esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
As observed in Figure 3a, maximum sensitivity in the radial plane is obtained in the centre of the cone and decreases towards the ends; it is unlikely that the sensitivity component out-of-plane is minimal at the centre and increases towards the ends (see Figure 3b). \begin{table}
\begin{tabular}{c c} \hline \hline
**Laser Illumination Data** &
\begin{tabular}{c} **Wavelength, \(\lambda=552\) nm** \\ **Laser Power: 1 W** \\ \end{tabular} \\ \hline Digital camera CCD data & Resolution: \(804\times 556\) pixels \\ \hline \multirow{2}{*}{Divergent illumination source coordinates, \(SiL(r_{s},0,z_{s})\)} & \(r_{s}=0\) \\  & \(z_{s}=265\) mm \\ \hline \multirow{4}{*}{Conic mirror dimensions} & Outer radius: \(37.5\) mm \\  & Inner radius: \(25.0\) mm \\  & High: \(20\) mm \\  & Conic mirror angle: \(\delta=32^{\circ}\) \\ \hline Illuminated surface radius & Radius: \(25.0\) mm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data for the numerical analysis. Figure 3: Radial component in-plane (**a**) and out-of-plane (**b**) of the sensitivity vector. As observed, the maximum in-plane radial sensitivity is obtained from the cone's centre and decreases towards the ends. In contrast, the sensitivity component out of the plane is minimal at the centre and increases towards the ends. However, the contribution of the out-of-plane sensitivity is much smaller (less than 3%) than the in-plane radial sensitivity. Figure 4 shows the calculation of the total sensitivity of the system using the data in Table 1.  
As shown here, the out-of-plane component of the sensitivity vector on the outer side of the observed area only represents less than 3% of the total sensitivity of the system, and this decreases to zero as the observer moves towards the centre.
# Nondestructive Tomographic Imaging of Rust with Rapid THz Time-Domain Spectroscopy
## 3 Results and Discussion
In other words, there was no strong correlation between the optical and THz reflection images, as shown in Figure 4.  
Finally, in Figure 5c, we show the reflection spectra for different times averaged over the entire area in Figure 5b. We found that in the early stages, the decrease in the reflection (i.e., absorption by the iron oxides) was governed by the high-frequency component, whereas the lower-frequency component becomes more pronounced with time. In the end, the absorption peaked at approximately 0.8 THz on average after 60 min. Interestingly, we observed dynamic transitions between the different oxide types. However, their dynamic behavior in terms of different types of metal oxides has not been addressed previously. We observed a very similar tendency for the data shown in Figure 4 with the slow corrosion processes, in which the absorption peak reached 0.8 THz after Day 14 (when the reflection amplitude reached less than 10% of the initial value). To unravel the oxide types responsible for the THz reflection change, we performed Raman spectroscopy, as shown in Figure 5d. We used a laser source at 632.8 nm, focused  
Figure 5: Photograph images (**a**) and rapid THz reflection images (**b**) for different measurement time after it is exposed to acidic solution. (**c**) THz reflection amplitude spectra averaged over the entire region in (**b**) for different measurement time. (**d**) Raman spectra of lepidocrocite and hematite found in our experiments. Inset shows the THz reflection signal from a thick hematite pallet. with a spot size of 1 \(\upmu\)m. We identified two types of iron oxides from the rusted area; i.e., lepidocrotic (\(\gamma\)-FeOOH) and hematite (\(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)). It is likely that the initial frequency response is governed by \(\gamma\)-FeOOH whose THz absorption is known to be pronounced in the higher frequency range [33]. Contrarily, the THz response of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) has not been reported; we performed THz reflection measurement on a thick \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) pallet, which showed a strong absorption at around 0.6-0.8 THz (inset of Figure 5d). Therefore, we conclude that the transition toward the lower frequency region over time was mainly due to the increased amount of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\). The monitoring of dynamical behavior in chemical reactions is crucial for optimizing material processing. Therefore, our study necessitates future investigation on the development of in situ THz imaging tools, for instance, by incorporating fluidic devices that enable the use of thin chemical solutions for efficient THz detection.
