esa-sceva/satcom-chunk-collection huggingface dataset:
# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 5 Results
### Compare the Location Inference Based on Images and Text Instructions  
We evaluated whether GeoLocator could improve its performance in inferring the location of street-view images by providing additional image perspectives or textual prompts when the inference of location is not at street level. In both experiments, whether an additional image was added or there was an additional textual prompt, the model's inference accuracy increased. To assess GeoLocator's proficiency in inferring locations from images captured at various angles of the same site, we conducted a series of 10 experiments in which each scenario could potentially deduce a street-level location, using Taipei 101 as a representative example. It did not give us an expected answer at first, and we gave the model another image taken from the same place but at a different angle. Then GeoLocator did successfully infer the place where the photo was taken. With additional information, GeoLocator is able to infer the location more precisely.  
To evaluate the accuracy of location information identified by inputting additional text prompts, we tested GeoLocator's performance. We chose to highlight the USC University Park Campus case as an example, where each scenario could potentially deduce a street-level location as well. After we uploaded an image representing the campus, GeoLocator first observed the environment in the picture and inferred that it was in a temperate Mediterranean climate area. The people in the picture were dressed lightly and casually, and the activities looked like they were happening on a campus. Based on these clues, GeoLocator concluded that the picture was shot at a university in the United States. Having received this unsatisfactory answer, we gave the model three prompts until it gave us a street-level answer. In this situation, with richer information, GeoLocator showed better performance than relying on image input alone. This multimodal approach enabled GeoLocator to infer and understand the content of images, including specific details like road signs and street names, more effectively. The advantage of this multimodal analysis is that it is not just a simple overpay of different modalities of information, but rather a deep understanding and analysis of the interrelationships between these diverse types of data, leading to more comprehensive and precise conclusions. In this situation, with more information, GeoLocator performed better than when only given input of images, being able to infer more images with specific details like road signs and street names.
## 2 Materials and Methods
### Multicollinearity Assessment of Predictor Variables
#### 2.6.2 Variance Inflation Factor (VIF) and Tolerance Analysis  
To assess multicollinearity among the 14 landslide conditioning factors used in the machine learning models, we conducted a Variance Inflation Factor (VIF) and Tolerance analysis. While Pearson correlation assesses linear pairwise relationships, VIF and Tolerance provide a more comprehensive evaluation of inter-variable redundancy in multivariate contexts. VIF quantifies the extent to which the variance of a regression coefficient is inflated due to multicollinearity, with values above 10 typically indicating problematic collinearity. Tolerance, defined as the inverse of VIF, provides a complementary measure of variable independence; values below 0.1 are generally considered concerning.  
As summarized in Table 2, all predictor variables had VIF values below the critical threshold of 10 and Tolerance values above 0.1, suggesting that multicollinearity does not pose a serious concern in this study. The highest VIF was observed for distance to fault (7.787), followed by elevation (5.618) and peak ground acceleration (PGA) (4.937). These moderate values likely reflect the shared geophysical characteristics of terrain and seismic  
Figure 10: Pearson correlation matrix.  
proximity. In contrast, most topographic and environmental variables, including slope, TWI, SPI, NDVI, LULC, and aspect, exhibited VIFs close to or below 2, indicating low collinearity and strong statistical independence. The combined use of VIF and Tolerance not only verifies the robustness of our feature set but also ensures that redundant predictors do not undermine the reliability of model interpretation or predictive stability.
## 5 Experimental Results and Discussion
### Dual-Robot Collaborative Grinding Experiment
#### 5.2.1 Longitudinal Collaborative Movement  
In this scenario, the workpiece is set to a horizontal clamping state, and the grinding trajectory is designed according to the shape of the workpiece surface. Moreover, the grinding trajectory is decomposed into the X-direction and the Z-direction, which are input to the two manipulators, respectively. Here, the motion trajectories set for the two manipulators are as follows:  
\[\begin{split}\text{manipulator 1:}\ Z_{1}=0.01\times\sin(0.01 \times\pi(t-2))+0.0015(t-2)\\ \text{manipulator 2:}\ Y_{2}=0.00195(t-2),\ Z_{2}=0.0015(t-2)\end{split}\]  
In this experiment, a total of 203 trajectory points were planned, and the movement time of each desired position was 0.5 s, so the cooperative movement time of the two robotic arms was 101.5 s.  
