esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 4 Integration of Co-Simulation Components
### Design Challenges and Solutions of CARLA--Simulator System Hardware Integration
#### 4.2.3 Bicycle Simulator Hardware Integration  
Before integration, the bicycle simulator components must first be connected to the PC via Bluetooth. Unlike standard cycling applications, this study required direct data communication with the CARLA simulation. The _pycycling_ Python package was used to establish a connection between the Tacx Neo 2T smart trainer and Elite Sterzo smart steering block, allowing the real-time transmission of speed and steering angle data to CARLA. The synchronization module then transmits these data to PTV Vissim, ensuring the controlled bicycle is represented accurately in both simulation environments.
## Materials and Methods
### Raman Spectroscopy Measurement  
A Renishaw inVia Raman spectrometer (controlled by WiRE 3.4 software, Renishaw plc, Wotton-under-Edge, UK) was used to collect the Raman spectra of the eight different human cells. It was connected to a Leica microscope (Leica DMLM, Leica Microsystems, Buffalo Grove, IL, USA), and equipped with a 532 nm laser that was focused through a 50\(\times\), NA = 0.75 objective (Leica Microsystems, Buffalo Grove, IL, USA); A standard calibration peak of 520.5 \(\pm\) 0.1 cm\({}^{-1}\) was used for the system with a silicon in a static mode. 20 \(\upmu\)L cell suspension was dripped onto MgF\({}_{2}\) wafer for Raman spectrum measurement. The Raman spectra ranged from 623 to 1783 cm\({}^{-1}\) were collected at 10 s laser exposure for 1 accumulation in a static mode. The laser power is 0.5 mW, BLZ of a diffraction grating is 2400 line/mm. The three replicate measurements at different times were performed for each cell to reduce the measurement error. The humidity and the temperature was kept at a stable level in the laboratory. Figure 2a presents the raw Raman spectra of the background and A549 cell samples.  
For the Raman spectral pre-processing, Renishaw WiRE 3.4 software (Renishaw plc, Wotton-under-Edge, UK) was used to remove the cosmic rays in the raw spectra. Then, all of the Raman spectra were baseline corrected using the Vancouver Raman algorithm [23]. The smoothing pre-treatments were performed to reduce the external noises, and enhance the useful information of the biochemical composition. Therefore, Figure 2b gives representative peaks of the A549 cells after pre-processing. The average spectra of the eight different human cells after pre-processing are presented in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample’s Serial Number** & **Sample’s Name** \\ \hline
1–100 & SH-SYSY, human neuroblastoma cells \\
101–200 & HeLa, human cervical cancer cells \\
201–300 & HO-8910, human ovarian cancer cells \\
301–400 & MDA-MB-231, human breast cancer cells \\
401–500 & U87, human glioma cells \\
501–600 & HEB, human glioma cell line \\
601–700 & A549, human lung cancer cells \\
701–800 & MCF-7, human breast cancer cells \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the eight different human cells.  
Figure 2: (**a**) Raman spectra of the background and A549 human cells; (**b**) The representative peaks of the A549 cells after pre-processing.
## 1 Introduction
### Geometry Assurance  
Geometry assurance is a term describing a set of activities, all aiming to improve the geometrical quality of an assembled product. The geometrical quality affects both the esthetical and functional requirements of the final product. Usually, the maximum geometrical variation allowed in a certain dimension on the product is specified with a tolerance.  
The geometry assurance activities start in the early design phases of the product realization process with tasks such as locating scheme optimization to secure a robust positioning system during the joining of parts and variation simulations to predict the variation in the critical dimensions of the final assembly. Inputs to the variation simulation are part geometries, information about joining and fixturing and inspection data, or suggested tolerances on the part level. The analysis is done iteratively to find a reasonable set of part tolerances, i.e., finding limits for maximum allowed variations on the part level at an acceptable cost. The simulation is sometimes based on the assumption that parts are rigid [4], and sometimes, the flexibility of, for example, sheet metal parts is included [5; 6]. The latter one usually improves the simulation accuracy [5] and makes it possible to include clamping and joining forces, different material characteristics, joining processes, etc.  
The variation simulation can be done in a standalone CAT (computer-aided tolerancing) software, such as RD&T [7], or integrated into the CAD system, as, for example, 3 DCS [8]. Both setups are widely used industrially and have their pros and cons.