## 2 Materials and Methods
### Dose Radiation Calculation  
Dose Length Product (DLP) and CT dose index volume (CTDIvol) values of each DE-CTA examination were also recorded from the automatically provided scan protocol to evaluate the absorbed radiation dose and its potentially harmful effects on the patient. DLP values were also standardized using gender-specific k coefficients (Kmale = 0.0056, kfemale = 0.0068) to obtain effective dose values (E). The effective dose values and estimated percentage of dose reduction were calculated.
## 5 Experiment Validation
### Roll Angle Detection Results  
The detected roll angle is compared to the Hall Effect sensor measurements, as shown in Figure 23, on the left. The deviations of estimated roll angles are shown in Figure 23, on the right. And the overall standard deviations of the roll angle estimation errors are shown in Table 3.  
Figure 23: _Cont._  
Figure 22: Roll modulation signals tracking results of different spin rates of 3.8 r/s (top left group), 6.4 r/s (top right group), and 7.5 r/s (bottom group).  
As we can see from Table 3, the average standard deviation of roll angle measurements is 3.3\({}^{\circ}\), which meets the requirements of practical usage. However, it is noticeable that the errors increase as the spin rate rises. It is due to the increase of dynamic stress caused by the instability of motor at high spin rates, which worsens the tracking performance of the second-order phase-locked loops.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 4 Improved Genetic Algorithm to Solve TT Flow Routing Problem
### Description of GATTRP
#### 4.3.1 Chromosome Encoding Rules  
In order to reduce the search space and eliminate redundant solutions, this paper adopted a two-segment chromosome encoding method. The first segment represented the order of salesmen traversing each node and the second segment represented the breakpoints between each salesman. When there were \(n\) nodes, the first node \(v_{1}\) was fixed as the starting and destination node for salesmen to traverse; the other \(n-1\) nodes were randomly arranged to be visited by \(m\) salesmen. The fixed starting node was not encoded in the chromosome and the length of the first part of the chromosome was \(n-1\), indicating the random arrangement of \(n-1\) nodes. The length of the second part was \(m-1\), indicating that when the nodes needed to be divided into \(m\) salesmen's paths, \(m-1\) breakpoints were needed. The breakpoints in the second part were stored in increasing order.  
In the following example, we made \(n=10\), \(m=3\), and the number of the fixed starting node be \(1\). We let the randomly generated breakpoints be \(4\) and \(6\). Then, we could encode the chromosome as is depicted in Figure 4. The traversal route of the first salesman was \(1\to 5\to 6\to 9\to 4\to 1\), the traversal route of the second salesman was \(1\to 2\to 8\to 1\), and the traversal route of the third salesman was \(1\to 10\to 3\to 7\to 1\).  
The above chromosome corresponds to the route of TT flows traversing in the actual network as depicted in Figure 5.  
Figure 4: Encoding method for an example chromosome.  
Figure 5: The traversal route of the chromosome in Figure 4.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Deep Keypoint-Based Loop Closing
#### 3.3.1 Online Learning for Binary BoW  
Unlike handcrafted descriptors, learned descriptors occupy a larger feature space, e.g., 128 dimensions in our case. Offline-trained BoW models may struggle to capture this space, making it difficult to distinguish deep feature indices in BoW leaf nodes. To tackle this, we introduce an online learning-based BoW model that uses extracted features exclusively from testing scene data. Constructing a tree-like structure with floating descriptors is challenging due to the large memory and time consumption, so inspired by [49], we employ binary hash transformation for processing deep features. As depicted in Equation (9),for descriptor vector values \(\mathbf{d}_{i}\) less than 0, the processed \(\mathbf{\hat{d}}_{i}\) is set to 0. For descriptor values greater than 0, the processed \(\mathbf{\hat{d}}_{i}\) is set to 1, where \(i\) ranges from 0 to 256.  
\[\mathbf{\hat{d}}_{i}=\begin{cases}1&\text{if }\mathbf{d}_{i}\geq 0\\ 0&\text{if }\mathbf{d}_{i}<0\end{cases} \tag{9}\]
# Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset V1 (CPD-1)
## 7 Conclusions  
This article presents the Cretan Pollen Dataset v1 (CPD_v1), which comprises microscope images of stained pollen grains of 20 plant types. A segmentation method based on the watershed algorithm was developed to segment each unique pollen grain, with an overall accuracy of 92%. The complete dataset with the raw data, the ground truth annotations, and the segmented pollen grains are also publicly available at 10.5281/zenodo.4756360 (accessed on 13 May 2021), as presented in Table 4, for other researchers to use [10].  
