esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results of Numerical Analysis and Discussion
### Field Measurement  
According to the measurement data collected from the tell-tale technique and extensometer used during the two months of field investigations of the roadway from the underground coal mine project, the roof displacement drastically increased upwards of 9.5 mm within less than 0.6 m of drilling into the rock as shown Figure 5. Although numerous displacement measurements have been taken during the extension of the main roadway which had been done from December 2015 to February 2016, the measurement results by extensometer-01, which was installed at around a depth of 13 m and a distance of 60 m from the entrance of the main gate, was selected to compare the actual field deformation and the numerical analysis results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Parameters**} & \multirow{2}{*}{**Claystone**} & \multicolumn{2}{c}{**Lubricant Material**} \\ \cline{3-4}  & & **After Injection** & **1 Month Elapsed** \\ \hline Unit weight (MN/m\({}^{3}\)) & 0.0217 & 0.022 & 0.022 \\ \hline Young’s modulus, E (MPa) & 153.0 & 9.8 \(\times\) 10\({}^{-4}\) & 0.41 \\ \hline Poison’s ratio, v & 0.26 & 0.45 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mechanical properties of the rocks and lubricant materials.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Unit Weight** **(MN/m\({}^{3}\))**} & **Young’s** **Modulus** **E (MPa)** & **Poison’s** **Ratio** **v** & **Shear** **Strength** **(MPa)** & **Friction** **Angle** **(**MPa)** \\ \hline
0.026 & 33,000.0 & 0.17 & 7.0 & 45 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of the concrete pipe.  
The mining depth assumed at the end of the tunnel is around 10 m, which is a relatively shallow depth. Despite the difference in the geological conditions, a correlation between the results of the numerical analysis and field data can be seen as reasonable and can be explained logically.
## 6 Future Directions
### Metadata Extraction and Integration Via ID Management  
As described in Appendix A, the PEFC research involves complex and diverse metadata. Although unique IDs are currently assigned to samples and experimental proposals, achieving scalable and automated metadata extraction requires the integration of persistent ID systems. In addition to internal databases, the use of instrument-specific persistent identifiers (PIDINST [64]) and researcher-associated identifiers (ORCID [65]) streamlines metadata retrieval and facilitates cross-platform interoperability.  
From April 2022 to September 2023, Japan's Research Data Utilization Forum (RDUF) [66] subcommittee on persistent identifier (PID) for research resources and instruments made significant efforts to address the challenges of assigning PIDs to physical research assets, including samples and experimental equipment. This initiative is part of Japan's broader goal of enhancing digital research transformation (DX) and open science practices. The activities of the subcommittee culminated in a comprehensive proposal published in Japanese that outlined strategies and solutions for effective metadata management and resource visibility improvement [67].  
In collaboration with international initiatives, such as the Research Data Alliance (RDA) [68], the subcommittee supported the dissemination of the global framework and guidelines by creating a Japanese translation of the RDA whitepaper on Persistent Identification of Instruments [69]. This effort reflects Japan's commitment to enhance interoperability and promote the visibility of international frameworks within the domestic research community.  
For instance, the MatVoc materials dictionary [70] used in the MDR XAFS DB exemplifies progress toward standardized metadata; however, these identifiers are not persistent. Transitioning to PID-based systems, as proposed by the subcommittee, ensures long-term stability, broader compatibility, and improves cross-disciplinary searches. These enhancements will strengthen data sharing within the synchrotron community and enable seamless collaboration across diverse research fields.
# G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud
## 3 Methods
### Geometry-Aware  
As shown in Figure 2, we construct a point cloud sensitivity map using a novel method, from which we extract the critical points. Various classification models place different emphases on critical point selection, yet significant overlap exists. We indirectly determine this subset through an alternative model, which adds scientific rigor and rationality to the process.  
We determine the 3D point cloud sensitivity map by monitoring the cumulative gradient loss and calculating the geometric information content of each point. The sensitivity map summarizes the input's skeleton shape, and points located on the surface typically have higher scores. Therefore, we use the following formula to approximate \(S_{1}\).  
\[S_{1}=\sqrt{\frac{\mathcal{G}}{\|\mathcal{G}\|_{2}}+H(x_{i};\mathcal{P})\sum \limits_{i=1}^{3}(p_{i}-p_{ci})^{2}}, \tag{3}\]  
among them, \(\mathcal{H}\) is the local curvature of k adjacent points, and \(p_{c}(x_{c1},x_{c2},x_{c3})\) shows the overall center of gravity of the object.  
