esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of Gelling Properties  
The standard methods reported by [PERSON] et al. [15] were used to evaluate the texture (breaking force, deformation, and gel strength), expressible drip, whiteness, and thiobarbituric acid reactive substances (TBARS) of gels.  
The breaking force (g) and deformation (mm) were determined with a spherical head plunger (dia 5 mm) pressed into the specimen (5-cm high/6 cm per min/60% compression) using a texture analyzer (Stable Micro Systems, Godalming, Surrey, UK). The gel strength (g\(\cdot\)cm) was obtained by multiplying the breaking force with the deformation. For the expressible drip, a gel sample (0.5 cm thick) was weighed and placed between layers of Whatman filter paper No. 1 (two pieces at the top, and three pieces at the bottom). After compressing with the standard weight (5 kg) for 2 min, the sample was reweighed. Expressible drip was reported as percentage of sample weight. The gel color (\(L^{\star}\), a\({}^{\star}\), and \(b^{\star}\)) was determined using a Hunterlab Miniscan/EX instrument, and the whiteness was calculated as follows:  
\[\mathrm{Whiteness}=100-\sqrt{\left[(100-L^{\star})^{2}+a^{\star 2}+b^{\star 2} \right]} \tag{5}\]  
For the TBARS assay, sample (0.5 g) was homogenized with 2.5 mL of a TBARS solution (0.375% TBA, 15% TCA, and 0.25 N HCI). The homogenate was heated in a boiling water bath (95-100 \({}^{\circ}\)C) for 10 min to develop a pink color, cooled with running tap water, and centrifuged at 3600\(\times\)\(g\) at 25 \({}^{\circ}\)C for 20 min. The absorbance of the supernatant was measured at 532 nm. A standard curve was prepared using 1,1,3,3-tetramethoxyp propane at concentrations ranging from 0 to 10 ppm. TBARS was calculated and reported as mg malondialdehydedehyde equivalent/kg sample.  
The fishy odor analysis of unwashed minec and surimi gels was performed following the experimental protocol approved by the Human Research Ethics Committee of Walailak University (WUEC-21-125-01). Gel samples (3-cm diameter and 1-cm thickness) were conditioned for 30 min at room temperature. The gel samples were assessed by 10 trained panelists by sniffing. All of them are graduate students who were trained to have rich professional knowledge on fishy odor. A line scale was used to rate the strength of the fishy odor (0-10, representing none-strong).  
Protein patterns of unwashed minec, surimi, and their gels were studied using sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) under non-reducing (without \(\beta\)ME) and reducing (with \(\beta\)ME) conditions using, the method of Laemmli [16]. Fourier transform infrared (FTIR) spectra of gels in the region of 400-4000 cm\({}^{-1}\) were obtained using a Bruker INVENIO-S FTIR spectrometer (Bruker Co., Ettlingen, Germany), equipped with an attenuated total reflection (ATR) diamond crystal cell. A scanning electron mi croscopy (SEM) (GeminiSEM, Carl Ziesc Microscopy, Oberkochen, Germany) was used to visualize the microstructure of gels [9].
