esa-sceva/satcom-chunk-collection huggingface dataset:
# Activity Evaluation Methodology for the Disposed Medical Linear Accelerators
## 4 Discussion  
In this study, we developed an in situ HPGe detector and survey meter-based methodology for medical linac component radionuclide identification and radioactivity level detection. Because of the high cost of the HPGe detector, it is not easy to place an HPGe detector with analyzing software in every medical institution. Moreover, it is remarkably difficult for users to analyze metal-based radioactive waste. However, we can simply estimate the activity (Bq/g) of the component with the information on the radionuclide and the dose rate. In our methodology, we acquired the information on the radionuclides using the ISOCSTM program without considering the uncertainty due to the difference in density. The components in a medical linac head vary in their constituent material, density, size, and shape. The feasibility of the methodology was studied, targeting a discarded medical Siemens linac. The radioactivity-derived radionuclides were identified, and the dose rate-based radioactivity levels were determined. The efficiency calibration of the HPGe detector generally uses the CRM of the 1 g/cm\({}^{3}\) density. As the linac was predominantly composed of metals, the uncertainty can be increased because of the different densities of CRM [18,19]. It is difficult to acquire a metal-based CRM in reality. The components to be measured need to be pre-processed to match their volume and shape to the CRM. Preparing adequate samples was challenging as the preprocessing involved melting and cutting the predominantly metallic components. As a result, in situ HPGe detector-based measurements for medical linac radioactive waste that could not be processed further were feasible. We employed ISOCSTM software for the geometry and density corrections, and the radionuclides were identified for each component. The Japanese guideline suggests correcting the survey meter-measured dose rates with weight and radioactivity conversion factors for calculating the radioactivity (in Bq) [20]. The dose rate-based radioactivity values were calculated for the Siemens linac components using the Japanese radioactivated material management methodology (Table 6). The radionuclides and radioactivity (Qi = H \(\times\) Ki \(\times\) F) were determined using the measured surface dose rate (H), component weight (F), conversion factor per material, and radionuclide (Ki). The radioactivity from the decommissioning day and five weeks post-decommissioning were compared. Most components exhibited radioactivity higher than that of the clearance limit according to the National Nuclear Safety Act. In the case of the 6 MV primary collimator and the ion chamber, the result exceeded the clearance level
# Similarity-Based Malware Classification Using Graph Neural Networks
## 5 Conclusions  
This work proposes a novel method for identifying malware families that is based on GNNs. The behavior of malware is represented by a graph data structure that is based on the call relationship and the potential semantics of the malware function. GNNs are used to aggregate the nodes on the graph and update the feature of specified nodes. Then, the readout function is used to output a fixed-size graph-level embedding. In this work, a similarity model that is based on Siamese networks is implemented to measure the distance between two samples in the feature space, and thus to determine whether they belong to the same class. Finally, the proposed similarity model was tested using an unseen dataset and a test dataset from Malware Bazaar. The accuracies of the model with a test dataset and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Technique**} & **Test Dataset** & **Test Dataset** & **Unseen Dataset** & **Unseen Dataset** \\  & **(Accuracy)** & **(Precision)** & **(Accuracy)** & **(Precision)** \\ \hline Ssdeep [6] & 0.605 & 0.559 & 0.519 & 0.510 \\ TLSH [7] & 0.619 & 0.568 & 0.541 & 0.522 \\ MSM\_GCN [25] & 0.764 & 0.776 & 0.547 & 0.560 \\
**Proposed method** & **0.920** & **0.929** & **0.704** & **0.695** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Similarity model performance comparison with previous works.  
Figure 13: Visualization of an unseen dataset using PCA.  
an unseen dataset were 0.920 and 0.704, respectively. The similarity model outperformed previously developed techniques.  