Gradient-based visualization calculates the gradient of the decision score on the original image for the target class. The gradient reflects the impact of local position perturbations. However, the original gradient alone may have some limitations. Inspired by [51], we define \(\tilde{p}\) as the baseline input, representing samples with no information. In our paper, we use the center of gravity instead of the baseline input. The attribution of the \(i\)-th component of \(p\) can be viewed as the accumulation of all gradients along the linear path from the baseline \(\tilde{p}\) to the input \(p\). The integrated gradient (IG) can be approximated through multiple summations, as illustrated by the following formula:  
\[S_{2}\approx(p_{i}-\tilde{p_{i}})\times\sum\limits_{k=1}^{m}\frac{\partial f \Big{(}\tilde{p}+\frac{k}{m}(p-\tilde{p})\Big{)}}{\partial p_{i}}\times\frac{ 1}{m}, \tag{4}\]  
where \(m\) is the order of Taylor expansion.  
We utilize an Integrated Gradient (IG) [51] to capture the output of the maximum pooling. The core is a deep network that requires a baseline to simulate the absence of causes. We accumulate the gradient along the direction of the line between the baseline and the input so that the IG satisfies both sensitivity and implementation invariance. The critical point set \(S2\) is determined by monitoring the maximum pooling layer and the integral gradient loss. The robustness of the model is studied by taking the union of the 200 points with the highest scores selected from \(S1\) and \(S_{2}\). Here, \(S\) represents the final point cloud sensitivity map, and rank(.) denotes the selection of the top 200 points in the set. The sensitivity map of the point cloud is shown in the figure.  
\[S=rank(S_{1})\cup rank(S_{2}). \tag{5}\]In summary, the generalization method is used to reduce the discriminability of the logits, then the critical point set (S) is obtained, and some critical points are moved to the boundary until the classification network can no longer classify them.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 8 Polar-2  
### Mission Overview  
POLAR-2 ([[https://www.unige.ch/dpnc/polar-2](https://www.unige.ch/dpnc/polar-2)]([https://www.unige.ch/dpnc/polar-2](https://www.unige.ch/dpnc/polar-2)) is a dedicated GRB polarimeter foreseen to be launched in 2027 towards the China Space Station (CSS). The detector is being developed by an international collaboration consisting of teams from Switzerland, Poland, Germany, and China. The project is currently ready for the production of the flight model after a prototype was successfully tested for physics performance and space qualification in 2023. POLAR-2 is a successor of the POLAR mission, which detected 55 GRBs between October 2016 and April 2017 and performed polarization measurements in the energy range of 50-500 keV of 14 of these [56], as well as of the Crab pulsar [57].  
Although the measurement results of POLAR are the most constraining GRB polarization measurements to date, they are only able to constrain the polarization degree to be below \(\approx\)40%. Therefore, the results remain consistent with the majority of the existing theoretical predictions [58]. This, along with the hint of an evolution of the polarization with time observed in two of the GRBs [56], indicates the need for a significantly more sensitive detector. For this purpose, the POLAR-2 detector was initiated in 2017, followed by an approval for launch towards the CSS, through a United Nations Office for Outer Space Affairs call in 2019.
## 2 Target Detection Prototypes and Their Related Metrics and Crucial Issues
### Issues in Target Detection  
To tackle the target detection challenges and upgrade the above-stated metrics through UAVs, it is necessary to sort out the following problems.  
1. _Small-Size Objects:_ In real applications, the height of the shot is high, the size of the target is much smaller than that of the image, and the target has defective properties; then, the target suffers a particular degree of distortion overwhelmed by the angle of the shot and the correlative movement between the UAVs and target leads to a target which is substantially changing in the background, etc. Besides this, some datasets such as MS COCO characterized small targets due to limited discrete features which remit the missing and several false target detections [47].
2. _Target Occlusion:_ Target occlusion will occur due to target blockage and the effect of illumination surrounding which sorely validates the tracking and identification of targets. Further, false and missed detection is also caused due to occlusion. Many researchers tried to solve the target occlusion issue with the help of different methods based on various occlusion conditions. Some of the progressed methods based on DL are discussed in the next section.
3. _Joint Issues:_ An increased number of executions are multi-perspective. For example, some jobs are concurrently time-sensitive and have extensive resources, such as multi-scale appearance, spot, missed recipient and victim recognition [48; 49], and enhancement of realistic applications [50]. Scientists began to tackle various combined problems in target detection which are called joint issues.