## 3 Secrecy Performance Analysis
### MRC-ZFB/MRT-SC-ZFB with FD Scenario
\begin{split} F_{\gamma_{2}^{MKT-SC-ZFB}}\left(R_{S}\right)& =\sum_{i=0}^{N_{R}}\binom{N_{B}}{i}(-1)^{i}\Theta_{N_{R},i}\sum_{j=0}^{\phi} \binom{\phi}{j}\bigg{(}\frac{\phi}{\varepsilon\tau_{B}}\bigg{)}^{\phi-j}2^{iR _{S}}\\ &\times\left[\Psi\left(j+1,j+4-N_{B},\frac{i2^{R_{S}}\eta\tau_{E} +\varepsilon\tau_{B}}{\varepsilon\tau_{B}\tau_{I}}\right)\frac{1}{\tau_{j}}+ \left(N_{B}-2\right)\right.\\ &\times\left.\Psi\left(j+1,j+3-N_{B},\frac{i2^{R_{S}}y\tau_{E}+ \varepsilon\tau_{B}}{\varepsilon\tau_{B}\tau_{I}}\right)\right]\left(\frac{ \eta\tau_{E}}{\tau_{I}}\right)^{j}\Gamma\left(j+1\right)\\ &\times\left[\exp\left(-\frac{i\left(2^{R_{S}}-1\right)}{ \varepsilon\tau_{B}}\right)\left(1-\exp\left(-\frac{\mu}{\lambda_{RP}}\right) \right)+\frac{\mu}{\lambda_{RP}}\right.\\ &\times\left.\left(\frac{i\left(2^{R_{S}}-1\right)\lambda_{RP}+ \mu\varepsilon\tau_{B}}{\varepsilon\tau_{B}\lambda_{RP}}\right)^{-\left(\phi-j +1\right)}\Gamma\left(\phi-j+1,\frac{i\left(2^{R_{S}}-1\right)\lambda_{RP}+ \mu\varepsilon\tau_{B}}{\varepsilon\tau_{B}\lambda_{RP}}\right)\right].\end{split}  \tag{46}\]  
In this phase, the proof process of the \(F_{\gamma_{2}^{MKT-SC-ZFB}}\left(R_{S}\right)\) for the MRT-SC scheme is similar with _Theorem_**1**, and will not be elaborated here. Substituting (44) and (46) into (27), the closed-form expression of SOP of MRC-ZFB/MRT-SC-ZFB for \(P_{out}^{MRC-ZFB/MRT-SC-ZFB}\left(R_{S}\right)\) is attained after simple mathematical manipulations. In (27), replacing \(\zeta\), \(\xi\) with MRC-ZFB and MRT-SC-ZFB, respectively.
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 3 Results
### Sensitivity Analysis of Input Variables  
To determine which input variables have the greatest influence on the classification results of both KNN and ANN-LSH models, we performed a sensitivity analysis inspired by the methodology used in [46]. For KNN, a one-at-a-time (OAT) perturbation approach was adopted, in which each attribute was varied individually while others were kept constant, and the impact on classification accuracy was measured. For ANN-LSH, the connection weight method, based on analysing the magnitude of feature weights in the ANN structure, was used to infer the relative importance of each variable by analysing the impact of feature weights on the output layer.  
The analysis shows that Water Temperature had the strongest influence on the KNN model. This is expected, as it is a key environmental variable that differentiates farmer profiles in the dataset. For ANN-LSH, Humidity Level was the most significant, suggesting that the hash functions were more sensitive to this feature in grouping similar farmers. Other high-impact variables included Soil Type and Pest Management, which relate directly to farming strategies and environmental adaptation, as it can be seen in Table 6.  
This sensitivity analysis helps highlight the robustness of each model in capturing the most discriminative features and supports a better understanding of how model-based similarities are determined, which in turn informs decision-making for agricultural recommendations.
# System for the Acquisition and Analysis of Maintenance Data of Railway Traffic Control Devices
## 6 Concept of Service Data Acquisition and Analysis System for Railway Traffic Control Devices  
The basis for efficient and effective management of the exploitation process of railway traffic control devices is a maintenance decision support system, an example block diagram that is shown in Figure 3.  
Due to the growing importance of rationalizing the maintenance and renewal processes of railway traffic control systems, a significant issue is the continuous identification of their technical condition, including the forecasting of their reliability. Railway traffic control is a component of the railway transport system that significantly affects the safety and efficiency of the movement of people and goods. Therefore, the rationalization of maintenance and  
Figure 3: Block diagram of the maintenance decision support system for railway traffic control systems.  
renewal processes is particularly relevant for railway traffic control systems, which often operate under difficult exploitation conditions.  
Assuming that the maintenance decision support system for railway traffic control devices will be based on a condition-based strategy (points 2 and 3) and using the railway traffic control system model (point 4), a system for the acquisition and analysis of service data from railway traffic control devices (SADEK) has been proposed [10].  
The SADEK system, as an IT system, has a distributed structure. Data collection and processing from railway traffic control devices and systems take place at the level of the Operation Control Centre (OCC). At the same time, damage detection inference is also conducted at the OCC level. This information is then sent to the Maintenance and Diagnostics Centre (MDC) for the purpose of making service-related decisions (see Figure 4).  
The process of inferring the type of failure or the probability of its occurrence is based on large data sets (Big Data) collected at the Operation Control Centre (OCC). These data are obtained from computerized railway traffic control systems based on defined communication protocols, while in the case of devices and systems built with older technologies, they are mainly collected using Internet of Things (IoT) sensors [28; 29; 30]. A block diagram of the decision-making process for fault detection, carried out at the OCC level, is presented in Figure 5.
