esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Moreover, since the addition of SF leads to a high water demand for RPC mixtures, it is necessary to add SP to maintain the slump level of RPC. [PERSON] [5] suggested that the optimum WB range for RPC is from 0.15 to 0.24 in order to achieve its required flowability along with the use of SP. On the other hand, many researchers have studied the characterization of RPC mixtures with the addition of steel or other fibers. Since RPC is a strong and brittle material, fibers are typically added to the RPC mixture to improve its tensile strength and ductility. Steel fibers in RPC change the failure behavior of the material from sudden explosive to ductile, which makes it more suitable for many applications [19; 20]. Typical steel fiber content and a steel fiber to total binder ratio are 190-250 kg/m\({}^{3}\) and 0.15-0.30 (by weight), respectively [5; 11; 14]. Although many researchers around the world have investigated the properties of RPC, including mixture proportion, mechanical characteristics, and durability, a wide implementation of RPC as a construction material into construction business is limited because of its relatively high cost of production [21; 22]. The addition of expensive steel fibers and the special treatment of a curing regimen influence the overall cost of RPC [8]. Moreover, since RPC is a newly emerging material, there is no standard procedure for mixture proportioning and the determination of material properties. As a part of research studies using RPC as a material for an energy storage pile foundation system [23; 24; 25], this study focuses on the determination of properties of RPC without the addition of steel fibers in normal temperature and warm temperature curing regimes. For the energy storage pile foundation system, the hollow sectioned pile is required to store the excess energy produced in off-peak hours in the form of compressed air. A reduced cross-sectional area and additional tensile stresses coming from the pressurized air inside the pile require the pile to withstand complicated loading conditions. Therefore, RPC has the potential to be a suitable material because of its considerable high compressive, tensile strengths, and low porosity for bearing, crack prevention, and air tightness. This study has focused on a mixture proportion of RPC without the addition of steel fibers to achieve an appropriate compressive strength under normal and warm temperature curing conditions. Fresh properties such as the flowability and setting time have also been investigated in this study. Moreover, the drying shrinkage of RPC due to its high cement and SF contents has been checked. To optimize the mixture proportion of RPC, the response surface method (RSM) has been selected. To check whether RPC is applicable for the energy storage pile foundation, extensive finite element simulations were conducted to determine critical stress demands in the pile foundation as discussed in [PERSON] et al. [26]. This paper compares the strength of the RPC achieved in this study to the maximum stress demand obtained from analytical simulation.
## 4 Conclusions  
A novel and simple TWA SPME-based method for the quantification of acetic acid in ambient air was developed. The following conclusions can be drawn:  
* An SPME fiber exposed inside a glass liner followed [PERSON]'s law of diffusion. There were linear relationships between mass of the analyte extracted and extraction time up to 12 h (\(R^{2}\) > 0.99), and mass extracted and the inverse of retraction depth (\(1/Z\)) (\(R^{2}\) > 0.99). The amount of VOC adsorbed via the TWA SPME using a GC glass liner to protect the SPME was reproducible.
* There was no statistically significant difference between cleaned and \"saturated\" (equilibrated) glass liners. Thus, no special precautions are recommended for a practical application of this approach.
* The 85-\(\upmu\) Car/PDMS fiber revealed a higher response than the DVB/Car/PDMS fiber. The mass extracted by Car/PDMS was 8.9% higher than the mass extracted by the DVB/Car/PDMS fiber coating.
* The limit of detection (LOD, S/N = 3) and limit of quantification (LOQ, S/N = 5) were 10 and 18 \(\upmu\)g\(\cdot\)m\({}^{-3}\) (4.3 and 7.2 ppbV), respectively.  
Figure 8: Field air sampling in TWA SPME mode on a commercial swine farm (Farm 2) in Iowa. The air sample diffuses through the opening in the GC glass liner (left side of the photo) onto an SPME fiber fully exposed inside the liner. A short section of Teflon tubing and a half-hole septum seal the liner and facilitate SPME insertion. A clear glass vial encloses the SPME assembly from dust and other gases in the sampled air.  