## 3 Results and Discussion
### Radiation Characteristics  
The proposed antenna is simulated using the Ansys HFSS simulator tool, which produces peak gains of 2.31 dBi at 24.1 GHz, 6.11 dBi at 67.1 GHz, 12.00 dBi at 129.1 GHz, 14.14 dBi at 249.1 GHz, and 14.29 dBi at 287.1 GHz, as shown in 3D gain polar plots in Figure 6. Also, 2D far-field patterns in E-plane and H-plane are shown in Figure 7. At low frequencies, the current is distributed uniformly across the ground plane and patch (Figure 6a,b). As a result, radiation patterns in the E and H planes are omnidirectional. At mid- and higher frequencies, higher-order modes are generated, and the current density on the radiator is not as evenly distributed. Therefore, the radiated modes are more prone to high radiation interference or minor lobes. As illustrated in Figure 6c-e, deterioration in the omnidirectional nature of the radiation pattern begins with an increase in frequency. Various techniques, such as periodic structures, defected ground structures (DGS), parasitic elements, and split ring resonators, can be employed to suppress these undesired radiated modes, thereby improving the performance of the proposed antenna over the SWB range.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No** & **Resonant Frequency** & **Resistance, R\({}_{\text{in}}\)** & **Reactance, X\({}_{\text{in}}\)** & **Input Impedance, Z\({}_{\text{in}}\)** \\ \hline
1 & 24.1 GHz & 47.48 \(\Omega\) & 1.54 \(\Omega\) & 47.48 + j1.54 \(\Omega\) \\
2 & 67.1 GHz & 55.45 \(\Omega\) & \(-\)6.93 \(\Omega\) & 55.45 - j6.93 \(\Omega\) \\
3 & 129.1 GHz & 53.16 \(\Omega\) & \(-\)3.97 \(\Omega\) & 53.16 - j3.97 \(\Omega\) \\
4 & 249.1 GHz & 50.96 \(\Omega\) & \(-\)0.65 \(\Omega\) & 50.96 - j6.65 \(\Omega\) \\
5 & 287.1 GHz & 47.39 \(\Omega\) & \(-\)2.85 \(\Omega\) & 47.39 - j2.85 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input impedance of proposed antenna at selected resonant frequencies.  
Figure 6: _Cont._
## 5 Experimental Testing and Analysis
### Benchmark Test Functions  
In this section, 23 fundamental benchmark functions are used to evaluate DRSTASA and eight comparison algorithms. The test functions cover both unimodal and multimodal problems. Specifically, \(F_{1}\sim F_{7}\) functions are unimodal, and \(F_{8}\sim F_{23}\) are bimodal. The dimensionality of the functions is variable; for these tests, it is set to 30. Detailed definitions can be found in Tables 2 and 3. \begin{table}
\begin{tabular}{c c c} \hline \hline \begin{table}
\begin{tabular}{c c c} \hline \hline
**Function** & **Value Range** & **Best** \\ \hline \(f_{1}=\sum\limits_{i=1}^{n}x_{i}^{2}\) & [\(-\)100, 100] & 0 \\ \(f_{2}=\sum\limits_{i=1}^{n}|x_{i}|+\prod\limits_{i=1}^{n}|x_{i}|\) & [\(-\)10, 10] & 0 \\ \(f_{3}(x)=\sum\limits_{i=1}^{n}\left(\sum\limits_{j=1}^{i}x_{j}\right)^{2}\) & [\(-\)100, 100] & 0 \\ \(f_{4}(x)=\max_{i}\{|x_{i}|,1\leqslant i\leqslant n\}\) & [\(-\)100, 100] & 0 \\ \(f_{5}(x)=\sum\limits_{i=1}^{n-1}\left[100\big{(}x_{i+1}-x_{i}^{2}\big{)}^{2}+ (x_{i}-1)^{2}\right]\) & [\(-\)30, 30] & 0 \\ \(f_{6}(x)=\sum\limits_{i=1}^{n}\left([x_{i}+0.5]\right)^{2}\) & [\(-\)100, 100] & 0 \\ \(f_{7}(x)=\sum\limits_{i=1}^{n}ix_{i}^{4}+\text{rand}[0,1)\) & [\(-\)1.28, 1.28] & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Unimodal benchmarking function definition. Each algorithm was independently executed 30 times, during which the optimal values, mean values, and standard deviations (std) were recorded. The optimal values of each algorithm were compared: if an algorithm outperformed the comparison algorithm, it was assigned a score of 1; if the results were equal, it received a score of 0; and if it underperformed, it was assigned a score of \(-1\). In cases where the optimal values were identical, the mean values and standard deviations were sequentially compared. Ultimately, the total scores were tallied for each algorithm across all test functions, clearly demonstrating the performance of DRSTASA in relation to other algorithms. The results of DRSTASA, compared to nine other algorithms, are presented in Table 4. Apart from its weaker performance on the multimodal functions F21, F22, and F23 -- where the mean values were suboptimal and stability was lacking --DRSTASA exhibited stable performance on the other test functions, with minimal differences between the minimum and mean values. In functions F6, F12, F13, F16, F21, F22, and F23, DRSTASA did not achieve the optimal value; however, it reached the theoretical optimal values in other test functions, demonstrating adequate solution accuracy.