Conceptualization, N.T., G.M., K.M., and E.A.; methodology, N.T., G.M., and K.M.; software, N.T. and S.K.; validation, N.T. and E.S.; formal analysis, N.T. and E.S.; investigation, N.T. and E.S.; resources, E.S. and E.A.; data curation, N.T., E.S., and E.A.; writing--original draft preparation, N.T., E.S., and G.M.; writing--review and editing, N.T., N.V., K.M., and E.A.; visualization, N.T.; supervision, N.V., K.M., and E.A.; project administration, E.A.; funding acquisition, N.V. and E.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Emblematic Action \"The Honeybee Roads\" of the Greek Public Investments Program (P.I.P.) of General Secretariat for Research and Technology (GSRT) (project code: 2018E01300000).  
Not applicable.  
The data presented in this study are openly available in Zenodo at 10.5281/zenodo.4756360 (accessed on 13 May 2021).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Description** & **\#** & **Data Type** & **File Format** \\ \hline Color Microscope Images & Raw data & 157 & Image & png \\ \hline \multirow{2}{*}{Pollen Annotations} & Bounding boxes & \multirow{2}{*}{20} & \multirow{2}{*}{Tabular} & \multirow{2}{*}{CSV} \\  & of pollen grains & & & \\ \hline \multirow{2}{*}{Cropped Pollen Grains} & Segmented pollen grains & \multirow{2}{*}{4034} & \multirow{2}{*}{Image} & \multirow{2}{*}{png} \\  & with the background & & & \\ \hline \multirow{2}{*}{Isolated Pollen Grains} & Segmented pollen grains & \multirow{2}{*}{4034} & \multirow{2}{*}{Image} & \multirow{2}{*}{png} \\  & without the background & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of data available in the dataset (10.5281/zenodo.4756360 accessed on 13 May 2021).  
Figure 7: (**a**) proper segmentation example, (**b**) poor segmentation example and (**c**) an example of an isolated pollen grain image which wrongfully includes part of other pollen grains.
## 1 Introduction
[PERSON], [PERSON] et al. [27] developed a low-complexity ZF equalizer by exploiting the sparsity of the delay-Doppler cascaded channel and developed an optimization framework to design intelligent reflecting surface coefficients with an objective to minimize the bit error rate (BER). [PERSON] et al. [28] derived statistical models for the minimum mean square error (MMSE) equalizer coefficients and developed analytical solutions for the post-filtering information rate. [PERSON] et al. [29] developed a robust block MMSE-DFE (joint minimum mean square error and decision feedback equalization) framework with joint time-frequency-domain processing for UAV-to-Ground SC-FDE communication systems, which can greatly control the error propagation of DFE and further improve the balancing performance of the DFE equalizer. In this paper, a channel estimation and equalization algorithm based on FrFT is proposed to acquire the mud channel state information, eliminate inter-symbol interference caused by multipath effects, and alleviate frequency-selective attenuation of mud signals. The MPT system model and a detailed introduction of the technological method are shown in Section 2. Both simulations and real well tests have been conducted to test the proposed method's effectiveness; comparisons and analyses are described in Section 3. The discussion and results are given in Section 4. Conclusions are presented in Section 5.
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 3 Biomedical Applications of Magnetic-Plasmonic Nanocomposites
### In Vitro Toxicity Studies  
While many of the current nanomaterials propose potential biomedical applications, such as multimodal imaging, photothermal therapy, magnetically guided drug delivery etc., very few comprehensive studies have been carried out. While the nanoparticles can be used to treat or diagnose disease, we must also consider any possible side-effects that the nanoparticles themselves may have in vivo. A number of toxicity studies have been carried out on different cell lines. [PERSON] et al. carried out toxicity evaluations on Au@Fe\({}_{3}\)O\({}_{4}\) nanoparticles capped with L-cysteine on Chinese Hamster Ovary (CHO) cell lines [69]. These nanoparticles, proposed as drug delivery vehicles and hyperthermia agents, showed no significant cytotoxicity to the CHO cells over 48 h even up to concentrations of 1 mg/mL. The toxicity of similar nanoparticles was tested using an MTT (3-(4,5-dimethyltetrazolzolium bromide)) assay and it was found that the nanoparticles were non-toxic at 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL [26]. However, it was noted by the authors that the toxicity pathways of the Au@Fe\({}_{3}\)O\({}_{4}\) composites could be different due to size and morphology variations.