* The new method was evaluated under field conditions by comparing it to the standard method (sorbent tubes) in four different locations. The TWA SPME sampling with a glass liner showed a reasonable match with the sorbent tubes.  
The method shown is a relatively simple and practical, yet accurate sampling technique for the quantification of acetic acid in both an indoor workplace and a swine farm building. The method is reusable. Further research should be done to extend the number of odorous VOCs that can be used with this method, allowing further improvement of TWA SPME modeling (e.g., Reference [22]), and the incorporation of temporal changes in sampled air on TWA SPME [37].  
Investigation, M.T. and J.A.K.; methodology, M.T. and J.A.K.; software, D.L.M. and S.R.; resources, D.L.M. and J.A.K.; writing--original draft preparation, M.T.; writing--review and editing, M.T., J.A.K., B.K., and S.R.; visualization, M.T. and S.R.; supervision, J.A.K.; funding acquisition, M.T. and J.A.K.  
This research was partially supported by the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa. Project No. IOW05556 (Future Challenges in Animal Production Systems: Seeking Solutions through Focused Facilitation) is sponsored by the Hatch Act and State of Iowa funds.  
The authors would like to thank the Ministry of Education and Science of the Republic of Kazakhstan for supporting [PERSON] with an M.S. study scholarship via the Bolashak Program.  
The authors declare no conflicts of interest.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 6 Directions for Future Research
### Smart Forests  
One idea raised during the course of this paper was that of smart forests, whereby you could use a relatively simple unmanned aircraft, but where there are technological aids built around the forest environment to facilitate quasi-BVLOS flight. For example, the use of electro-optical cameras installed around the forest area that can be used to track the unmanned aircraft such that an artificial line of sight is maintained, similar to with EVLOS+. This may be easier from a regulatory point of view and may also be cheaper than having an aircraft with the necessary DAA capabilities. However, that is uncertain until such an idea is trialled and costs and challenges ascertained. The authors argue that such research would make a useful and pragmatic contribution to the literature.
## 4 Assessment Framework Based on Complex Networks and Fuzzy Petri Nets
### Complex Network-Based Topological Modeling  
Classical security systems are spatially and functionally stratified into four concentric defense layers (from outer to inner): **Perimeter**, **Surveillance Zone**, **Protection Zone**, and **Restricted Zone**. A successful intrusion typically requires sequential breaching of these four layers. Each layer comprises multiple subzones:  
1. **Perimeter**: Includes fencing/walls and access points for vehicles and personnel.
2. **Surveillance Zone**: Area between perimeter and buildings, including gathering points, parking lots, and outdoor pathways.
3. **Protection Zone**: The building area containing critical assets, including building exteriors and internal passageways.
4. **Restricted Zone**: Specific rooms containing core assets; unauthorized access to this zone signifies system failure.  
It should be noted that a Protection Zone may encompass multiple buildings, while a Restricted Zone may involve several rooms. This multi-layered spatial configuration significantly increases the complexity of physical protection systems.  
Figure 4: Evaluation Flowchart.  
Based on the spatial layout and functional characteristics of the PPS, the system can be abstracted as a directed graph G = (N, E), where  
1. Node N: represents sub-areas with defined functions and spatial boundaries within each PPS protection layer.
2. Edge E: denotes the pathways primarily connecting different functional areas within the PPS.  
In the PPS complex network model, the graph contains multiple source nodes (representing Perimeter sub-areas) and terminal nodes (representing Restricted Zones that house critical assets). A connected path from source to terminal nodes constitutes a complete intrusion path, with multiple potential paths existing between them.  
Based on the Chinese national standards GB 50348-2018 [40] and GJB 7674-2012 [41], along with industry standards such as GAT 1093-2013 [42], GJB 6118-2007 [43], GAT 1399.1-2017 [44], GAT 1399.2-2017 [45], and GAT 992-2012 [46], and incorporating field investigations of PPSs at various military-industrial facilities, museums, large stadiums, and educational institutions, a two-level indicator system has been developed, as detailed in Appendix A. This system is designed to quantify the protective capacity of sub-areas represented by nodes. The first-level indicators define the PPS protection zones, which form nodes and edges within the complex network, while the second-level indicators correspond to specific PPS components and their attributes that affect the protective capabilities at the first level.  