## 2 Methodology
### Classification of Normal and Glaucoma Retinal Images Using Deep CNNs
#### 2.2.1 Transfer Learning Using Pretrained Deep CNNs (Method P1)  
Method P1 used a transfer learning scheme based on pretrained deep CNNs. Eleven pretrained models--AlexNet [30], GoogleNet [31], InceptionV3 [32], XceptionNet [33], ResNet-50 [34], SqueezeNet [35], ShuffleNet [36], MobileNet [37], DenseNet [38], InceptionResNet [39] and NasNet-Large [40]--were evaluated. Table 2 lists the pretrained CNNs along with their specifications. The network depth presents the total number of layers from the input to the output of each model. DenseNet had the deepest depth with 201 layers, whereas AlexNet had only 8 layers. The inputs to all networks were RGB images. These models were pretrained on the ImageNet dataset for classification of thousands of natural objects. To adapt  
Figure 4: Retrieving OD regions from the full retinal image using DeepLabv3+ and cropping to the bounding box.  
Figure 3: Block diagram for optic disc (OD) detection using DeepLabv3+.  
those models to predict glaucoma, a transfer learning technique was used, as shown in Figure 5. First, the OD was resized to the input size of each CNN model and the resized images were separately input to pretrained CNNs. The last fully connected layer of each model was replaced with the new layer. The new fully connected layer was trained and fine-tuned on the cropped OD images to classify between normal and glaucoma. Cross entropy loss was optimized during each iteration:  
\[\text{CE}\big{(}\text{Y},\text{Y}^{\text{Predict}}\big{)}=\sum_{\text{c=1}}^{ 2}\text{Y}_{\text{c}}\log\big{(}\text{Y}_{\text{c}}^{\text{Predict}}\big{)}+( 1-\text{Y}_{\text{c}})\log\!\big{(}1-\text{Y}_{\text{c}}^{\text{Predict}}\big{)} \tag{1}\]  
where Y is the target vector and Y\({}^{\text{Predict}}\) is the predicted class vector: the vector elements are binary--0 represents normal and 1 represents glaucoma. Each CNN model was trained using the Adam optimizer [41] with default parameters: initial learning rate, \(\alpha\) = 0.001, decay rate of gradient moving average = 0.9 and squared gradient moving average = 0.999.
## 2 Materials and Methods
### Algorithm Efficiency Improvement  
This study proposes two strategies to improve the matching efficiency: (1) extracting the overlapping area in the auxiliary image based on the partitioning of the main image during the pre-processing stage; (2) employing a CPU-GPU collaborative processing strategy to enhance the matching efficiency of the proposed algorithm.  
.3.1 Extracting Overlapping Regions of Primary and Secondary Images Based on Block Matching Strategy  
To address the issue of redundant computations caused by direct feature matching between the main and auxiliary images, this paper employs a feature matching algorithm based on segmenting the main image into blocks. By applying a rational function model, intersections between the main and auxiliary images are computed to extract their overlapping areas. The steps of the algorithm are summarized as follows:  
1. First, the corner points of the images are mapped to the world coordinate system to determine if there is an intersection between the two images. If no intersection exists, subsequent steps are not executed to avoid unnecessary computations.
2. After identifying the overlapping region between the images, one image is selected as the main image, which is then divided into blocks. The image coordinates of the corner points of each block are computed using the Rational Polynomial Coefficients (RPC) model. Next, the corner points of each block in the main image are substituted into the auxiliary image, and the row and column coordinates of the overlapping region in the auxiliary image are calculated using the RPC model, as shown in Figure 4.
3. The STSU-SAR-SIFT matching algorithm is applied to obtain the connecting points in the overlapping area.
4. The connecting points in the overlapping area are mapped back to the original images. By multiplying the size of each image block by the row and column number and adding the connecting point coordinates, the coordinates are obtained on the corresponding image.  
Figure 3: Descriptors constructed using different GLOH methods.