This study has three goals worthy of further research and improvement in the future. First, add more sample data. More malware families can be collected, and benign software can also be added to the dataset to make the model more versatile. Second, work on anti-obfuscate research. Some malware interferes with the analysis by adding packs and obfuscations which make the decompiler unable to parse the function call graph and assembly code content correctly. The third is to improve the method of transforming malware into a graph. Improve the processing of DLL functions and study how to find the key functions and ignore the general functions generated by the compiler.  
Conceptualization, Y.-H.C.; methodology, Y.-H.C. and R.-F.D.; validation, R.-F.D.; writing--original draft preparation, Y.-H.C.; writing--review and editing, Y.-H.C. and J.-L.C.; funding acquisition, J.-L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Results
### Dynamic Changes of SIF and GPP
#### 2.1.2 Seasonal Variations  
Figure 2 shows the seasonal variations of SIF, GPP and environmental variables at Qianyanzhou Station. It can be seen that the SIF value showed a gradual upward trend from spring to summer and tended to be stable in June. It reached the minimum value of 0.08 W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\) at the end of February and attained the maximum value of 0.46 W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\) by the end of June. GPP showed the same trend as that of SIF. It also increased gradually from spring to summer and tended to be stable in June. The GPP value was the minimum value of 2.28 gC m\({}^{-2}\) d\({}^{-1}\) at the end of February and the maximum value of 8.16 gC m\({}^{-2}\) d\({}^{-1}\) at the beginning of June. Therefore, the consistency between seasonal SIF and GPP indicated SIF can effectively characterize the seasonal change in GPP in the subtropical coniferous ecosystem.  
For the seasonal trends of environmental variables, including Ta, PAR, SWC and rainfall, it can be seen that Ta gradually increased from spring to summer and tended to  
Figure 2: Seasonal variation in SIF, GPP and environmental variables. Rainfall (grey bar) and SWC (open circle) in upper panel; PAR (open triangle) and Ta (open circle) in middle panel; SIF (open triangle) and GPP (open circle) in lower panel.  
stabilize in June. Ta reached the minimum value of 11.07 \({}^{\circ}\)C at the end of January and the maximum value of 31.54 \({}^{\circ}\)C at the beginning of July. We found that PAR showed a relatively stable trend from January to April, increased slightly in May, and tended to be stable in June. PAR reached the minimum value of 77.00 umol m\({}^{-2}\) s\({}^{-1}\) at the end of February, reaching the maximum value of 433.75 umol m\({}^{-2}\) s\({}^{-1}\) at the beginning of July. SWC was mainly affected by precipitation and generally showed a slight increase after the beginning of April and a gradual decrease after the beginning of June. SWC reached the maximum value of 0.40 m\({}^{3}\) m\({}^{-3}\) at the end of May, and the value gradually decreased from June to the minimum value of 0.22 m\({}^{3}\) m\({}^{-3}\) at the beginning of September.  
In general, the seasonal trend of Ta was similar to that of GPP, which both increased from spring to summer and tended to be stable in June, while the increasing trend of Ta was relatively more obvious. PAR showed a relatively stable change from January to April, rose in May, and tended to be stable in June, which was slightly different from the change trend of GPP. SWC was relatively stable from January to March and began to rise in April, reached its maximum value in May, and gradually declined after June, which was apparently different from the change trend of GPP.