After the experiment, for manipulator 1 clamping the workpiece, the movement in each desired direction is shown in Figure 5:  
In the process of the Z-direction, the motion error generally shows a trend of first rising and then decreasing. The error reaches the maximum in about 60 s, and the maximum error is 2.079 mm. After 60 s, the error gradually shows a decreasing trend, and the average error in the whole process is 0.99 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Manipulator 1** & **Manipulator 2** \\ \hline
**Stiffness matrix** & \(K\) & \(K_{1}=diag(1200,1200,1200,20,20,20)\) & \(K_{2}=diag(1200,1200,1200,20,20,20)\) \\ \hline
**Damping matrix** & \(D\) & \(D_{1}=diag(69,69,69,9,9,9)\) & \(D_{2}=diag(85,85,85,9,9,9)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The impedance parameters selected by the two manipulators.
## 2 Materials and Methods  
### Materials  
The white wheat flour (WF, type 550) was purchased from Dizing SRL, Brusturi, Neamt County, a local milling and breadmaking company. It was not treated with ascorbic acid or other improvers.  
The rosehip (_Rosa canina_ L.) fruits were collected from the Dofteana area, Bacau County, at the maturity stage to benefit from the higher content of bioactive compounds. After discarding the achenes (the real fruits) containing the seeds, the dried sepals, and the remainder of anthers and filament, the hypanthium (fleshy shell or pulp) was dried in the dark at atmospheric conditions to avoid the loss of vitamins. The dried pulp of rosehip fruits was ground by an ultra-centrifugal laboratory mill, ZM 200 Retsch (18.000 rpm,4 min) to obtain the Rp, then was sieved to select the particles around 180 \(\upmu\)m in size, similar to wheat flour granularity. The obtained Rp was stored in airtight brown glass jars in the dark and was kept cold until use.  
The Rp additions were selected based on the regular supplementation of wheat flour with ascorbic acid and the vitamin C content of Rp. Usually, the wheat flour is supplemented with 2 to 10 g ascorbic acid per 100 kg flour (10 mg/100 g), where the higher quantities in the interval are used for flour with lower quality indicators. Calculations based on the vitamin C content of Rp, just before the addition to flour (420 \(\pm\) 16.09 mg/100 g), served to select the Rp addition levels used in the research, which were 0.5-2.5%.  
Other ingredients used for breadmaking, i.e., salt and compressed yeast, were purchased from the local market (Bacau, Romania). Pakmaya compressed yeast was produced by Rompak SRL, Pascani, Iasi County, Romania.  
Ascorbic acid was purchased from Enzymes & Derivates, Costisa, Neamt County, which is a local supplier of ingredients for the food industry.  
Chemicals and reagents, such as ethanol (C\({}_{2}\)H\({}_{5}\)OH, with a minimum of 95% \(w\)/\(w\)), concentrated sulphuric acid (98%), copper sulphate (CuSO\({}_{4}\cdot\)5H\({}_{2}\)O), boric acid (H\({}_{3}\)BO\({}_{3}\)), chlorhydric acid, and borax (Na\({}_{2}\)B\({}_{4}\)O\({}_{7}\cdot\)10H\({}_{2}\)O) of an analytical grade were purchased from Merck (Darmstadt, Germany). Calcium chloride, natrium hydroxide, natrium chloride, and phenolphthalein of an analytical grade were purchased from Sigma-Aldrich (Steinheim, Germany).
## 3 Experimental Study
### Experimental Setup
#### 3.1.4 Sliding Time Window Processing  
In time-series-based prognostic problems, temporal information is essential for RUL prediction. Since temporal sequence data are hypothesized to include more information about the current degradation state than single-time step data, this study adopted a sliding time window strategy to exploit temporal information.  