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 3 Basic Idea and Preliminaries  
### Basic Idea  
In this paper, a novel model for vector map copyright protection is proposed, where the core innovation lies in replacing traditional methods of storing entire datasets into the blockchain with the use of unique identifiers (UIDs), as shown in Figure 1. These UIDs are constructed based on the geometric and topological features of vector maps, capturing the essential characteristics of the data in a compact and robust manner. Instead of registering large map files on the blockchain, this model only registers the UIDs and relevant metadata, significantly reducing the storage overhead on the blockchain.  
The UIDs are derived through a process that analyzes the geometric and topological relationships inherent in the vector map dataset. By leveraging geometric relationships and topological relationships, a unique and robust identifier is created for each vector map. This identifier not only ensures uniqueness but also maintains robustness against changes in the map's structure due to transformations such as rotation, scaling, and translation. The UID construction process is computationally efficient, which makes it suitable for large-scale datasets.  
Once constructed, these UIDs, along with associated metadata, including watermark information, user information, and timestamps, are independently registered on the blockchain. The blockchain serves as a decentralized and immutable ledger that ensures the traceability and verifiability of each map's copyright status, while the map data are stored off-chain using the InterPlanetary File System (IPFS). This approach provides a scalable solution for map copyright management, where only essential identifiers and metadata are stored on-chain, thus enhancing both efficiency and security.
## 3 Discussion
The observed residual binding can be described as non-HS-mediated binding and may be explained by the presence of other GAGs or negatively charged molecules on the cell surface, as only the absence of HS was confirmed by flow cytometry. Interestingly, [PERSON] et al. observed the residual binding of CXCL12\(\gamma\) to both HS-deficient pgsD-677 cells and GAG-deficient pgsA-745 cells [36]. This may be due to the binding of the highly positively charged COOH-terminal domain of CXCL12\(\gamma\) to negatively charged structures such as sulfated glycosphingolipids (sulfatides) or sialylated O-glycans that are present on the cell surface and have been identified as binding partners for chemokines [36, 37, 38]. The addition of soluble heparin or HS reduced the binding of CXCL9(74-103) to cellular GAGs (Figure S2G,H). The more heparin or HS was added, the less CXCL9(74-103) was bound. This effect could be observed on the HS\({}^{+}\) CHO cells and, to a lesser extent, on the HS\({}^{-}\) CHO cells. The effect of heparin was more pronounced than the effect of HS, as lower doses of heparin (0.3, 1 and 3 ug) already resulted in greater reductions in the binding of the peptide to the cellular GAGs. In contrast, the addition of HA did not affect the binding of CXCL9(74-103) to cellular GAGs (Figure S2I). As opposed to heparin, HS contains less and HA lacks sulfation. This reduced presence of negatively charged sulfates may explain the less pronounced effect of HS or the failure of HA to compete for peptide binding to cells. These experiments with soluble GAGs emphasize the strong GAG-binding capacity of CXCL9(74-103) on heparin and HS, as well as its GAG-specificity, since it poorly interacted with HA. The peptides in this study were fluorescently labeled with TAMRA in order to enable their detection in both IFT and flow cytometry experiments. Molecular interactions may be affected by any kind of labeling. However, all the peptides were labeled with the same fluorescent group at the same position (N-terminus), allowing a direct comparison between the different peptides. In summary, this study provides insights into the structural elements, such as the number and type of GAG-binding motifs and linker sequences, in peptides that play a role in GAG-binding affinity. Moreover, we report amino acid motifs that give specificity for binding to particular GAGs. An increased understanding of GAG-binding motifs will facilitate their identification in GAG-binding proteins, leading to increased insights into GAG-protein interactions, which is important for therapeutic drug development targeting these interactions.