Second-level indicators are further classified into three dimensions: Detection, Delay, and Response. According to Appendix A Table A1, evaluators can quantify the protective capability of security zones represented by first-level indicators, with this quantified result defined as the protection score in this study. The protection scores serve as critical input parameters for the FPN model used to evaluate overall system vulnerability.
# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## 3 Methodology
### Architecture  
Our approach employs a Conv2D UNet architecture, which is comprised of tailor-made ConvBlocks for the encoder and decoder, along with MaskedConv3D layers inserted amid the skip connections.  
A ConvBlock is structured to include a multi-headed convolution, essentially meaning that it contains 3 Conv2D layers with \"same\" padding and respective kernel sizes of \(3,5,7\). These layers are then concatenated together on the channel dimension. Subsequently, a batch normalisation is performed, followed by the application of a ReLU activation function. The output is then restructured to reduce the height and width dimensions, and passed through a final Conv2D layer (also with \"same\" padding). The entire process is capped off with an additional batch normalisation and another ReLU layer. Figure 5 provides a visual representation of an encoder ConvBlock.  
The UNet architecture is composed of 8 of these ConvBlocks, with 4 each dedicated to the Encoder and Decoder. Skip connections are implemented between the layers, each of which traverses a MaskedConv3D layer before being concatenated with its corresponding layer. The encoder and decoder, which consist of Conv2D layers, treat each frame as an individual image, the temporal dimension is only considered during the MaskedConv3D layers. It is worth noting that this architecture largely mirrors a traditional UNet, with the key distinction being the introduction of the MaskedConv3D layers in the skip connections, lending a unique capacity for contextual inference to the framework. The appropriate  
Figure 4: This figure illustrates the concept of the MaskedConv3D layer used in the proposed MaskedSkipUNet architecture. The MaskedConv3D layer applies a zero mask to a subset of the temporal dimension in a 3D kernel. The kernel size and zero mask size are chosen analogous to the frame size. This creates a “hollow sphere” boundary around potential anomalies, allowing the model to infer the reconstruction of this space based on surrounding spatial and temporal information. The MaskedConv3D layer enhances anomaly detection by preventing anomalies from being inadvertently reconstructed and normalized, thereby improving their detectability within the video data.  
kernel sizes and mask dimensions for the MaskedConv3D layers were selected through empirical experimentation to balance temporal context and reconstruction quality. A visual of the architecture can be seen in Figure 1.
###### Abstract  
This paper studies the orbital pursuit-evasion-defense problem with the continuous low thrust propulsion. A control strategy for the pursuier is proposed based on the fuzzy comprehensive evaluation and the differential game. First, the system is described by the Lawden's equations, and simplified by introducing the relative state variables and the zero effort miss (ZEM) variables. Then, the objective function of the pursuer is designed based on the fuzzy comprehensive evaluation, and the analytical necessary conditions for the optimal control strategy are presented. Finally, a hybrid method combining the multi-objective genetic algorithm and the multiple shooting method is proposed to obtain the solution of the orbital pursuit-evasion-defense problem. The simulation results show that the proposed control strategy can handle the orbital pursuit-evasion-defense problem effectively.  
Keywords:differential game; fuzzy comprehensive evaluation; continuous low thrust; zero effort miss variables +
Footnote †: journal: Applied
## 5 Experimental Results
### Middle Stage: Treatment Agent and Water-Based Adhesive Spraying Station  
After completing the 3D scanning process and generating the corresponding spraying path in the previous station, the lasted upper proceeds directly to the second stage, where a treatment agent (primer) and water-based adhesive are sequentially applied. This spraying station is operated by four six-axis robotic arms, arranged in two parallel pairs--one pair dedicated to spraying the treatment agent, and the other to spraying the adhesive.  
The external appearance of this station is shown in Figure 25, where the lasted upper enters from the right side and exits on the left. The reason for pairing the robotic arms is to accommodate the higher processing speed of the outside gluing line. To keep pace with the outside process, the system assigns the left and right lasted uppers to separate robotic arms immediately after scanning. This setup enables the near-concurrent spraying of both sides, thereby maintaining an efficient production flow without bottlenecks. The treatment agent is applied by the following steps:  
1. The lasted upper (L.U.) is first transferred via a rotary table, where it is rotated and tilted in preparation for being gripped by the robotic arm at the primer station, as shown in Figure 26a.