The time window was created using a single step sliding over the data with a specific time window length. If \(L\) is the number of engine cycles until failure and \(tw\) is the time window length, then \(L-tw+1\) samples can be extracted from engine data. The sample at time \(t\) can be written as follows:  
\[x_{t}=[x_{t-tw+1},x_{t-tw+2},\ldots,x_{t}]\text{ where }\ t=tw,\ldots,L \tag{14}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Range** \\ \hline Number of epochs & \((20,25,30,40)\) \\ Batch size & \((64,128,256)\) \\ Number of nodes in hidden layer & \((5,7,10)\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter values evaluated in the autoencoder network.  
Figure 7: Extracted sensor signals (**a**) 100 engines for training dataset; (**b**) 100 engines for test dataset.  
As stated in [9; 36], a longer time window yields better performance by including more degradation information. However, a longer time window increases the computational complexity. Here, the window size was set to 30 to not exceed the cycle length of the engine with the shortest cycle length in the test dataset. The time windows were designed to overlap, and the step size was set to 1. Sliding time window processing is shown in Figure 8. Using the given window length and step size, 17,731 training samples were obtained. During the testing and training phases, the last sensor measurement data of the last time window was used for the RUL prediction of each engine. As the number of engines in the test data set for FD001 was 100, we had 100 samples of 30-step time windows to test the prediction model.
# A Study on the Feasibility of Natural Frequency-Based Crack Detection
## Appendix A Critical Load Solution from LEFM: For Brittle Metals  
Figure 1 shows the configuration of a common fracture toughness test specimen featuring a single-edge crack. The solution of critical load \(P_{c}\) is derived from the solution of stress intensity factor \(K\). \(K\) is related to tensile stress \(\sigma\) and crack length \(c\) and can be expressed as follows [25]:  
\[K=F(c/a)\sigma\sqrt{\pi c} \tag{1}\]  
where \(F(c/a)\) is a dimensionless function depending on the geometry of the cracked plate. From Equation (1),  
\[\sigma=P/(at)=K/\left[F(c/a)\sqrt{\pi c}\right] \tag{2}\]  
where \(P\) is the tensile load as shown in Figure 1. Fracture toughness \(K_{c}\) varies with the thickness \(t\) of the specimen tested. When increasing thickness beyond a critical value, plane strain conditions dominate and the fracture toughness remains the same. This is the so-called plane strain fracture toughness \(K_{lc}\). A semi-empirical equation that relates fracture toughness \(K_{c}\) of a specimen of arbitrary thickness with the plane strain fracture toughness \(K_{lc}\) is presented by [PERSON], et al. [74]:  
\[K_{c}=K_{lc}\sqrt{1+\frac{1.4}{t^{2}}\left(\frac{K_{lc}}{\sigma_{0}}\right)^ {4}} \tag{3}\]  
where \(\sigma_{0}\) is yield strength.  
For a given metal, the critical load \(P_{c}\) that causes brittle fracture can be obtained by referring to the value of plane strain fracture toughness \(K_{lc}\) in the literature, converting \(K_{lc}\) to \(K_{c}\) using Equation (3), then substituting \(K_{c}\) into Equation (2), which yields the following:  
\[P_{c}=K_{c}at/\left[F(c/a)\sqrt{\pi c}\right] \tag{4}\]  
In the current study, when deriving the solution of \(P_{c}\) for brittle metals, the dimensions of the specimen (i.e., thickness, minimum crack length and the minimum width of the uncracked ligament) are set to be over \(2.5{(K_{lc}/\sigma_{0})}^{2}\), thus the plane strain condition is satisfied and \(K_{lc}\) can be substituted into Equation (2) directly. However, when deriving the solution of \(P_{c}\) considering small-scale yielding (as described in the next section), the plane strain condition may not be satisfied, thus Equation (3) should be used to convert \(K_{lc}\) to \(K_{c}\).  