# Mesh Twisting Technique for Swirl Induced Laminar Flow Used to Determine a Desired Blade Shape
## 3 Results and Discussion
### Case Comparison
All three show the tendency of the flow distribution to follow the theoretical profile for the tangential velocity. The final result in Figure 18 shows the average axial, tangential and radial velocities as a function of radius, for four cross sections downstream from the blades using Case II as an example. This corresponds to the results in [17] where the profiles are axisymmetrical (not the case here because of the blades) and indicate a clear exponential decay along the flow direction. The deviation angle is furthermore shown for the four cross sections in the figure. Figure 17: Tangential velocity in m/s in a cross section, 0.5 diameters downstream of the swirler exit, (a) Case I (left); (b) Case II (center) and (c) Case III (right). Figure 18: Axial (\(z\), **upper left**), tangential (\(\theta\), **upper right**) and radial (\(r\), **lower left**) velocities averaged for each radius in four cross sections. On the **lower right**, the deviation angle of the flow is shown. The dotted lines indicate theoretical profiles according to the results from [17] with the same swirl number as used in this study (\(S=0.2\)). The averaging is performed by taking a plane cut at a given axial \(z\) position, followed by a cylindrical cut at a given radius \(r\). Each value is therefore based on an average over a circle. It is clear that the velocity profiles deviate severely from the theoretical one at the blade exit, but downstream the profiles quickly reach the shape predicted by the fundamental mode. The radial component (assumed zero in [17]) is also quite small and is reduced quickly downstream. The distribution of the deviation angle also quickly reaches the predicted profile, as seen in the lower left part of the figure, two diameters from the blade exit.
## 4 Experiment and Result Analysis
### Reconnaissance Task Assignment
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**UAV** & \multirow{2}{*}{**Current Pos**} & **Task** & \multirow{2}{*}{**Task Pos**} & \(\tau_{f}^{\text{s}}(\mathbf{s})\) & **Planned** & **Maximum** & **Collaborate** \\
**ID** & & & **ID** & & **\(\tau_{f}^{\text{s}}(\mathbf{s})\)** & **\(v_{i}(\mathbf{m}/\mathbf{s})\)** & **\(v_{i}(\mathbf{m}/\mathbf{s})\)** & **UAVs** \\ \hline U0 & (1029.8, 204.3) & T23 & (1420.5, 729.8) & 23.38 & 35.63 & 50 & U6 \\ U6 & (2618.0, 202.9) & T5 & (1904.0, 377.2) & 23.38 & 40.00 & 40 & U0 \\ U5 & (9722.0, 4410.0) & T28 & (8364.7, 4134.4) & 34.66 & 50.00 & 50 & U7 \\ U7 & (6776.9, 5650.7) & T15 & (7634.8, 4636.8) & 33.66 & 48.01 & 50 & U5 \\ U4 & (3223.9, 5507.1) & T24 & (9391.8, 6108.0) & 23.57 & 50.00 & 50 & — \\ U9 & (8777.8, 2235.3) & T14 & (8712.1, 2190.3) & 22.95 & 40.00 & 40 & — \\ U13 & (1596.7, 2313.2) & T6 & (3348.8, 1669.4) & 51.66 & 40.00 & 40 & — \\ U14 & (8179.1, 5827.7) & T1 & (9846.0, 6986.1) & 56.75 & 40.00 & 40 & — \\ U15 & (8543.8, 9725.0) & T9 & (7617.9, 9429.2) & 30.30 & 40.00 & 40 & — \\ U19 & (8114.8, 3937.6) & T3 & (6881.7, 3528.7) & 22.24 & 80.00 & 80 & — \\ U21 & (8326.9, 436.9) & T20 & (4121.4, 67.6) & 15.48 & 50.00 & 50 & — \\ U24 & (2615.7, 5425.9) & T21 & (3144.3, 4235.8) & 39.56 & 40.00 & 40 & — \\ U29 & (9578.5, 9477.7) & T19 & (9387.9, 9928.5) & 17.24 & 40.00 & 40 & — \\ \hline \end{tabular}
\end{table} 
Table 6: Allocation information and time coordination relationship of the first round of reconnaissance tasks. Figure 5: Topology diagram for different simulation times. (**a**) Each reconnaissance UAV has confirmed the reconnaissance task before time = 8. (**b**) Each reconnaissance node executes reconnaissance task, and the strike nodes perform deterrence maneuver at time = 30. The meanings of elements are consistent with those in Figure 3.