# Advances in Fermentation Technology: A Focus on Health and Safety
## 3 The Effectiveness of Innovative Methods in Fermented Food Safety Assurance
### Artificial Intelligence and Machine Learning in Smart Fermentation Systems
ML algorithms, particularly Random Forest, have shown high accuracy (>95%) in predicting foodborne pathogen presence [132]. As a matter of fact, one study explored the use of machine learning algorithms to assess the presence of _Salmonella_ in ground chicken meat. Their findings indicated that the semi-supervised Random Forest model achieved the highest accuracy at 94%, along with a Kappa value of 82% and an 87% prediction rate. The model successfully identified virulence-associated genes, suggesting potential contributions to predictive modelling in food safety, including identifying pathogen sources, predicting antibiotic resistance, and evaluating foodborne pathogens [133]. Another study investigated the growth and inhibition limits of _L. monocytogenes_ under various preservatives commonly used in the meat industry, along with pH as a control factor. They created a multifactorial logistic regression model to estimate the likelihood of growth, establishing two thresholds to define growth limits. At the first threshold (\(p\) = 0.0104), their model achieved a classification accuracy of 68.6%, with a true positive rate of 77.6% and a true negative rate of 45.5% for growth detected on PALCAM agar. The second threshold (\(p\) = 0.04) yielded an improved accuracy of 82.6%, with a true positive rate of 95.5% and a true negative rate of 80%. These insights can inform the development of ready-to-eat products that inhibit _L. monocytogenes_ growth when stored at optimal temperatures [134]. Real-time environmental sensors and predictive models can significantly improve supply chain management and food safety [135]. However, limitations exist in modelling complex microbial interactions under dynamic conditions, necessitating ongoing research and development [136,137]. AI has shown the potential to enhance the management of microbial communities in fermentation processes, creating environments that support beneficial microorganisms and suppress harmful pathogens. Nonetheless, dependence on AI-driven algorithms could foster an excessive trust in automated systems, possibly neglecting the subtle human judgment essential in quality control. ML algorithms have become potent instruments in food safety, especially in forecasting microbiological behaviours that provide considerable public health hazards. With the incorporation of extensive datasets, these algorithms may exceed the restrictions of classic culture-dependent approaches. Supervised machine learning can enhance production processes by enhancing conventional microbiological testing rather than serving as an alternative. By anticipating microbial activity at critical production phases, these models may serve as proactive tools for food safety, allowing early intervention and better results.
## 2 Related Work
Furthermore, CNN is used to identify NLOS signals in the correlation output. This approach adds weights based on NLOS probabilities to PR to improve GNSS positioning in complex urban environments [32]. Ref. [33] developed a deep neural network (DNN) to address the limitations of traditional beamforming methods and improve the positioning performance of vehicle navigation in harsh multipath environments. Meanwhile, a model for NLOS signal detection and correction based on CNN and variational modal decomposition is proposed. The model first uses CNN to identify the NLOS signal. Then, the variational mode decomposition method is used to decompose the detected NLOS signals and eliminate the NLOS part [34]. Based on image characteristics, a CNN model is proposed to estimate images with synthetic multipath information generated by a GNSS signal-propagation simulator [35]. However, DL-based NLOS detection requires a large number of labeled data. In addition,in the process of NLOS detection, there are still some problems, such as high dimensional data redundancy, low detection accuracy, and poor generalization performance. Although these model-based methods have obtained promising detection results, they are still inadequate in terms of NLOS series information. The specific limitations can be broadly summarised as follows: (1) Supervised-learning-based NLOS detection techniques require a large number of labeled data, yet constructing large-scale labeled data is difficult. (2) The data are contaminated by the noise around the receiver and exhibit non-linear characteristics, and the detection accuracy and robustness of the existing learning-based NLOS models are low. To address the above limitations, from the perspective of series data, we applied DAE to achieve unsupervised NLOS detection. Then, Gaussian noise was added to the model to improve its robustness. Finally, MLP was used to detect nonlinear NLOS data.
## 4 Results and Discussion
### Morenci Mine (USA)-SkySat
The flyover animation generated with the textured model is shown in the supplement. It is worth mentioning that, if only two sets of images (backward and nadir or forward and nadir) are used, the DEMs have blind areas (Figure 8). Therefore, the tri-stereo yields both a better accuracy and a more complete coverage. Figure 8: Subset of SkySat shaded DEMs over Morecni Mine using (**a**) backward and nadir images, (**b**) nadir and forwards images, (**c**) backward, nadir and forwards images. Figure 7: (**a**) DEM of Morecni Mine are computed in the object space from tri-stereo SkySat on 1 January 2019; (**b**) a zoom view of 3D-textured model (a flyover animation of the 3D model is available in the Supplementary Materials). We were also able to apply the classical DEM extraction work-flow in the image space [26; 28]. The epipolar rectification was performed between pairs of images that overlap by more than 50%. This criterion excludes pairs of consecutive scenes, or of scenes from different sensors. A total of 156 pairs were selected. Then, the SGM algorithm is used for the dense matching. Ultimately, the resulting 156 DSMs were merged using a mean approach to build a final DSM of 1.5 m GSD. The image base DSM reconstruction was performed using the OrthoEngine module of PCI Geomatics software. To allow a visual assessment of our results, a subset of the extracted DSM is depicted in Figure 9. The stepped benches related to the open-pit mine (which are typically 2.25 km wide and 4-5 m in height) are clearly visible in the DEM obtained from the DEM obtained with traditional photogrammetry in the image space. They are also visible in the DEM obtained in the object space but are less well resolved. We now compare the LiDAR DEM with DEM extracted from the SkySat triplet in the object space. We down-sample the LiDAR DEM to the 3 m GSD of the SkySat DEM which was generated in the object space. Figure 10 shows the complete map of elevation differences between SkySat and LiDAR DEMs. It shows large differences up to 200 m in amplitude. Visual inspection of the images shows that the large differences are clearly due to mining operations, constructions or vegetation clearing. We selected an area not affected by any obvious temporal changes (outlined box in Figure 10). Panel (b) in Figure 10 shows the histograms of the differences within that \(\sim\)10.56 km\({}^{2}\) area. The mean difference is only \(\sim\)0.22 m, the standard deviation is \(\sim\)3.9 m and 95% of the differences are less than 10 m. The normalized median absolute deviation (NMAD), a quantity commonly used to compare DEMs [26], is 5.3 m.  