\(F(c/a)\) in Equation (4) is usually an empirical formula determined through various methods such as polynomial fitting to exact values, interpolation, asymptotic approximation, etc. For the single-edge crack plate, the formulas of \(F(c/a)\) derived using different methods can be found in the handbook by [PERSON], et al. [25]. The formula used in the current study is reproduced as follows:  
\[F\left(\frac{c}{a}\right)=\sqrt{\frac{2a}{\pi c}tan\frac{\pi c}{2a}\frac{0.75 2+2.02\left(\frac{c}{a}\right)+0.37\left(1-sin\frac{\pi c}{2a}\right)^{3}}{cos \frac{\pi c}{2a}}} \tag{5}\]  
The error of Equation (5) is below 0.5% for any \(c/a\).
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
## 1 Introduction
The influence of environmental conditions was tested on operators already described in the literature. The operators were implemented for focus estimation on the stack of images. Based on a literature review and preliminary research, 18 operators (digital filters) were classified. The use of operators was conducted by [PERSON] [17] based on simulated scenes and images from a low-resolution camera. The stacks contained between 25 and 51 images. A study of operators in the field of focus evaluation was also carried out by  
Figure 1: Method for changing the position of the image plane in the SFF: (**a**) classical method—camera movement, (**b**) proposed method—use of a tunable liquid lens. [PERSON] [18]. The authors simulated surface shapes in the form of a plane surface, a cone, and a sine wave. In addition, they recorded images of a hat, a doll, and a cup. [PERSON] [19] used focus estimation operators on several partial-focus images to generate an image in focus over the entire distance range. A set of operators was selected and tested based on the literature analysis for potential use in industrial applications. The operators were selected based on the analysis of the available results and the described computational complexity. Computational complexity is essential for implementing a solution in a real-time system. Cited papers related to studies performed under laboratory conditions. [PERSON] [20] described the use of the SFF method for imaging of a grinding wheel in a microscopic setup. [PERSON] [21] presented an algorithm for face reconstruction using the SFF method. The influence of the applied operator and the window size was also described. However, the cited articles did not reach the functional prototype stage; they were proofs of concept. The literature has also addressed issues related to the topic of autofocus [22; 23; 24; 25]. Another approach not covered in this paper is blind image quality assessment. Such algorithms are image assessment techniques based on a pre-prepared model. Such algorithms allow image quality assessment without recalibrating the model for each new scene. However, this approach only provides a single rating for the entire image and cannot be used to select and assess specific regions of interest, which is required by the SFF method [26; 27]. This article differs from the cited publications by imaging the surfaces of actual industrial products. Recorded images have great metrological potential due to the 5 MPx resolution of the sensor. A dedicated camera for industrial solutions was used. A test stand was constructed to allow reproducible algorithm testing using different industrial light sources. A controllable tunable liquid lens was used to change the position of the image plane. The paper identifies estimators that allow practical focus estimation in the presence of interference in SFF imaging. Discussed interferences were not digitally simulated. They were introduced through a modification of industrial illuminators or acquisition parameters.
## 2 Materials and Methods
### Differential Interference Contrast (DIC) and Fluorescence Microscopic Imaging  
Differential interference contrast (DIC) and fluorescence microscopic imaging were used in the study for observing the morphology and evaluating the viability of the cells with and without cordycepin treatment. First, three experimental groups of HeLa cells were acquired and washed with phosphate buffered saline solution (PBS) three times to remove the cell culture medium. Propidium iodide (PI, Beyotine Institute of Biotechnology, Haimen, Jiangsu, China), a red-fluorescent nuclear staining dye, was used to stain to HeLa cells under 37 \({}^{\circ}\)C for 15 min. The samples were then observed and imaged under a spinning disk confocal microscopy system (Dragonfly200, Andor Technology Ltd., Belfast, Ireland). Both DIC images and fluorescence images were acquired using this system.