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 3 Experiments and Results
### Real Images
Then, the mean error of the reprojection can be obtained using  
\[E_{reprojection}=\frac{\sum\limits_{i=1}^{n}\left|p^{\prime}_{i}-p_{i}\right|}{n} \tag{24}\]  
The position error and reprojection error of three methods in this case (the first case) are reported in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Proposed Method** & **P3P** & **RPnP** \\ \hline Position error/m & 0.043 & 0.105 & 0.0915 \\ Reprojection error/pixel & 0.37 & 0.80 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The position error and reprojection error in the first case. Figure 6: Real images and vanishing points: (**a**) left camera; (**b**) right camera. Finally, we choose the parallel lines of the \(y\)-axis to test our proposed method similarly, and the position error and reprojection error of the three methods in this case (the second case) are reported in Table 3.  
From Tables 2 and 3, the position error of our proposed method is 41% and 47%, respectively, compared with the other two methods; the reprojection error of our proposed method is 46% and 69%, respectively, compared with the other two methods in the first case; the position error of our proposed method is 38% and 43%, respectively, compared with the other two methods; and the reprojection error of our proposed method is 49% and 56%, respectively, compared with the other two methods in the second case. It can be seen that our proposed method has a better performance in terms of position error and reprojection error for both categories of parallel lines. In addition, after obtaining the mean computational time of all three methods in real images, our proposed method is shown to be faster than the other two methods and the computational speed is 2.31 times and 2.86 times as fast as the other two methods, respectively. This is consistent with the results of the synthetic data.
# Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections
## 2 Literature Review
### Image-Based Methods
#### 2.6.2 Diaphrag Tracking  
Image-based extraction of the respiratory signal can also rely on the diaphragm as an anatomical marker. The diaphragm is visible as a region of high contrast in the scanned images, distinguishing the lungs from the upper abdominal region. In the study by [PERSON] et al. [16], the correlation between diaphragm movement and motion of the tumor using two linear models was investigated. The main difference between the two models was that the second model took into consideration the shift in the phase between tumor and diaphragm movements. A mean correlation coefficient of 0.94 and 0.98 were observed with the first- and second-motion models. Even though a good correlation factor was observed with both models, it was emphasized that the correlation can differ between different patients and it must be investigated for each patient individually. [PERSON] and [PERSON] [17] performed a study to extract respiratory motion from CBCT images through diaphragm tracking. A medical professional determined five points on the projection images to identify the area containing the diaphragm. Through a modified canny edge detection scheme, the identified points were detected in the successive images throughout the projection data. In comparison to manual identification of the diaphragm edge, the observed error with the proposed algorithm for CBCT projections was 0.79 mm. For the rotating cone-beam CT, the diaphragm shape differs at different angle of gantry rotation, making it difficult to reliably track diaphragm position across the entire set of projections.
# Assessment of GNSS Galileo Contribution to the Modernization of CROPOS's Services
## 3 Results
### Kinematic Observations Using VPPS
The differences show variable sign, mostly negative, confirming the presence of a certain bias between the VPPS and REFERENCE solution. Figure 9: Easting coordinates obtained with VPPS using GGG (cyan) and GAL-only (green) observations given as difference to the REFERENCE value. If the planar (2D) departure of VPPS (GGG) and VPPS (GAL-only) solutions from the REFERENCE position are considered, the following values are obtained: GGG (0.4 mm to 6.0 mm), GAL-only (+0.6 to +6.9 mm). This is an additional confirmation that MEDIAN (3.3 mm) and AVERAGE (3.5 mm) planar deviations of the VPPS (GGG) solutions are  
Figure 11: Height obtained with VPPS using GGG (cyan) and GAL-only (green) observations given as difference to the REFERENCE. Figure 10: Northing coordinates obtained with VPPS using GGG (cyan) and GAL-only (green) observations given as difference to the REFERENCE. slightly smaller compared to GAL-only solutions: MEDIAN (4.0 mm) and AVERAGE (3.9 mm). If all solutions (27 GGG and 26 GAL-only) are plotted together, Figure 12 is displayed. Compared to the REFERENCE solution, all VPPS (GGG) solutions are within the circle with the radius of 6 mm, whereas all VPPS (GAL-only) solutions are within the circle with radius of 7 mm.