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 3 Proposed Search Algorithm, a Set of Behaviors: A1
### Keep Velocity Behavior  
Similar to the keep distance behavior, and mimicking flocks and schools, a behavior aimed at keeping the velocity of the other agents is also implemented. Most of these sorts of behaviors are proportional to a certain function of the distance between the agents; see, for instance, [26]. Here, a similar law is proposed. The total velocity effect over an agent \(i\) is calculated as:  
\[\mathbf{V}_{T}^{i}=\sum_{\begin{subarray}{c}j=1\\ \forall j\
eq i\end{subarray}}^{N_{q}}\mathbf{v}_{j}f_{j}^{i} \tag{44}\]  
where \(\mathbf{v}_{j}\) is the velocity vector of agent \(j\), different from agent \(i\), and \(f_{j}^{i}\) is a function of the distance between both agents:  
\[f_{j}^{i}=\text{sign}\left(\alpha_{v}\right)\cdot\min\left\{e^{|\alpha_{v}|/ \bar{d}_{j}^{i}}-1,1\right\} \tag{45}\]  
where \(\bar{d}_{j}^{i}\) is the adimensional distance between agents \(i\) and \(j\) and \(\alpha_{v}\) is the coefficient to be configured. In this case, the characteristic distance is taken equal to the sensor footprint:  
Figure 6: In (**a**) attractive, repulsive and total forces with \(\bar{d}_{0}=1/3\) and \(\bar{d}_{max}=0.5\). In (**b**) function \(f\) of the keep velocity behavior (see Equation (45)) depending on the dimensionless distance \(\bar{d}\) and for different values of \(\alpha_{v}\).  
\[\tilde{d}_{j}^{i}=\frac{|\mathbf{d}_{j}^{i}|}{R_{f}} \tag{46}\]  
In Figure 6b, the function \(f_{j}^{i}\), Equation (45), has been represented depending on the adimensional distance \(\tilde{d}\) for three values of \(\alpha_{v}\). Once the total velocity \(\mathbf{V}_{T}\) over agent \(i\) has been calculated, the incomes of each surrounding cell are calculated similarly to Equation (42):  
\[I_{V_{g}}=\frac{V_{T}}{\sqrt{2\pi\sigma^{2}}}e^{-\frac{\left(\alpha_{v}^{g} \right)^{2}}{2\sigma^{2}}} \tag{47}\]  
where \(V_{T}=|\mathbf{V}_{T}|\) and \(\sigma^{2}=0.5\). An example of incomes for each cell has been presented in Figure 6b, where it has been assumed \(V_{T}^{1}=1\) m/s.
## 4 Results and Discussion
### Enhanced Performance of Structurally Modified Emulsifiers  
To further explore the influence of molecular architecture on emulsion stability, two modified emulsifiers--AS4 and AS5--were designed based on the PAC core structure of AS2. These new emulsifiers incorporate additional functional side groups, specifically amide and carboxylic heteroatoms, positioned differently along the aliphatic chains. This structural modification was intended to promote intermolecular interactions with the aqueous phase and improve interfacial film stability. The emulsification performance of these modified molecules was remarkable. As illustrated in Figure 8a-c, both AS4 and AS5 retained the characteristic PAC base but exhibited extended polar side chains. Simulations demonstrated that for all 14 oil types, AS4 and AS5 consistently formed water-in-oil (W/O) emulsions, resulting in a 100% HIPE success rate (see Table 7). In contrast, AS6--a third emulsifier with a similar backbone but lacking polar heteroatom groups--exhibited significantly diminished performance and formed stable emulsions in only 6 of the 14 oil models.  
Figure 7: \(\pi\)–\(\pi\) stacking of AS2 at oil–water junctions facilitating emulsion stability. (**a**) AS2O12 and (**b**) AS2O14 exhibit aromatic phase bridging through dimers and trimers at the interface.  