2. The robotic arm then grips the shoe last, maneuvers it close to the fixed spray head, and follows the spraying path generated in the previous station to apply the treatment agent, as illustrated in Figure 26b.
3. Upon completion of the treatment agent spraying, the robotic arm places the lasted upper onto a supporting mechanism, as shown in Figure 26c.
4. Once the lasted upper is laid flat, a transfer mechanism grips the shoe last and places it onto the conveyor system, as depicted in Figure 26d. The lasted upper is then placed into the oven to undergo the second drying stage.  
After completing the second drying stage, the lasted upper is sequentially transferred to the water-based adhesive spraying station. This station adopts the same robotic configuration as the treatment agent station, with a pair of six-axis robotic arms responsible for spraying the left and right lasted uppers, respectively. The robotic arms grip the shoe last, maneuver it close to the fixed adhesive spray head, and follow the previously planned spraying path to apply the water-based adhesive.  
The spraying process mirrors that of the treatment agent station in both sequence and motion control. However, due to the viscosity and transparency of the water-based  
Figure 25: External view of the treatment agent and adhesive spraying station.  
adhesive, special attention is given to ensure consistent coating coverage. After the spraying is complete, the lasted upper is placed onto a supporting platform and then transferred onto the conveyor system for the final drying stage in the shared oven.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 4 Results
### Spatial Distribution of Irrigated and Rainfed Wheat in South Central Shanxi Province
Spatial Distribution of Sowing and Maturity Dates of Irrigated and Rainfed Wheat in South Central Shanxi Province  
Spatial variations in sowing and maturity dates are associated with various factors, such as water conditions (_i.e._, irrigated or rainfed conditions), latitude and elevation [61; 62]. The sowing dates of rainfed and irrigated wheat in south central Shanxi Province were mainly between 17 September and 10 October 2010 (Figure 8a,b). Rainfed and irrigated wheat exhibited similar spatial patterns with respect to sowing dates, increasing from southwest to northeast and were significantly related to elevation (rainfed: y = -0.0082x + 269.4273; irrigated: y = -0.0124x + 272.6023; \(p<0.05\)). The maturity dates of rainfed wheat were mainly from 2 to 21 June, whereas irrigated wheat matured between 8 and 29 June (Figure 8c,d). Rainfed and irrigated wheat exhibited different spatial patterns with respect to maturity, and the overall maturity dates of irrigated wheat was 15 days later than that of rainfed wheat. The maturity dates of rainfed and irrigated wheat were significantly related to elevation (rainfed: y = 0.0077x + 160.6768; irrigated: y = 0.0197x + 160.0829; \(p<0.05\)), increasing from southwest to northeast, and irrigated wheat varied faster than rainfed wheat. Figure 7: Spatial distribution of maximum NDVI (**a**) and TIN (**b**) of wheat in south central Shanxi Province.
## 3 Proposed Broadcast Event-Triggered Consensus Control Scheme
### Event-Triggered Controller, \(\gamma\)  
The event-triggered controller was designed as part of the agent distributed consensus protocol (Equation (14)) to reduce the number of communication channels while reaching an average consensus (Equation (6)) among connected agents. Since agent \(i\) is connected to its neighbour \(j\in N_{i}\) via a strongly connected undirected graph topology (Figure 2), the agent will then exchange its current state value \(x_{i}\) with its neighbour's state value \(x_{j}\) if it violates the ETF, which leads to the consensus of the average point (Equation (6)) and the desired target point (Equation (8)). The ETF of this system is represented by Equation (14), where \(f(.)\) represents the error function, whereas \(\sigma(.)\) represents the threshold function. The state measurement error (Equation (15)) depends on the difference between the agent state's position, where \(t\in\ i_{k}^{i},\ i_{k+1}^{i}\) denotes the event instant of agent \(t_{k}^{i}(k=1,2,\ldots\ldots\ldots)\) and \(t\) refers to the previous event. The threshold function (Equation (16)) is known as state-dependent since the value depends on the agent's state position.  
