esa-sceva/satcom-chunk-collection huggingface dataset:
# Centralized Task Allocation and Alignment Based on Constraint Table and Alignment Rules
## 6 Results and Discussion
### Time-Discounted Rewarding for Complicated Case  
The time-discounted scoring scheme is verified in simple cases as in Section 6.1. Therefore, this scheme is applied to the complicated case and the results are as in Figures 7-9. These results are different from the results in Section 6.1 because the spatial and temporal constraints are changed. In Figure 7, agent 1 is not allocated task 3 because task 1 is in local MUTEX relationship with the task 3. Additionally, the start-time of task 8 is faster than task 4 which is in global MUTEX relationship with task 8 so task 4 is not allocated to all agents. Therefore, all spatial constraints in Table 13 are satisfied in the results. Figure 8 shows the computational time in this case. Most iterations are under 0.05 s so it is fast enough. In Figure 9, agent 1 arrives at the point of task 1 early but task 1 starts late because task 1 must start with task 5 simultaneously. The red bar means hold time at point of each task. Note that the hold time is from arrival time to start time of task. Task 2 must start after task 1 ends so agent 2 waits until the task 1 ends. The third temporal constraint is that task 6 must start during work at task 7. To satisfy this constraint, agent 3 waits until 0.2 s before the start-time of the task 6 because \(\mathbf{T}_{min,dur}\) is set to 0.3 s and \(\mathbf{T}_{t,dur}\) is set to 0.5 s. Note that TACTAR is iterated frequently so if the start-time of task 6 is delayed, the start-time of task 7 will be also delayed reflecting the situation of task 6. All temporal constraints in Table 13 are also satisfied in the results.  
Figure 6: Timetable result of TACTAR for a simple case applying an accumulated distance cost scheme.  
Figure 7: 2-D result of TACTAR for a complicated case applying a modified time-discounted reward scheme.
# Investigation of Intermetallics Formation and Joint Performance of Laser Welded Ni to Al
## 3 Results
### Microhardness  
Formation of IMCs has a direct relationship with the hardness and strength of the weld. In this study, we performed the hardness testing along with the lines shown in the inserted images in Figure 9. The hardness distributions across the base material and the welds are presented in Figure 9a-d.  
As shown in Figure 9a, most hardness values measured along the top line in the fusion zone of the sample with 150 W PW laser are in the range of 250 Hv to 350 Hv, whereas the measured hardness at the bottom line is about 450 Hv, which correlates well to the formation of the AlNi\({}_{3}\). In addition, due to the appearance of a large number of pores along the bottom of the fusion zone, only limited measurement points can be taken. Figure 9b shows the hardness results of the sample welded using a CW laser with 1800 W laser power at focal point position zero. Due to the mixing of possible low-hardness Al\({}_{3}\)Ni and high-hardness AlNi in the fusion zone, the hardness value of the fusion zone along the bottom line of the weld ranges from 150 Hv to 450 Hv. The hardness along the top line of the weld varies between 220 V and 350 V, as shown in Figure 9b. When the PW laser power is reduced to 120 W, the hardness in the fusion zone is lower, in the range of 100 Hv to 200 Hv, as shown in Figure 9c. This results from the fact that the material mixing in  
Figure 8: SEM image with high magnification, EDS results in areas A–D of the Al–Ni joint welded using CW laser with 1800 W laser power at focal point position of +5 mm.  
Figure 7: SEM image with high magnification, EDS results in areas A–D of the Al-Ni joint welded using PW laser with 120 W laser power.  
the fusion zone is very minimal and the formation of the soft Al\({}_{3}\)Ni should be dominant. Figure 9d shows the hardness profiles along the top line and bottom line of the sample welded using a defocused CW laser with 1800 W laser power. As observed in Figure 8, the fusion zone is very narrow, performing the hardness measurement along the joint interface. Hence, the measurement was conducted vertical to the joint interface. Hardness distributions along three different vertical lines were presented in Figure 9d. The hardness distributions are quite identical among the three measured lines. Hardness values from the Ni side are similar to the value of as-received Ni sheet. Similarly, hardness values in the Al side are close to the value of as-received Al sheet. The hardness of the fusion zone indicates that the Al\({}_{3}\)Ni IMCs are dominant. Some hardness values are relatively low, due to the fact that the indentations may partially cover the soft Al.  
