Stress, \(\alpha_{q}\) (kPa)**} & \multicolumn{3}{c}{**Static Sinkage, \(z_{0}\) (mm)**} \\ \cline{2-4}  & \(D_{r}\) = 40\% & \(D_{r}\) = 60\% & \(D_{r}\) = 80\% \\ \hline
7.0 & 1.96 & 1.37 & 1.04 \\ \hline
10.6 & 2.99 & 2.00 & 1.56 \\ \hline
14.0 & 3.75 & 2.69 & 2.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Static sinkage of model track experiments.  
As shown in Figure 10, normalized slip sinkage increased with slip displacement; similar behavior was shown under different surcharge stress and relative density of the soil conditions. Therefore, slip sinkage can be predicted relatively simply and accurately by applying the static sinkage amount of the off-road tracked vehicle assessed under specific conditions and normalized slip sinkage-slip displacement relationship presented in Figure 10. Many researchers [2; 3; 6; 7] have proposed methods to calculate off-road tracked vehicle static sinkage; the total ground sinkage (static sinkage+slip sinkage) that occurs in the soil-track interface is obtained using the proposed normalized slip sinkage (= slip sinkage/static sinkage).
## 2 Materials and Methods
### The HJA-N Model
#### 2.3.2 Hydrologic Model  
The hydrologic model is conceptually similar to models such as HBV [32] in that process descriptions involving filling and drainage of storages in the model are based on first order assumptions. While a variety of more sophisticated techniques are readily available, our simplifications are consistent with the monthly timescales of both input and output data. A complete description of the rate terms is included in Table 3. Five pools are used to define the hydrology (Table 2). These pools represent the canopy, root zone, below the root zone, instream aquatic environment, and as well as a separate storage representing the riparian/hyporheic zone.  
Interception is treated as a linearly decreasing function of canopy storage. Canopy evaporation is calculated independent of evapotranspiration and, along with canopy throughfall, it is treated as a first order loss term from canopy water storage. Evapotranspiration from the rooting zone is calculated using a simple air temperature index approach, which is limited by water content, following from [33]. The runoff generation model is comprised of two vertically-oriented storages. The storages conceptually correspond to the rooting zone and to the region below the rooting zone and above bedrock, which is considered to be impermeable. Surface soils in the region are highly permeable and surface ponding or infiltration excess overland flow has not been observed [34]. Following from these observations, modeled infiltration capacity assumed to be larger than the rainfall rate, and all throughfall enters the upper soil layer. The upper water storage feeds water vertically into the lower storage unit. Downslope flows are assumed to occur within the lower storage as saturated subsurface stormflow,  
\begin{table}
\begin{tabular}{c c c} \hline
**Term** & **Definition** & **Units** \\ \hline \(q_{{}_{f}}\) & Hydro factor for particulate export & – \\ \(p_{{}_{y}}\) & Max particulate export rate constant & day\({}^{-1}\) \\ \(q_{{}_{max}}\) & Hydro threshold & m/m \\ \(k_{{}_{2}}\) & Hydro factor & – \\ \(k_{{}_{3}}\) & Hydro factor & – \\ \(k_{{}_{w\;,\;,\;,\;}}\) & Canopy interception and evaporation rate terms & day\({}^{-1}\) \\ \(k_{{}_{w\;,\;,\;}}\) & \(k_{{}_{w\;,\;,\;}}\) & Vertical, long, and short flowpath & day\({}^{-1}\) \\ \(k_{{}_{w\;,\;,\;}}\) & \(k_{{}_{w\;,\;,\;}}\) & Hyporheic and instream exchange & day\({}^{-1}\) \\ \(k_{{}_{w\;,\;,\;}}\) & \(k_{{}_{w\;,\;,\;}}\) & Instream water retention rate constant & day\({}^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 8: \(Cont\).  
which has been demonstrated in the catchment during high input events [26]. Water exiting the lower soil zone enters the near-stream zone where exchanges between the riparian zone, hyporheic zone and surface water are depicted again using a series of first order storage terms.