Visualizations of AS4O11 and AS4O13 in Figure 8d-e revealed dense emulsifier coverage at the interface. These emulsifiers formed monolayers that were uniformly distributed without reliance on interfacial activity from the oil phase. Their ability to independently stabilize emulsions underscores the critical role of heteroatom functionality in modulating interfacial interactions. The polar groups likely enhance hydrogen bonding or dipolar alignment with water, thereby strengthening the interfacial film. These findings suggest that rational molecular design--particularly the strategic placement of heteroatoms--can significantly elevate emulsification capacity in HIPE systems. The results presented here provide a clear guideline for designing future emulsifiers with optimized interfacial performance tailored for diverse oil compositions.
## 3 Results
### Output Power and Efficiency  
The pump diodes were set to a power level to reach about 20 mW of CW output power. It was noticed that the optimum position of the achromatic pump lens was slightly different for the diode pump and the Nd:YAG laser. To maximize the achieved pulse energy the pump lens position was optimized for the Nd:YAG laser and the power of the diodes was increased to compensate for the loss in CW power. The wavelength was fixed at 790 nm close to the gain peak of Ti:sapphire. A thermal power-meter was used to measure the average power of both the pump laser and the Ti:sapphire laser output. To estimate the output pulse energy as shown in Figure 6 the average power was divided by the PRR after subtracting the CW baseline. This may cause a slight underestimate of the true pulse energy,  
Figure 4: PD and error signals in CW+pulsed mode—scanning.  
Figure 5: PD and error signals in CW+pulsed mode—locked.  
as the pulsing causes a short-term depletion of the inversion after the pulse, meaning that the average CW baseline would be reduced in the CW+pulsed mode.  
The pulse energy conversion efficiency dropped from about 10% at low pump energy to about 7% at our highest setting. This reduction of efficiency was also observed in the absence of CW pumping and is still unexplained. At a pump energy of 95 \(\upmu\)J the output pulse shape was measured using a PD with 50 MHz bandwidth (Thorlabs PDA8A2), displayed in Figure 7, yielding a FWHM pulse duration of 73(5) ns, not corrected for the PD impulse response function. Assuming that the true pulse duration is shorter than this value, a peak power of about E/\(\Delta\)T = 15.4 \(\upmu\)J/73 ns = 0.21 kW is reached at maximum pump energy.  
Figure 6: Tisapphire laser pulse energy as a function of pump pulse energy.  
Figure 7: FWHM Pulse duration of the Ti:sapphire output.
## 1 Introduction
### Mobile Augmented Reality (MAR)  
A key advantage of augmented reality (AR) is its seamless integration with portable devices, such as tablets and smartphones, which now offer computational power comparable to traditional desktop systems. This mobility factor enhances AR's adaptability, enabling interactive experiences in diverse settings without being confined to stationary workstations or specialised infrastructure. Unlike conventional AR setups that require controlled environments, mobile AR applications provide ubiquitous access, allowing users to engage with digital overlays in real-world contexts, regardless of their location [8; 9].  
The concept of mobile augmented reality (MAR), also referred to as location-based AR, enables users to engage with augmented digital content in real-world environments without spatial constraints. Unlike traditional AR systems that require controlled settings, MAR enhances mobility and interaction, making it a versatile tool for immersive experiences.  
While not classified as wearable technology, modern smartphones and tablets possess the necessary capabilities to support MAR environments. Their integration of high-resolution displays, real-time processing, and sensor-based tracking enables interactive AR experiences that are comparable to those of dedicated AR systems.  
One of the key advantages of MAR is its ability to enhance spatial awareness by providing a dynamic, immersive navigation experience. Instead of relying solely on static, two-dimensional maps, MAR facilitates first-person exploration, where digital overlays respond in real-time to user movement and positioning. This approach transforms geospatial data from simple coordinate-based references into contextualised, location-driven insights, allowing for a richer and more intuitive understanding of territorial information [10].  
MAR has demonstrated high effectiveness in presenting geospatial content and digital overlays over extended distances. However, its accuracy can be compromised when applied to close-range positioning, often exhibiting deviations of up to 50 m, particularly in environments with GPS signal interference or limited sensor calibration [11]. Nevertheless, this adaptation of AR enables users to visualise content outside the classroom and formal teaching settings, thereby allowing the exploration of topics with real information about real phenomena [12]. Furthermore, by utilising mobile device interfaces and harnessing their portability, educational activities can be designed around consultations, providing students with a personalized experience in the context of their real environment [13]. Situated learning is also regarded as a suitable educational context for implementing MAR, placing students in authentic scientific research scenarios [13; 14].