By comparing the distribution of weld hardness values obtained via laser welding of Al-Ni using different process conditions, it can be seen that the hardness value of the fusion zone depends on the type of intermetallic compounds produced during welding. Deep penetration and severe mixing of the two materials will lead to the formation of IMCs with high hardness, as well as obvious defects.
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
###### Abstract  
This paper proposes a 3D point-of-regard estimation method based on 3D eye model and a corresponding head-mounted gaze tracking device. Firstly, a head-mounted gaze tracking system is given. The gaze tracking device uses two pairs of stereo cameras to capture the left and right eye images, respectively, and then sets a pair of scene cameras to capture the scene images. Secondly, a 3D eye model and the calibration process are established. Common eye features are used to estimate the eye model parameters. Thirdly, a 3D point-of-regard estimation algorithm is proposed. Three main parts of this method are summarized as follows: (1) the spatial coordinates of the eye features are directly calculated by using stereo cameras; (2) the pupil center normal is used to the initial value for the estimation of optical axis; (3) a pair of scene cameras are used to solve the actual position of the objects being watched in the calibration process, and the calibration for the proposed eye model does not need the assistance of the light source. Experimental results show that the proposed method can output the coordinates of 3D point-of-regard more accurately.  
Keywords: gaze estimation; 3D point-of-regard; binocular eye model; stereo vision +
Footnote †: journal: Applied
# Assessment of Areal Methane Yields from Energy Crops in Ukraine, Best Practices
## 3 Results and Discussion
### Influence of Crop N Uptake on AMY  
Crop N uptake is related to crop N demand, the dosage of fertilizer supplied, and the N contents in soil [50,51]. The generalised mechanism of N uptake by plants related to N supply is described by [PERSON] [51]. A small correlation (R\({}^{2}=0.29\), significant at the 0.001 probability level) was found between the crop N uptake and the AMY values for all the analysed samples (see Figure 6). The crop N uptake varied depending on plant, variety and harvesting time.  
Figure 5: Correlation between dry matter yield (DMY) in t ha\({}^{-1}\) and areal methane yield (AMY) in m\({}^{3}\) ha\({}^{-1}\) for the total of 22 varieties from 7 crops harvested in three harvesting times (H1, H2 and H3) in 2017 (total amount of samples \(n=66\)).  
Among all the analysed research crops, miscanthus has the lowest N-demand per 1 m\({}^{3}\) methane produced (\(23.41\pm 7.18\) g\({}_{\rm N}\) m\({}^{-3}\)). Maize has higher N-demand than miscanthus, but is more efficient in N-use compared to other analysed crops (\(29.58\pm 7.13\) g\({}_{\rm N}\) m\({}^{-3}\)). Switchgrass and sweet sorghum continue this list with their N-demand of \(36.84\pm 13.87\) g\({}_{\rm N}\) m\({}^{-3}\) and \(39.08\pm 11.35\) g\({}_{\rm N}\) m\({}^{-3}\), respectively. Paulownia, soriz and soybean are the least efficient in N use for producing 1 m\({}^{3}\) methane among all the analysed plants.
# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
###### Abstract  
Non-intrusive load monitoring (NILM) provides a cost-effective solution for smart services across numerous appliances by inferring appliance-level information from mains electrical measurements. With the rapid growth in appliance diversity, continual learning that adapts to new appliances while retaining knowledge of previously learned appliances is of great interest. However, existing methods can handle only a few new appliance types and suffer from high computational complexity and data leakage risks. Therefore, an analytic continual learning-based (ACL) NILM method is proposed. The method employs a lightweight model that is constructed with dual output branches using depthwise separable convolution for load identification and novelty detection. Meanwhile, a supervised contrastive learning strategy is applied to enhance the distinctiveness among appliance types in the feature extraction module. When the novelty detection branch determines that new data need to be learned, the parameters of the dual branches are updated by recursively calculating the analytical solution using only the current data. Experiments on four public datasets demonstrate superior performance on pre-collected appliances with lower computational effort. It also significantly outperforms existing methods during the continual learning process, as the number of appliance types increases to 56. The practicality of the proposed method is validated through a real-world application on an STM32F407-based smart socket.  