## 5 Conclusions  
In this study, the main novelty is to integrate GPTransformer based on improved geographic location encoding with 1D-CNN to construct a dual-branch neural network, GPTransCNN. The experimental results demonstrate that GPTransCNN achieves the best prediction performance and maintains strong generalization ability under ten-fold cross-validation. Ablation experiments were conducted to dissect the model architecture, revealing that GPTransCNN outperformed all other variants across multiple metrics. It provided the most refined predictions of SOM spatial distribution, achieving an R\({}^{2}\) improvement of 7.26% to 44.49% compared to other component models. Uncertainty analysis further demonstrated that GPTransCNN exhibited the lowest predictive uncertainty, indicating the highest stability and robustness. These experimental results further validate that the proposed GPTransformer incorporating geographic location information, combined with the dual-branch design integrating the local feature extraction capability of 1D-CNN, can more accurately characterize the nonlinear relationships and spatial heterogeneity between environmental features and SOM.  
The SOM spatial distribution map, predicted by GPTransCNN, provides essential support for analyzing SOM variability and identifying spatial patterns within the study area. It provides data support for optimizing agricultural production, improving cultivated land quality, and facilitating refined land management. This model has good accuracy and stability, and has the potential for promotion in practical applications. In future work, this model can be extended to more complex terrain regions to enable precise SOM spatial pattern prediction, thereby better supporting sustainable land management and responses to global climate change.  
L.S.: Conceptualization, validation, writing--review and editing, and funding acquisition. Y.X.: project administration, writing--original draft, and methodology. Y.D.: visualization, data curation, and conceptualization. Y.F.: formal analysis, investigation, and visualization. Q.Z.: software, data curation, investigation, and resources. H.X.: writing--review and editing, validation, supervision, resources, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hunan Province (No.2025 JJ80038), the National Natural Science Foundation of China (No.62401203), the Key Research and Development Program of Hunan Province (No.2023 NK2026), and the Postgraduate Scientific Research Innovation Project of Hunan Agriculture University (No.2024 KKC058).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
We are grateful to the many students and staff who participated in land cultivation projects and soil sample collected but are not listed as co-authors. All individuals included in this section have consented to the acknowledgment.  
The authors declare no conflicts of interest.
## 3 Methods
### Support Vector Regression
In the case of anomaly detection, the input variables should be strongly correlated with the target because the interest is in having a reference on the expected value of the target. When the other way round, they should not be so correlated as to contain practically the same information as the target, because this would not help in highlighting the novelty hidden in the target measurements, which is given by the fault. For each target \(y\), the most appropriate input independent variables of the model were checked by constructing a cross-correlation map basing on the Pearson correlation coefficient (Equation (9)) with each possible regressor \(x\):  
\[r_{xy}=\frac{\sum_{i=1}^{N}(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum_{i=1}^{N }(x_{i}-\bar{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\bar{y})^{2}}}, \tag{9}\]  
where \(N\) is the number of observations in the training data set, \(\bar{x}\) and \(\bar{y}\) are the averages of the output and of the potential regressor \(x\) in the training data set. The selected regressors are required to have \(r_{xy}\) with the target between 0.4 and 0.98. The final choice was to keep fixed inputs with different predicted outputs: for boosting the sensitiveness of the approach only machine parameters and the ambient temperature were used to predict the temperature of the different components of the drivetrain. The ambient temperature was included to correct the bias due to seasonal variability; anyway, the contribution of this parameter was discovered to be marginal in improving the reliability of the analysis. The selected input variables and output are finally reported in Table 2.  
The flow chart in Figure 2 resumes the methodology used for the processing of the SCADA data: oil pressure and temperatures were, at first, studied with weekly averages where general information about the status of the machines was retrieved (presumably faulty or healthy). Then, a more specific analysis with machine learning approach was applied, modelling the trend of temperatures. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Inputs** & **Modelled Outputs** \\ \hline \(\bullet\) & wind speed (average and standard deviation) & \(\bullet\) & gear bearing temperature \\ \(\bullet\) & rotor speed & \(\bullet\) & generator bearing temperature \\ \(\bullet\) & blade pitch (average and standard deviation) & \(\bullet\) & main bearing temperature \\ \(\bullet\) & power output & \(\bullet\) & ambient temperature \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structure of the SVR regressions for the selected analysis.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 3 Proposed System
### Contextual Information Integration  
The final stage of the proposed system integrates user-specific contextual information with the data extracted from road sign images. This process ensures that the outputs are not only accurate but also actionable and tailored to the user's immediate needs. The integration relies on user data, such as location, speed, and destination, combined with textual and symbolic information extracted from the road sign images. A step-by-step reasoning approach, based on the CoT framework, is employed to generate meaningful outputs [8; 11; 16]. Examples of the text and contextual data extracted from road sign images, which are directly utilized in the contextual information integration process, are presented in Table 1.  