Clearly, the large topographic changes revealed by the DEM comparison (for example along profile AA' in Figure 10) are real and can be used to quantify the mining operation (excavation or stock piling) or clearing activities.
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 3 Physical Characterization and Results
### Initial Conjunction Screening Algorithm  
To reduce computational overhead and isolate potential high-risk encounters, an initial conjunction screening algorithm was developed. This process employs a geometric filter to identify candidate objects likely to enter a 50 km proximity threshold with selected Starlink satellites. The threshold is deliberately conservative to maximize detection sensitivity while minimizing the number of cases requiring high-resolution analysis. For this screening, a subset of Starlink satellites with varying orbital inclinations was selected. Each satellite's orbital elements were compared against a debris catalog of 2644 tracked objects. Any object predicted to pass within the 50 km radial distance was flagged for further refinement. Table 1 presents a subset of Starlink satellites, representing the diverse orbital inclinations within the constellation. These satellites were analyzed against a debris catalog comprising 2644 objects associated with the Iridium 33 and Cosmos-2551 fragmentation events.  
For each chosen Starlink satellite, the Euclidean distance to each debris object in the catalog was calculated using coarse SGP4 propagation by the flowchart of the detection algorithm, as shown in Figure 4. To address potential inaccuracies in the initial TLE data and the infrequent updates, a conservative proximity threshold of 50 km was established, ensuring no significant close approaches were overlooked. The values in Table 1 reflect the number of distinct debris objects from the catalog that came within 50 km of each Starlink satellite at least once during the screening period, with each object counted only once per satellite, irrespective of the number of encounters.  
Figure 4: Flowchart conjunction candidate detection.
# IoT: A Long-Reach Fully Passive Low-Rate Upstream PHY for IoT over Fiber
## 2 Materials and Methods
### Requirements for IoT over Fiber (IoToF)
Figure 1: Relation between bandwidth and range for wireless IoT standards and passive optical networks (PONs)-based communication networks with the inclusion of the IoToF concept here proposed. for implementing solution to meet IoToF PHY requirements listed in Table 1. FBG can be used to inexpensively modulate information coming from IoT devices directly onto the optical signal in a passive way, enabling upstream transmissions over long distances satisfying the requirements outlined in Figure 1. In addition, different IoT devices can physically share a single optical fiber using FBGs tuned on different wavelengths. This provides a point-to-point (i.e., non-shared) upstream connectivity with a gateway exploiting the fiber bandwidth with wavelength division multiplexing to improve availability and privacy over IoToF for each IoT device.
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 5 Trust Management
### Trust Calculating  
The information collected about the behaviour of the neighbouring node is used to calculate a trust value for that node. The trust value can be updated over time to arrive at a more accurate judgment. Calculating the trust values can be as simple as counting the packets dropped by the node in question, or it can be more complicated and use maths functions.