Keywords:load identification; novelty detection; analytic continual learning (ACL); depthwise separable convolution; supervised contrastive learning +
Footnote †: journal: Applied sciences
## 2 Preliminaries
### Bipartite Graph Representation for UCPs  
This part describes how the UCP is transformed into a bipartite graph, a structured representation that enables GNN to learn variable-constraint interactions. Our approach adapts the MIP to the bipartite graph conversion framework proposed in [27] as shown in Figure 2.  
The bipartite graph \(\mathbb{G}=\{\mathcal{V},\mathcal{C},\mathcal{E}\}\) represents two disjoint sets of nodes--variables and constraints--and the edges connecting them. The associated features of these nodes and edges are summarized in Table 1.  
* Variables: \(\mathcal{V}=\{v_{1},\ldots,v_{n}\}\), where each variable embedding \(v_{i}\in\mathbb{R}^{7}\) corresponds to a decision variable \(x_{i}\), partitioned as binary variables \(x_{1}\ldots x_{nb}\) (e.g., commitment, startup, and shutdown variables) and continuous variables \(x_{nb+1}\ldots x_{n}\) (e.g., power output and startup cost).
* Constraints: \(\mathcal{C}=\{c_{1},\ldots,c_{m}\}\), where each constraint embedding \(c_{j}\in\mathbb{R}^{4}\) corresponds to the \(j\)-th constraint, e.g., operational rules formalized in Equations (2)-(12).
* Edges: \((i,j)\in\mathcal{E}\), which encodes the relationship where variable \(x_{i}\) appears in constraint \(b_{j}\) with a nonzero coefficient and has an edge embedding \(e_{i,j}\in\mathbb{R}\).  
Figure 2: Transforming a UCP formulation to a bipartite graph.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 4 Simulation Experiments and Validation
### Simulation Analysis
#### 4.2.3 Micromotion Analysis  
Target micromotion causing a change in projected area is an important factor contributing to the variability of target SNR. The proposed method supported the analysis of the micromotion characteristics in shorter time steps by enhancing the performance of the simulation system. A representative of each target type was selected for analysis; their corresponding SNR values over time are presented in Figure 11, obtained from the Sat47 sensor. Under the present circumstances, the Cylinder, which has notable variations in projected area across multiple dimensions, experiences a considerably greater change in SNR because of micromotion compared to other targets over the entire observation period. The observation effect of micromotion varies greatly at different time periods owing to changes in the sensor's viewpoint during long-term observation. The Cone2 target exhibits the smallest fluctuation during the period closest to the sensor. Figure 12 displays the effect of short-term target micromotion after excluding the interference caused by sensor line-of-sight changes. Cylinder rolling causes a more pronounced change in projected area than other types of targets, whereas Sphere rolling has the least effect on the change in projected area and the weakest SNR fluctuations. The different reflectivity of two cone  
Figure 10: Images in different bands from Sat11 to targets: (**a**) Band A; (**b**) Band C; (**c**) Band E.  
shaped targets with the same size leads to differences in average SNR. Cone2, with higher precession angle and frequency, exhibits significant differences in terms of the amplitude and frequency of SNR changes. Our system, combined with STK, can differentiate between the various micromotion modes of space targets with different shapes. Establishing a practical micromotion model of complex targets can be a crucial means of distinguishing target types in practical scenarios.