By utilizing CoT, the system combines detected objects such as \"Mokpo\" and \"27 km\" with user-specific contextual data to create dynamic and intuitive insights. For example, by integrating the current vehicle speed of 60 km/h and a destination of Mokpo, the system produces outputs such as \"You will reach Mokpo in approximately 27 min\". This approach transforms raw data into user-centric, actionable information that directly supports real-time decision making [17; 18].
# Extremality of Disordered Phase of \(\lambda\)-Model on Cayley Trees
## 4 Three-Indexed Markov Chains of the Disordered Phase
### Conditions of Non-Extremality  
In this subsection, we are going to find the regions of the parameters, the disordered phase is not extreme in the set of all Gibbs measures (including the non-translation invariant ones).  
It is known that a sufficient condition (Kesten-Stigum condition [26]) for the non-extremality of a Gibbs measure \(\mu\) corresponding to the matrix \(\mathbb{P}\) on the Cayley tree of order \(k\geq 1\) is that \(k|\alpha_{max}|^{2}>1\), where \(|\alpha_{max}|\) is the second largest (in absolute value) eigenvalue of \(\mathbb{P}\)[22; 26]. Furthermore, we are going to employ this condition for the non-extremity of the disordered phase.  
Let us consider  
\[\alpha_{\max}=\max\{|\alpha_{1}|,|\alpha_{2}|\}.\]  
Through the use of (17), we have  
\[\alpha_{\max}=\left\{\begin{array}{ll}|\alpha_{1}|,\ \text{if}\ (B-A)(C_{1}-B) \geq 0\\ |\alpha_{2}|,\ \text{if}\ (B-A)(C_{1}-B)<0,\end{array}\right.\]  
Hence, by checking the non-extremality condition, we arrive at the following result.  
**Theorem 3**.: _Assume that, for the \(\lambda\)-model given by (5), the condition (7) is satisfied. Suppose that one of the following conditions is held:_  
_(i) if_ \((B-A)(C_{1}-B)\geq 0\) _and_  
\[\frac{|C_{1}-A|}{2B+C_{2}}>\frac{1}{\sqrt{k}}\] _(ii) if_ \((B-A)(C_{1}-B)<0\) _and_  
\[\frac{|C_{2}-B|}{2B+C_{2}}>\frac{1}{\sqrt{k}},\]  
_then the disordered phase is not extreme._
## 4 Methodology
### Complexity Analysis
#### 4.3.1 Complexity Analysis of the Simulated Annealing Part  
The specific running time of a simulated annealing algorithm basically depends on the number of iteration steps. But different solving methods may differ in the convergence speed of the solution. In this work, the convergence speed of the solution can be used to shorten the steps of simulated annealing iterations. Specifically, the visible matrix \(C\) is chosen to calculate the average delay between pairs of points as the theoretical optimal solution \(\tau_{min}\), followed by selecting a numerical value as the exit threshold \(\rho\). The exit threshold \(\rho\) is a controllable parameter that balances the quality of the solution and the number of iterations. The simulated annealing process terminates when the convergence rate \(\tau_{C}\) of the current solution satisfies Equation (14). This approach can effectively reduce the number of iterations.  
\[\tau_{C}=\rho\times\tau_{min} \tag{14}\]  
The number of iteration steps of the simulated annealing algorithm only depends on the initial temperature \(T\), the decay rate of each temperature \(\Delta T\), and the final exit temperature \(\varepsilon\). If \(\mathcal{K}\) is the total number of iteration steps, then there is  
\[\mathcal{K}_{SA}=\log_{\Delta T}\varepsilon-\log_{\Delta T}T \tag{15}\]  
Therefore, the number of iteration steps is a controllable variable. The objective function of the solution is defined as the average delay of the topological network, in other words, the shortest path between all point pairs. The famous Floyd algorithm is designed to solve this problem within \(O(\mathcal{N}^{3})\). The final complexity of the simulated annealing algorithm in this work is \(O(\mathcal{K}_{SA}\mathcal{N}^{3})\).