## 5 Limitations, Prospective and Practical Socio-Educational and Research Implications  
Despite the optimistic findings, this research has been a subjective analysis of AR, so its real value in enhancing learning needs to be verified. We are aware that this requires more than just observations and opinions and should consider aspects regarding its real possibilities for implementation based on a controlled assessment of the learning that is going on. Moreover, this study does not collect information from all the variables involved within the theoretical models considered. Our intention is to supplement this investigation by studying the interaction of the presage, process, and product variables within the 3P model to see how they affect perceived acceptability and academic performance. At the same time, we must verify to what extent the motivational strategies adopted by a given student influence the student's perception of these environments. To this end, categories are being designed and will soon be implemented within a qualitative approach. We also want to acknowledge limitations such as the lack of a comparison group or a focus group, which would have provided additional insights into the students' experiences.  
At practical, socio-educational and research levels, the findings provide a valid and reliable response to the objectives sought, and although future research should provide a more solid base, these results allow us to state that AR enjoys wide acceptance among primary school students and has a very relevant role to play in teaching and learning in the coming years. The development of this technique (higher levels of processing, sensors, etc.) will also affect its level of acceptance and use as a school resource, which will serve to demand that high-level politicians invest larger amounts of money and draw up new plans and projects to be implemented around this technique.
## 3 Abnormal Scenarios
### Water Quality Measurement Location Decision Scenario  
In this study, we established a scenario of an abnormal situation in which an outage occurs in an area due to a reservoir's loss of ability to supply water. For the normal situation, the demand pattern information over a 24-h period was utilized. There are five abnormal cases which are shown in Table 1. It is assumed that situations occur where the three reservoirs individually cannot supply water and, when this situation does occur, the water must be received through the emergency connection pipelines from the surrounding reservoirs in order to minimize the water outage damage. Therefore, scenarios of opening all the emergency connection pipelines that are connected to each block for each situation were established. Block 1 receives water supply from block 2 through the connected emergency pipeline and vice versa. If there is a problem in reservoir 2 and it cannot supply water to block 2, it can receive water through the 10 emergency pipelines connected to blocks 1 or 2. If a problem arises in block 3, it will receive water through the nine emergency pipelines connected to block 2.
## 4 Analysis
### Security Analysis
#### 4.2.1 Local Forged Checksum  
In this case, the adversary may attempt to use mechanisms to generate the correct checksum. In this paper, the checksum generation program is designed as a series of computation process. Because every computation requires the result of the previous computation, the adversary cannot generate an accurate checksum in a specific time. If the adversary generates the checksum randomly, the probability that the adversary can generate the accurate checksum coincidentally is only \(\frac{1}{2^{k}}\) when the checksum has \(k\) bits. As long as the checksum is long enough, it is hard for the adversary to guess the correct checksum.
## 5 Conclusions  
This paper used the laser scanning method through boreholes to obtain three-dimensional point cloud data of an abandoned gypsum mine in south China. It reconstructed the three-dimensional spatial morphology of the actual gypsum mining rooms beneath the transportation infrastructure and established a numerical stability analysis model for the abandoned gypsum mine from mining excavation to long-term stages using the IMASS rock mass constitutive model. The following main conclusions are drawn:  
1. The IMASS rock mass constitutive model can effectively reflect the entire process of rock mass from microscopic damage to macroscopic instability, and the numerical simulation of current and long-term stages provides a much greater understanding of the mining room stability and the effect of various geo-mechanical parameters not considered in traditional empirical methods.
2. Through the proposed numerical analysis, it is revealed that the stability of the roof in the medium-weathered rock layer is relatively assured, and the stability of the abandoned gypsum mine is mainly controlled by the residual sidewalls. In the current stage, the abandoned gypsum mine stability is guaranteed. The numerical results show that a 0.4 m spalling can cause the overall instability and failure of the sidewall. This is almost inevitable in the medium to long term as the gypsum rock mass continues to soften and deteriorate. Therefore, reinforcement measures must be taken to ensure safety.