# A Cross-Chain-Based Access Control Framework for Cloud Environment
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Year** & **Application** & **AC Type** & **Platform** & **Digital System** \\ \hline
[7] & 2024 & Cloud storage & Smart agreement + AES & * & TP \\ \hline
[8] & 2024 & Edge computing & Smart contract + (ABAC, bloom filter) & Ethereum (private/public) & TP \\ \hline
[9] & 2023 & Fog IoT cloud-based & Smart contract + CP-ABE & Ethereum (public) & TP \\ \hline
[10] & 2023 & Cloud computing & Smart contract + ABE & * & TP \\ \hline
[11] & 2023 & Cloud-based medical & Smart contract & Hyperledger Fabric (chaincode) & P2P (private/permissioned) & P2P \\ \hline
[12] & 2023 & Cloud device services & Smart contract + (TFHE, bloom filter) & Ethereum (private) & TP \\ \hline
[13] & 2020 & Cloud storage & Smart contract + AES & EOS (public) & TP \\ \hline
[14] & 2019 &
\begin{tabular}{c} IoT-data \\ management/cloud \\ sidechain \\ \end{tabular} & Smart contract & Hyperledger Fabric (consortium) & Cross-chain \\ \hline \hline \end{tabular}  
* not provided by the authors in the paper. AES: advanced encryption standard; ABAC: attribute-based access control; CP-ABE: ciphertext policy attribute-based encryption; ABE: attribute-based encryption; TFHE: fast fully homomorphic encryption over the torus; TP: third-party; P2P: peer-to-peer.  
\end{table} 
Table 1: Surveyed blockchain-based access control. context vocabulary, such as banking, healthcare, education, etc. Some schemes do not meet these criteria because they utilize BC platforms that lack heterogeneity and interoperability, or they employ a static chain code for access control.
# Highly Active under VIS Light M/TiO\({}_{2}\) Photocatalysts Prepared by Single-Step Synthesis
## 3 Results and Discussion
### Photocatalytic Studies
#### 3.4.4 Rutile/Anatase Concentration Ratio Effect  
To investigate the role of rutile content in the photocatalytic activity of our meta-doped titania, five different catalysts were synthesized by only changing the ratios between titania's forms--rutile and anatase. In each experiment, the 100% substrate was completed by summing the rutile and anatase percentage, e.g., in the experiment with 70% rutile content, the other 30% is titania anatase. Their photocatalytic activity was evaluated towards the MB decolorization under UV and visible light emission. Two different metal dopants--Cu and Ag--were investigated, and the acquired results are presented in Figure 10. The optimum metal concentrations were chosen for each metal for a higher evaluation degree of the resulting photocatalyst.  
The results indicated that the ratio 95% rutile-5% anatase is the most beneficial substrate for both metal dopants towards the examined photocatalytic reaction. More specifically, in the case of Cu-based photocatalyst, the catalyst prepared with 5% anatase and 95% rutile achieved a major decrease in the irradiation time required to achieve 90% MB degradation (from 2.5 h to 1 h), comparing the results of Figures 7 and 10.  
Figure 9: Effect of catalyst concentration on catalytic behavior towards the MB degradation under visible light.  
The results suggest that the introduction of a small quantity of anatase titania (5%) considerably increases the activity of the examined photocatalysts towards methylene blue degradation. It was previously reported that the remarkable co-existent effect of rutile and anatase would arise from the increase in the charge separation efficiency due to photoinduced interfacial electron transfer from anatase to rutile under UV light excitation. In this study, the preferential Ag deposition on the anatase/rutile interface was held responsible for the superior photocatalytic activity in the case of the anatase and rutile mix substrate [46]. There are previous studies that demonstrate the photocatalytic superiority of catalysts prepared with a mixture of titania phases compared to those prepared with pure anatase or pure rutile. These data strongly indicate the existence of a synergistic effect between anatase and rutile phases in the metal-doped photocatalyst under visible light excitation. This synergistic phenomenon is similar to that of TiO\({}_{2}\) under UV light excitation. In their approach, though, the Sm-doped titania photocatalysts were prepared via a sol-gel approach using precursor compounds for the metal and the titania synthesis [47].