## 4 Information Fusion and Residual Life Estimation
### Posterior Distributions of Multi-Source Information
#### 4.2.1 Posterior Distribution of Expert Information  
Similar to Equation (23), the likelihood function of field data is  
\[P(D|\lambda,\beta)=\lambda^{r}\beta^{r}M^{\beta-1}\exp\Bigl{(}-\lambda N^{( \beta)}\Bigr{)}, \tag{38}\]  
where \(M=\prod\limits_{i=1}^{r}t_{j}^{D}\) and \(N^{(\beta)}=\sum\limits_{i=1}^{n}\left(t_{j}^{D}\right)^{\beta}\). Then, the posterior distribution of expert information is  
\[ \begin{array}{ll}&\pi_{1}(\lambda,\beta|D)\\ &=&\frac{\frac{1}{\beta_{2}-\beta_{1}}\frac{\left(b_{1}\right.^{\beta}}{\Gamma _{(\beta_{4})}}^{\beta_{1}}\lambda^{\beta_{1}-1}\exp\bigl{(}-b_{1}\tau^{\beta} A\bigr{)}\cdot\lambda^{r}\beta^{r}M^{\beta-1}\exp\bigl{(}-\lambda N^{(\beta)} \bigr{)}}{\int_{\Omega_{1}}^{\beta_{2}}\left(b_{1}\right.^{\beta}\bigr{)}^{ \beta_{1}}\frac{\left(b_{1}\right.^{\beta}}{\Gamma_{(\beta_{1})}}^{\beta_{1}} \lambda^{\beta_{1}-1}\exp\bigl{(}-b_{1}\tau^{\beta}A\bigr{)}\cdot\lambda^{r} \beta^{r}M^{\beta-1}\exp\bigl{(}-\lambda N^{(\beta)}\bigr{)}d\lambda\right)} \end{array} \] (39) \[=&\frac{\left(b_{1}\tau^{\beta}\right)^{\beta_{1}}\beta^{r}M^{ \beta-1}\lambda^{\beta_{1}+r-1}\exp\bigl{(}-\lambda\left(b_{1}\tau^{\beta}+N^{ (\beta)}\right)\bigr{)}}{\int_{\Omega_{1}}^{\beta_{2}}\left(b_{1}\right.^{ \beta}\bigr{)}^{\beta_{1}}\frac{\left(b_{1}\tau^{\beta}\right)^{\beta_{1}} \left(b_{1}\right.^{\beta}+N^{(\beta)}\bigr{)}}{\int_{\Omega_{1}}^{\beta_{2}} \left(b_{1}\right.^{\beta}\bigr{)}^{\beta_{1}}\left(b_{1}\right.^{\beta}+N^{( \beta)}\bigr{)}}d\lambda\bigr{)}d\beta}{\int_{\Omega_{1}}^{\beta_{2}}\left(b_{1 }\right.^{\beta}\bigr{)}^{\beta_{1}}\frac{\left(b_{1}\tau^{\beta}\right)^{ \beta_{1}}\left(b_{1}\right.^{\beta}+N^{(\beta)}\bigr{)}}{\int_{\Omega_{1}}^{ \beta_{2}}\left(b_{1}\right.^{\beta}+N^{(\beta)}\bigr{)}}d\beta}{\int_{\Omega_ {1}}^{\beta_{2}}\left(b_{1}\right. Similar to Section 4.2.1, for the failure time of field data (\(t_{j}^{D}\), \(j\leq r\)), the failure probability at time \(t_{j}^{D}\) is  
\[ \begin{array}{ll}&f(t_{j}^{D}|\pi_{2}(\lambda,\beta))\\ =&\int_{\beta_{1}}^{\beta_{2}}\int_{0}^{\infty}f(t_{j}^{D}|\lambda,\beta)\cdot \pi_{2}(\lambda,\beta)d\lambda d\beta\\ =&\frac{\int_{1}^{\beta_{2}}\tau^{a_{2}\beta}(t_{j}^{D})^{\beta-1}\beta^{d+1} E^{\beta-1}(d+a_{2})\left[F^{(\beta)}+(t_{j}^{D})^{\beta}+b_{2}\tau^{\beta} \right]^{-(d+a_{2}+1)}d\beta}{\int_{\beta_{1}}^{\beta_{2}}\tau^{a_{2}\beta} \beta^{d}E^{\beta-1}(F^{(\beta)}+b_{2}\tau^{\beta})^{-(d+a_{2})}d\beta}.\end{array}  \tag{44}\]  
For the censored field data (\(t_{j}^{D}\), \(j>r\)), the reliability at time \(t_{j}^{D}\) is  
\[ \begin{array}{ll}&R(t_{j}^{D}|\pi_{2}(\lambda,\beta))\\ =&\int_{\beta_{1}}^{\beta_{2}}\int_{0}^{\infty}\exp\left(-\lambda t_{j}^{\beta }\right)\cdot\pi_{2}(\lambda,\beta)d\lambda d\beta\\ =&\frac{\int_{1}^{\beta_{2}}\tau^{a_{2}\beta}\beta^{d}E^{\beta-1}(F^{(\beta) }+(t_{j}^{D})^{\beta}+b_{2}\tau^{\beta})^{-(d+a_{2})}d\beta}{\int_{\beta_{1}}^ {\beta_{2}}\tau^{a_{2}\beta}\beta^{d}E^{\beta-1}(F^{(\beta)}+b_{2}\tau^{\beta} )^{-(d+a_{2})}d\beta}.\end{array}  \tag{45}\]  
The marginal PDF is  
\[