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 4 Biomedical Applications
### Magnetic Separation (MS)  
Among all these aforementioned methods, some authors reported the use of MNPs for cell separation. Their magnetic properties cause them to be recommended for use in various fields such as biotechnology, medicine, environmental sciences, and material sciences. MNPs can be used to remove contaminants from water or soil by selectively binding to pollutants, facilitating their easy removal under a magnetic field. In material science, MS using MNPs is used for material purification and separation, especially in nanotechnology. It can be applied in various industries for the separation of waste materials, making disposal and recycling processes more efficient. MS is widely used in diagnostics, drug delivery, and bio separation processes. It can be employed to isolate specific cells, proteins, or nucleic acids. For example, a tumor cell separation technique by means of dendrimer-assisted hydrophilic magnetic nanoparticles was implemented. These sensors were able to detect tumoral cells from blood samples, achieving a cell viability of 99% and a capture efficiency of nearly 80% [43]. MS by means of MNPs is very challenging because the properties of the nanoparticles should be carefully optimized, involving high synthesis and functionalization costs. However, the advantages offered by this method are of immeasurable value: high specificity and selectivity, a non-destructive and gentle separation process, and rapid and efficient separation [184]. Table 1 summarizes different remarkable examples of MNP use in biomedical applications found in the literature. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
The progress in obtaining such nanostructures has led to the development of ones that nowadays are commercially available and serve various purposes, especially in biomedical field [200]. Many companies offer support in obtaining MNPs for drug delivery, diagnostics, and imaging applications. Merck KGaA (Darmstadt, Germany), Nano Research Elements Inc. (Dhanora Jattan, India), Fortis Life Sciences (Waltham, MA, USA), Nanografi Nano Technology (Istanbul, Turkey), Nanoshell LLC (North Salt Lake, UT, USA), Read International Corp. (Culver City, CA, USA), SkySpring Nanomaterials Inc. (Houston, TX, USA), Strem Chemicals Inc. (Newburryport, MA, USA), US Research Nanomaterials Inc. (Houston, TX, USA) are some of the companies that produce magnetic nanoparticles for the healthcare industry.
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 3 Case Study for Vienna
### Mobility Demand Forecast
#### 3.2.4 OD Matrix Generation  
To derive the actual revenue coefficients \(p_{ij}\) for each pair of sub-districts \(i,j\in V\), we compute two different Origin-Destination (OD) matrices \(\mathbf{N},\mathbf{D}:|V|\times|V|\mapsto\mathbb{R}\) for each of the data sets. The _ntrips_ matrix \(\mathbf{N}\) contains the number of trips from sub-district \(i\) to \(j\) at \(N_{ij}\), whereas the _duration_ matrix \(\mathbf{D}\) contains the total duration of all these trips as \(D_{ij}\) instead.  
Figure 5 shows plots for each of the six different OD matrices, which reveal that they broadly exhibit similar structural characteristics. The sub-district indices correspond roughly to the centrality of the respective sub-district, with lower indices indicating more central ones. All matrices show a higher number of trips (or longer total trip durations) along their main diagonal. These entries correspond to round trips that start and end within the same sub-district. Off the main diagonal, trips (and their total durations) are more evenly spread among the various sub-district pairs, albeit with some sparser areas clearly visible in any case. For the ELOOP data set, most of these empty areas correspond to those parts of Vienna that are not covered by ELOOP's current AoO. In the taxi data set, sparser areas are mostly found in the rows and columns representing outer districts. Such trips from inner to outer districts (or between outer districts lying on opposite sides of the city) appear to be too long (and thus, expensive) to be frequently made by taxi. Conversely, the density of trips and their total durations increase towards the inner districts with lower indices. The OEU data set's matrices demonstrate the most uniform distribution of trips (and their overall durations) among sub-district pairs. Despite these differences, the individual matrices \(\mathbf{N}\) and \(\mathbf{D}\) of the three data sets are structurally very similar.  
Figure 4: Duration histogram of the taxi data set.  
Figure 5: _Cont_.