# Explainable AI-Enhanced Human Activity Recognition for Human-Robot Collaboration in Agriculture
###### Abstract  
This study addresses a critical gap in human activity recognition (HAR) research by enhancing both the explainability and efficiency of activity classification in collaborative human-robot systems, particularly in agricultural environments. While traditional HAR models often prioritize improving overall classification accuracy, they typically lack transparency in how sensor data contribute to decision-making. To fill this gap, this study integrates explainable artificial intelligence, specifically SHapley Additive exPlanations (SHAP), thus enhancing the interpretability of the model. Data were collected from 20 participants who wore five inertial measurement units (IMUs) at various body positions while performing material handling tasks involving an unmanned ground vehicle in a field collaborative harvesting scenario. The results highlight the central role of torso-mounted sensors, particularly in the lumbar region, cervix, and chest, in capturing core movements, while wrist sensors provided useful complementary information, especially for load-related activities. The XGBoost-based model, selected mainly for allowing an in-depth analysis of feature contributions by considerably reducing the complexity of calculations, demonstrated strong performance in HAR. The findings indicate that future research should focus on enlarging the dataset, investigating the use of additional sensors and sensor placements, and performing real-world trials to enhance the model's generalizability and adaptability for practical agricultural applications.  
XGBoost classification; SHapley Additive exPlanations (SHAP); feature importance; field experiment; material handling tasks; wearable sensors; multi-sensor information fusion; human factors +
Footnote †: journal: Applied sciences  
Article
# An Overview of Emergency Communication Networks
###### Abstract  
In recent years, major natural disasters and public safety accidents have frequently occurred worldwide. In order to deal with various disasters and accidents using rapidly deployable, reliable, efficient, and stable emergency communication networks, all countries in the world are strengthening and improving emergency communication network construction and related technology research. Motivated by these situations, in this paper, we provide a state-of-the-art survey of the current situation and development of emergency communication networks. In this detailed investigation, our primary focus is the extensive discussion of emergency communication network technology, including satellite networks, ad hoc networks, cellular networks, and wireless private networks. Then, we explore and analyze the networks currently applied in emergency rescue, such as the 370M narrowband private network, broadband cluster network, and 5G constellation plan. We propose a broadband-narrowband integrated emergency communication network to provide an effective solution for visual dispatch of emergency rescue services. The main findings derived from the comprehensive survey on the emergency communication network are then summarized, and possible research challenges are noted. Lastly, we complete this survey by shedding new light on future directions for the emergency communication network. In the future, the emergency network will develop in the direction of intelligence, integration, popularization, and lower cost, and space-air-ground-sea integrated networks. This survey provides a reference basis for the construction of networks to mitigate major natural disasters and public safety accidents.  
broadband-narrowband integration; emergency communication network; narrowband private network; satellite network; space-air-ground-sea integrated networks 202315595. [[https://doi.org/10.3390/rs15061595](https://doi.org/10.3390/rs15061595)]([https://doi.org/10.3390/rs15061595](https://doi.org/10.3390/rs15061595))
## 4 Contribution  
This paper aims to investigate the use of numerical simulation in conjunction with thermography for the detection of breast cancer. It studies tumors at different locations of the breast area and the effect of the physical properties of the tumor on the surface temperature of breast skin. The study is divided into two parts: the first part examines the effect of tumor heat generation on the surface temperature of the breast at different locations by using the bioheat Pennes equation [24] and COMSOL 5.3 software. The study considered a variety of thermophysical characteristics, including the size and shape of the breast, tumor size, and position within the breast. The effects of tumor properties on the breast surface temperature profiles are investigated through this work, and findings may contribute to the advancement of thermography-based breast cancer detection. The main contributions of this paper are as follows. To accommodate women's breasts in terms of size and tissue density, a large and dense tissue breast type was modeled using the extra fine mesh in COMSOL, instead of the normal mesh model used in previous studies. Additionally, a large size of the breast of approximately 17 cm was used, and thermophysical properties were analyzed. The second part uses an infrared camera and a situ-cooling gel to investigate the temperature profiles on the surface of healthy and tumorous anthropomorphic silicone breast models. The results were evaluated using the FLIR Research IRTM Toolbox. The cooling gel was used to study thermal variation and its effect to increase the detection accuracy of breast cancer at an early stage. In addition, nine different tumor locations, with different 3D cartesian coordinates to accommodate for depth and position, were studied.