# Evaluating Collapse Fragility Curves for Existing Buildings Retrofitted Using Seismic Isolation
## 2 Case Studies
### Isolation System  
An hybrid isolation system, constituted by high damping rubber bearings (HDRBs, featuring an equivalent damping ratio equal to 0.15 at 100% shear strain), positioned on the building perimeter  
Figure 1: Floor plan with one way slab orientation for (**a**) SLD building and (**b**) GLD building.  
and steel-Teflon (PTFE) Flat Sliding Bearings (FSBs, namely, \"sliders\") positioned below the internal columns of the building (see Figure 2), has been adopted for the retrofit intervention. The described configuration has been selected in order to provide an adequate deformability and, at the same time, a proper torsional stiffness and restoring capability also preventing uplift phenomena in the sliding bearings.  
Modal response spectrum analyses have been performed for the seismic design of the isolation systems. According to the Italian Seismic Code [9], the design objective is represented by the protection of the building's superstructure from any structural damage towards the design earthquake associated with the life-safety limit state (LLS), corresponding to a return period of 475 years. All that considered, the minimum design period of the isolated building (T\({}_{\text{is,min}}\)) has been obtained by entering the 15% damped response spectrum using the spectral acceleration target value (S\({}_{\text{d}}\) = V\({}_{\text{yield}}\)/M\({}_{\text{total}}\)) for the superstructure's verification, where V\({}_{\text{yield}}\) is the base shear associated to the first occurring of structural damage in the superstructure while M\({}_{\text{total}}\) represents the total seismic mass of the superstructure.  
Table 2 summarizes the main design results. In particular, \(T_{is}\) represents the isolation period (including the superstructure deformability), \(T_{is}\)/\(T_{bf}\) is the isolation ratio between the periods of the isolated and fixed-base configuration (including infill panels), \(d_{max}\) is the maximum displacement demand of the bearings. The catalogues of the main Italian manufacturers have been used to select commercial devices, featuring suitable values of effective stiffness and vertical capacity. The main characteristics of the selected catalogue devices, i.e., nominal diameter (\(\phi_{\text{n}}\)) and total rubber height (t\({}_{\text{e}}\)) for HDRBs; nominal vertical load capacity (V) and displacement capacity (d\({}_{\text{c}}\)) for sliders, are reported in Table 2.  
The collapse prevention limit state (CPL), associated to a return period equal to 975 years, has then been considered for the verification of the designed system.
## 5 Future Directions
### Sectoral Implications  
Aerospace assembly cells can benefit from tighter tolerance control and low-latency multi-robot coordination enabled by 5G/URLLC and DT-driven reconfiguration [112; 133]. Electronics manufacturing stands to gain from AI-assisted vision, DT-enabled rapid line changeover, and edge-inference anomaly detection [113; 135; 137]. Medical and rehabilitation robotics are poised to integrate soft/bio-inspired control and collaborative safety layers aligned with human-centric Industry 5.0 principles [113; 136; 138]. Cross-sector adoption will hinge on assurance cases that combine functional safety standards (e.g., ISO/TS 15066) with cybersecurity certification and explainable hybrid-AI control stacks [133; 134; 135; 141].  
Figure 6: Strategic technological convergence for next-generation industrial robot control. Enabling pillars (AI, quantum, digital twins, 5G/URLLC) interact with hybrid, bio-inspired, edge, and cybersecurity control layers to deliver sectoral impact and Industry 5.0 outcomes. Sources adapted from representative smart-manufacturing DT studies [112; 113; 137], 5G industrial networking guidelines [133; 135], quantum-inspired robotic control demonstrations [134], and Industry 5.0 policy frameworks [136].
# Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
## 2 Materials and Methods  
Monte Carlo (MC) simulations were made to evaluate the variability and correlation of production costs considering data provided by fifty companies in the state of Sao Paulo, Brazil. An MC simulation is particularly suitable for this type of analysis, as it models the uncertainty and variability of the main variables involved, generating different cost scenarios, and enabling a better understanding of the risks, rather than relying on single estimates, or simple averages. Furthermore, Spearman correlation coefficients and ARIMA models were used to validate and refine the predictions, ensuring greater precision in the results obtained. The analysis considered data between 2018 and 2022, allowing a detailed analysis of cost risks over time.
## 6 Case Study
### Case of Honest Nodes  
To demonstrate the functioning of the proposed algorithm, we have chosen a topology composed of 7 interconnected nodes, as shown in Figure 10, and we will start with the case where every network node is honest. Indeed, our algorithm will proceed as follows:  
* Step 1: Each node Broadcasts the list of his neighbor's nodes to the Network. Let \(B_{n}\) be the neighbor's list broadcasted by the nth node. For example, suppose that Node_3 is an honest node. Then, this node will broadcast \(B_{3}=\{2,4,5,7\}\) to the whole network. As a result of this step, every network node will have the following information:
* \(\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\}\)
* Step 2: During this step, each node Broadcasts all its received lists to the whole Network. Thus, every node will have the following knowledge: \[\{1\rightarrow\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\},\] \[2\rightarrow\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\},\] \[3\rightarrow\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\},\] \[4\rightarrow\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\},\] \[5\rightarrow\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\},\] \[6\rightarrow\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\},\] \[7\rightarrow\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\}\}\] Let \(C_{x,n}\) be the neighbors list of the Nth node, received and broadcast by the Xth node. For example, \[C_{3,5}=B_{5}\text{ and }B_{5}\text{is located in the list of node }3\rightarrow\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\}\] And \(C_{x}\) is the list of all neighbors received and broadcast by the Xth node. For example, \[C_{3}=3\rightarrow\{B_{1},B_{2},B_{3},B_{4},B_{5},B_{6},B_{7}\}\] (4)
* Step 3: During this step, the Trusted Topology is then calculated as the intersection of all the lists. In our case, the Trusted Topology is T determined as: \[T=\left\{\bigcap_{k=1}^{7}C_{k,1},\bigcap_{k=1}^{7}C_{k,2},\bigcap_{k=1}^{7} C_{k,3},\bigcap_{k=1}^{7}C_{k,4},\bigcap_{k=1}^{7}C_{k,5},\bigcap_{k=1}^{7}C_{k,6}, \bigcap_{k=1}^{7}C_{k,7}\right\}\] (5)  
\[T=\{F_{1},F_{2},F_{3},F_{4},F_{5},F_{6},F_{7}\}\] where \[F_{x}=\bigcap_{k=1}^{7}C_{k,x}\text{ and }F_{x}(n)\text{is the }n-th\text{ element in }\bigcap_{k=1}^{7}C_{k,x} \tag{6}\]In order to eliminate redundancy and verify the consistency of this topology, we perform horizontal verification. This verification leads to the new topology, which becomes:  
\[T=\left\{\bigcap_{x=1}^{7}\bigcap_{t=1}^{Card(F)}(x,F_{x}(n))\right\} \tag{7}\]  
Consequently, if the Network is composed of Honest nodes only, then we will have:  
\[B_{1}=\{2,6\}=\bigcap_{k=1}^{7}C_{k,1} \tag{8}\]  
\[B_{2}=\{3,7\}=\bigcap_{k=1}^{7}C_{k,2} \tag{9}\]  
\[B_{3}=\{2,4,5,7\}=\bigcap_{k=1}^{7}C_{k,3} \tag{10}\]  
\[B_{4}=\{3\}=\bigcap_{k=1}^{7}C_{k,4} \tag{11}\]  
\[B_{5}=\{3\}=\bigcap_{k=1}^{7}C_{k,5} \tag{12}\]  
\[B_{6}=\{1\}=\bigcap_{k=1}^{7}C_{k,6} \tag{13}\]  
\[B_{7}=\{3\}=\bigcap_{k=1}^{7}C_{k,7} \tag{14}\]