## 2 Materials and Methods
### Fire Prevention and Fire Detection
#### 2.2.2 Semantic Segmentation  
To enhance the precision of fire detection even further and to localize the fire in input images, two approaches were used--semantic segmentation and object detection. The goal of semantic segmentation is to cluster the parts of an image together that belong to the same object class. In our case, two target classes are fire and smoke. Semantic segmentation does not only expect labels and bounding box parameters as output. The result is a high-quality image with each pixel assigned to a certain class, often the same size as the input image. Therefore, it is a classification of images at the pixel level. There are two types of image segmentation: semantic segmentation, which classifies each pixel with a label, and instance segmentation, which classifies each pixel and differentiates each object instance. For the purpose of image segmentation, U-Net architecture [29] was used. U-Net architecture was originally designed for medical-image segmentation but over the time it was adapted for different purposes. It is one of the earliest deep-learning segmentation models, and  
Figure 7: Examples of network inference on test images. **A** represents actual image class, **P** represents model-prediction class, and **C** represents the model’s confidence in its inference. (**a) A: 1 P: 1 C: 97.21%. (**b) A: 1 P: 0 C: 75.63%. (**c) A:1 P: 1 C: 99.91%. (**d) A: 1 P: 1 C: 50.18%. (**e) A: 0 P: 1 C: 88.48%. (**f) A: 0 P: 0 C: 99.99%.  
several GAN variations, including the Pix2 Pix generator, use the U-Net design. The model architecture is fairly simple. It consists of an encoder (for downsampling) and a decoder (for upsampling) with skip connections. Skip connections are used to concatenate the encoder feature map with the decoder, which helps the backward flow of gradients for improved training. Two main metrics when evaluating image segmentation are the dice coefficient and Intersection-Over-Union (IoU). IoU is the area of the overlap between the predicted segmentation and the ground truth divided by the area of the union between those two measures. It ranges from 0 to 1 (0 to 100%). The dice coefficient is calculated exactly the same as the F1-score, two times the area of overlap divided by the total number of pixels in both images. The preliminary results, dice score, and IoU of the evaluated U-Net network are presented in Figure 8. From the obtained score, it can be seen that the dice score and IoU score were both slightly above the 0.6, which is considered a good score but leaves a lot of room for progress both in improving the dataset and upgrading the trained network model. An example of segmented images from the training is presented in Figure 9, which also confirms the abovementioned statement that the segmentation network already achieved great results, but additional improvements will make it even better.
## 8 Anonymity and Security Analysis
### Security against Active Attacks
#### 8.2.1 Soft-Active Attacks  
If ADV _physically_ compromises SNi, then it captures two sets of information:  
* Information related to the node itself: the current _PID_, the parameters used to calculate the pseudonyms, the hash functions, the keys and other information as listed in Table 2.
* Information related to the neighbors as listed in Table 3.  
The ADV would have all it needs to issue new valid pseudonyms and to send messages out to neighbors. Let us look closely at few scenarios:  
_Scenario 1_: If ADV physically compromises SNi, and SNi and SNi, so SNi knows some information about both SNj and SNr. However, it cannot calculate important information such as aj-r which is required for one-hop communication between SNj and SNr [16], because SNi would need IDj and IDr which are both deleted permanently at the end of the setup phase. If SNi hears a message, it cannot determine, with high confidence, the sender among neighbors while communicating with each other. If SNi receives message from sources \(\
otin\) Ni, then it would not be able to determine the source.  
_Scenario 2_: If ADV _physically_ compromises multiple SNs, let us call it set Ncs, and collects number of messages, let us call it set Ncm. Then, the number of compromised PIDs equal to Ncm since each message has unique PID. If the source SNi \(\
otin\) Ncs, then ADV cannot know the source node [16,27].  
_Scenario 3_: If the message sent, by source SNi as in _scenario_, 2 passes thought SNj \(\in\) Ncs or even through multiple compromised nodes, it will not be able to correlate the captured PIDi with SNi.  
_Scenario 4_: If a message sent by source SNi and V SN \(\in\) Ni is also \(\in\) Ncs (all neighbors are compromised), then ADV will be able to know that SNi is the source. It is unrealistic situation to have many compromised nodes in one area. However, this proves single or few compromised nodes cannot locate the identity of the source. In addition, a compromised node does not actually need to locate the sources within its range since it can detect the objects of interest (Panda) knowing that the ultimate goal of the adversary is to capture the _object_ not the sensor reporting the object.  
In summary, while we cannot prevent physical capturing of sensors, we need to make sure capturing sensors do not have destructive effects on other sensors. It is clear that our anonymity model protects against the _avalanche_ or the _domino effect_ behavior once one or few sensors are physically captured.