3. Using numerical simulation methods, the stability of the abandoned gypsum mine with filling solutions was evaluated. Assuming the residual collapsed mass and the filling cement body reach a height of 2 m, the stress state of the sidewall is effectively improved. Of course, the higher the filling rate, the more it can prevent sidewall softening and spalling, especially due to the groundwater influence.  
Conceptualization, Y.S.; investigation, H.W. and Y.J.; supervision, X.T.; writing--review and editing, X.T.; visualization, J.W.; software, B.T. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the \"National Natural Science Foundation of China (52278348)\" and the \"Transportation Science and Technology Development and Innovation Project of Hunan Province (202303-2)\".  
Not applicable.  
Figure 20: Sloss evolvement at different filling heights.  
Data Availability Statement: Data are contained within the article.  
Author [PERSON], [PERSON], and [PERSON] are employed by the Hunan Communications Research Institute Co., Ltd. Author [PERSON] is employed by the CCCC Construction Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Bone Chemical Composition Assessment with Multi-Wavelength Photoacoustic Analysis
## 3 Results and Discussion
The average and standard division results of the contribution curves for each group are shown in Figure 5. To examine whether these changes in contribution to the PA signal of different chemical components in the bone caused by the EDTA treatment are significant, a two-way ANOVA test was performed to compare the results in Figure 5a-h. After a 13-hour EDTA treatment and 26-hour EDTA treatment, the contribution of minerals, HbO\({}_{2}\), lipid, collagen, and water all show statistically significant changes (\(p\)-value \(<\) 0.05). The changes in contributions between the two treatment groups (i.e., 13-hour EDTA treatment and 26-hour EDTA treatment) are not significant. Figure 4: (**a**) Relative contents of the chemical components in the trabecular bone measured by multi-wavelength photoacoustic analysis (MWPA), including hydroxyapatite, deoxygenated hemoglobin (Hb), oxygenated hemoglobin (HbO\({}_{2}\)), lipid, collagen, and water). (**b**) The relative contributions of the mineral (hydroxyapatite), whole blood (Hb + HbO\({}_{2}\)), and other components (lipid + collagen + water) calculated from (**a**); \({}^{*}\) stands for \(p<\) 0.05.
# Steering Mirror System with Closed-Loop Feedback for Free-Space Optical Communication Terminals
## 5 Active Stabilisation Testing
When active corrections are enabled on the actuated steering mirror through the closed-loop system reading from the PSD, the beam spot's position becomes significantly more focused on the camera sensor. The pointing error becomes strongly attenuated (Figure 8a--bottom) when the active mirror is engaged, and the track remains within an angular extent of 600 \(\upmu\)rad. This signifies a more than 5-fold improvement in concentration compared to the uncorrected case, where the point track spreads over an angular extent of approximately 3400 \(\upmu\)rad. Figure 8: (**a**) Top—uncorrected pitch and yaw variation over 3000 s. Bottom—corrected pitch and yaw with closed loop enabled. (**b**) Plot of laser spot centroids measured on the camera sensor, with simulated Cubesat platform jitter being provided by a separate fast-steering mirror. The blue plot shows the track of the beam spot on the camera sensor with the piezo-steering mirror switched off, and the orange plot shows the track with real-time corrections switched on. The outlying peaks in the actively corrected data are due to the sharp motions in the jitter profile, which correlate with satellite platform maneuvering by the ADCS. Due to the limited 8 Hz readout rate of the PSD, fast changes in the direction of the satellite platform are under sampled, resulting in a time lag before the control algorithm updates. This results in jumps of the angular displacement of the spot, seen as small tails in the orange track in Figure 8. This effect could be minimized by using faster readout electronics for the PSD, or including communication between the ADCS and mirror controllers to predict sharp movements of the ADCS. Also notable is the elliptical shape of the spot track during active corrections. This is caused by the asymmetric mounting of the actuators about the pivot plane, meaning the mirror is able to rotate more quickly about its horizontal axis than its vertical axis. This could be solved by placing the actuators equidistantly in vertical and horizontal position relative to the pivot point.