## Materials and Methods
### Algorithms
#### 2.4.2 SA Conflict Detection  
After receiving traffic information about aircraft resulting from SA coarse filtering processing, the conflict detection function checks for a potential loss of separation between the ownship and each considered aircraft. The conflict detection is based on the calculation of possible breach of the cylindrical separation volume by the ownship with respect to all the surrounding traffic vehicles: a pair-wise check is implemented for each and all the surrounding traffic vehicles that have been provided by the dedicated SA coarse filtering functionality, and the cylindrical separation volume breach is considered only if occurring within the specified tactical time horizon considered for separation assurance by the ASACAS. The output of this functionality is a list of conflicting vehicles with associated conflict geometry data. For the separation assurance functionality, a suitable safety bubble, also referred as a \"protected zone,\" is defined. This represents a non-intrusion zone around a traffic aircraft where the ASACAS declares that action is needed to preclude the ownship from going into the volume associated with separation minima.  
The ASACAS's separation assurance functionality is implemented as the tactical capability of keeping the RPAS vehicle away from other airborne aircraft by at least the separation minima defined by Eurocontrol as follows [31]: \"Specification RPA11: Where an RPA pilot is responsible for separation, he should, except for aerodrome operations, maintain a minimum distance of 0.5 NM horizontally or 500 ft vertically between his RPA and other airspace users, regardless of how the conflicting traffic was detected and irrespective of whether or not he was prompted by a Sense and Avoid system\". Based on such approach, the separation assurance protected zone implemented by the ASACAS is a cylindrical volume of airspace centered on the aircraft, with a horizontal radius of 0.5 NM and a vertical height of 500 ft, as represented in Figure 3.  
In addition to such separation assurance volume nominal dimensions, suitable extra-sizes are considered due to the uncertainties affecting sensor measurements and aircraft maneuvering. These additional extra-sizes are obtained by means of an incremental sizing factor that increases separation volume dimensions according to the closure rate between the conflicting aircraft. Therefore, the protected airspace zone is obtained as the sum of the nominal (minimum) size and the closure rate-dependent calculated extra-size, resulting in a variable overall size due to the variable closure rate between the considered aircraft.
## 4 Results and Discussion  
### SSA-ISVR-Based Prediction Results  
Based on the online predictions, the temperature prediction evaluation metrics, including \(RMSE\), \(MAE\), and \(R^{2}\), for multiple blocks were obtained, as shown in Table 4. Figures 9-11 illustrate the predicted concrete temperatures for blocks 7, 11, and 17.  
From the perspective of the overall model performance, SSA-ISVR achieved excellent predictive accuracy. As shown in Table 2 and Figures 9-11, during the pre-training phase, the model yielded an average \(RMSE\) of 0.189, an average \(MAE\) of 0.08 \({}^{\circ}\)C, and an average \(R^{2}\) of 0.994. During the prediction phase, the average evaluation metrics across multiple pouring blocks for SSA-ISVR were \(R^{2}\) = 0.992, \(RMSE\) = 0.172, and \(MAE\) = 0.097 \({}^{\circ}\)C.  
Figure 9: Monitoring point in 7: (**a**) temperature of 7#-026; (**b**) the regression results of the SSA-ISVR model in 7#-026; (**c**) temperature of 7#-030; (**d**) the regression results of the SSA-ISVR model in 7#-030. In subfigures (**b,d**), color gradient: yellow = high data density, red = Medium density, black = low density, the same below.  
In the temperature prediction for a single concrete pour, the prediction error of the SSA-ISVR model (see Figures 9-11) was more pronounced during the rapid temperature change phase of the concrete. This was due to the highly exothermic hydration during the early stage of cement. Furthermore, the hydration process was influenced by various construction conditions (such as ambient temperature and temperature control measures), which exacerbated the dynamic temperature variations, causing more prediction errors in contrast to the more stable temperature change phase.  
In summary, despite these challenges, the predictive accuracy of SSA-ISVR remains sufficient to meet the requirements of specific engineering applications.  
Figure 10: Monitoring point in 11: (**a**) temperature of 11#-001; (**b**) the regression results of the SSA-ISVR model in 11#-001; (**c**) temperature of 11#-012; (**d**) the regression results of the SSA-ISVR model in 11#-012.