The sampling process, transmission of information and updating of the controller of agents \(i\) and \(j\) with ET is shown in Figure 5. The sampler will first sample \(n\) the state of \(x_{i}(t)\), which becomes \(x_{i}(nt)\) at every periodic odd sampling time \(t\). Based on the given samples, the event detector will monitor the condition of the event, where the agent state of \(x_{i}(t)\) need to be transmitted to its neighbour or not, based on Equation (14). If the event detector \(i\) detects an event, \(k\), in sample \(n_{kj}\), the new updated sample state \(x_{i}(n_{k+1}t)\) of agent \(i\) is sent to neighbours \(j\) giving sample state \(x_{j}(n_{k}t)\). When neighbours \(j\) receive the state sent by agent \(i\), the neighbours \(j\) update the agent \(i\) state information and store the newly received state values of agent \(i\), \(x_{i}(nt)\). This state will then be used by the controller and event detector of agent \(j\) until the next event is triggered from agent \(i\). When no event occurs, this means that it fulfils the condition of Equation (14), then \(x_{i}(n_{k}t)\) is directly fed into the controller, which means there is no transmission and controller update (Equation (1)) that occurs at this time. The zero-order hold will ensure that the control signal is kept constant until the next event occurs.  
\[f(e_{i}(t),x_{i}(t))\ \geq\ \sigma_{i}(z_{i}(t)) \tag{14}\]  
\[e_{i}(t)=x_{i}\left(t_{k}^{i}\right)-x_{i}(t) \tag{15}\]  
\[z_{i}(t)=\sum_{j\in N_{i}}a_{ij}\ (x_{i}(t)-x_{j}(t)) \tag{16}\]
# An Introduction to Nonlinear Integrated Photonics: Structures and Devices
## 5 Conclusions
Quantum technologies may well exploit other materials as well, and we refer interested readers to an extended review on the 2023 roadmap for materials for quantum technologies, published in January 2023 [209]. Returning to use of Si or SiC, [PERSON] et al. experimentally demonstrated nonclassical optical bistability in a photon-pair source using cw-laser-pumped spontaneous FWM in an all-pass microring resonator with 100 \(\upmu\)m radius fabricated on an SOI platform [206]. Using similar microring (110 \(\upmu\)m radius) with Q-5 \(\times\) 10\({}^{4}\) and free spectral range (FSR)-100 GHz, matching the standard International Telecommunication Union (ITU) frequency grid to enable multichannel photon-pair generation compatible with commercial WDM devices, the same research group showed how frequency correlation can be applied to high-dimensional encoding for parallel quantum key distribution (QKD) [207]. More recently, various works have underlined the strong potential of 4H-SiC platform for on-chip quantum photonics; it was also demonstrated that implantation of silicon vacancy centers in SiC does not deteriorate their intrinsic spin-optical properties [210]. This result makes feasible development of large-scale quantum networks based on integrated quantum computational clusters with efficient spin-photon interfaces. [PERSON] et al. reported both strong enhancement of emission from a single color center in a nanophotonic cavity and efficient nonlinear frequency conversion in the same platform, also demonstrating full compatibility of thin-film SiC with industry-standard nanotechnologies and foundry production [208]. Still using a SiC microring structure, [PERSON] et al. studied the underlying quantum processes of soliton microcombs, investigating the quantum formation dynamics of dissipative Kerr soliton (DKS) states and their quantum correlations [211]. Obviously, theoretical advances are being made as well; as an example, [PERSON] et al. have now (February 2023) published a paper where they propose to use a resonator with only passive optical components to scale up stimulated emission of polarization-entangled photon pairs [212]; they present only a proof-of-principle experimental demonstration with a double-pass polarization Sagnac interferometer and periodically poled KTP crystal, but use of waveguide structures should lead to realization of highly efficient and bright quantum-entangled photon sources needed for quantum information technologies. Conceptualization, L.S. and G.C.R. ; writing--original draft preparation, L.S. and G.C.R. ; writing--review and editing, L.S. and G.C.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.