esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Analysis  
In this section, we analyze dependencies in path loss data in different typical outdoor deployments and channel states. We start this section by describing the utilized ray-tracing tool and experimental setup, and then proceed with the data representation and statistical data analysis.
# A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
## 4 Validation
### Comparative Simulation
#### 4.1.1 Scenario 1: Without Measurement Noise  
In this scenario, a relative ideal situation is simulated in which only external disturbances are acting on the quadrotor. Figure 2a-c show the simulation results for the attitude angle, the angular velocity, and the control input torque, respectively. The attitude transient-response specifications are listed in Table 2. To compare steady-state performance, the root mean square error (RMSE) of the Euler angles are calculated for both controllers. Table 3 presents the steady state RMSEs of the MFOLC and OLC from 10 to 100 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Specifications**} & \multicolumn{1}{c}{**MFOLC**} & \multicolumn{1}{c}{**OLC**} & \multicolumn{1}{c}{**Units**} \\ \hline \multirow{3}{*}{Maximum} & Roll & 0.11 & 0 & \\  & Pitch & 0.19 & 0 & \% \\ \cline{1-1} \cline{3-5}  & Yaw & 0.03 & 0 & \\ \cline{1-1} \cline{3-5}  & Roll & 3.27 & 4.12 & \\ \cline{1-1} \cline{3-5}  & Pitch & 3.125 & 3.81 & s \\ \cline{1-1} \cline{3-5}  & Yaw & 3.465 & 4.375 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attitude transient-response specifications without measurement noise.  
Figure 2: Evolution of (**a**) attitude tracking; (**b**) angular velocity tracking; (**c**) control torques, without measurement noise.  
In Figure 2a,b, both the MFOLC and OLC can achieve a control objective within 5 s. In Table 2, the proposed MFOLC has a shorter settling time and a slight overshoot, and a smoother angular velocity trajectory. It is worth noting that the MFOLC achieves such fine performance without priori information, compared to the OLC which uses the exact inertia matrix. This is because the MFOLC uses a more flexible and intuitive parameter system.  
In Table 3, the attitude steady-state error of the MFOLC is improved by 87.26% compared to that of the OLC. This is because the OLC algorithm contains the feedforward term that depends on the inertia matrix. Additionally, in the simulation, the inertia matrix has some unknown uncertainty, which affects the steady-state performance of the OLC algorithm.  
In Figure 2c, the MFOLC also requests a smaller control torque than the OLC, and the duration of the control torque to reach the limit is significantly less than that of the OLC. This means a lower energy consumption for the quadrotor.
## 3 Numerical Simulation
### Load Analysis and Boundary Conditions  
The accident happened at the beginning of the construction of second-phase concrete. Considering the complex structure of the SCS, 24 grouting and exhaust holes were designed on the stay ring. The diameter of the holes was 29 mm, and six of them were within the second quadrant. The accident happened during the construction of concrete in the second quadrant, and the in-situ condition showed that five out of the six holes were blocked. What is more, the concrete in the accident area was high-flowing concrete. Based on the detailed accident conditions stated above, experts on the site believed that the direct cause of this accident was poor quality control of the concrete construction. Under continuous pumping of concrete, the lack of ventilation triggered a boost in the pressure on the SSC, leading to raising deformation [18]. Since the concrete of the first and third quadrants of the first layer has set by the time of the accident, the pressure from external load can only exist in in the second quadrant. Accordingly, the range of the external load is thus determined, and it is in accord with the construction arrangements.  
Given that the external load was unexpected, there was not any direct record of its specific value. Fortunately, the range of the external load was explicit, and the authors managed to acquire the equivalent value through back analysis. With the value of the external load at 1.8 MPa, the FE results encouragingly agree well with the in-situ measured data in terms of the raising displacement. Thus, the range and value of the load is shown in Figure 8.  
According to the construction procedure of a powerhouse, the mass concrete below the altitude of 354.60 m has already set, as is shown in Figure 5. Meanwhile, the tie rods are fixed to the same altitude during the construction of SCS. Therefore, the mass concrete below the altitude of 354.60 m is considered as a fixed bottom boundary for the model, and displacement constraints are applied to the bottom of the FE model.
## 1 Introduction  
### Metallic Nickel  
Over the past years, a plethora of studies have appeared searching for nanomaterials with unusual properties that are distinct from those of bulk materials. Nano-structured metals have applications in many fields including homogeneous and heterogeneous catalysis [1; 2; 3; 4], fuel cells [5; 6; 7; 8], nanoelectronics [5], optics [6], magnetism [7] and other areas of material technology [9; 10].  
The synthesis of nickel nanoparticles and their characteristics have been studied extensively over the past 10 years due to the fact of their unusual properties and potential applications in various fields. Nano-sized nickel is now used in diverse areas such as catalysts, batteries, and in super-alloys. The finer the size of the catalytic material is, the higher its catalytic activity. As an example, 30 nm nickel particles synthesized optically active 3-hydroxybutyric acid methyl ester where the ensuing yield rate was as high as 85%, and the reaction was up to 15 times higher than that of non-nano nickel catalyst for the same reaction [11].  
A variety of methods have been reported for the preparation of nano-sized particles including reduction of metal oxide salts [12], decomposition of carbonyls [11], and solution reduction using various reducing agents [13].
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 3 The Time-Division Multiplexing Problem
### System Model  
HAP is equipped with both LTE and Wi-Fi communication interfaces, as shown in Figure 1. The HAP is an eNB capable of the function of Wi-Fi AP. Thus, the HAP can coordinate the spectrum allocation and manage the switching of interfaces between Wi-Fi and LTE. The Wi-Fi interface of HAP can only access the unlicensed band. In contrast, the LTE interface of the HAP can access both licensed and unlicensed bands through carrier aggregation technology.  
The scenario for the coexistence of Wi-Fi and LTE in an indoor environment is shown in Figure 2. In this scenario, there is one LTE eNB, \(N_{UE}\) LTE users, \(N_{HAP}\) hyper-APs, \(N_{AP}\) access points, and \(N_{STA}\) Wi-Fi stations. We employ a new wireless AP architecture called Hyper-AP (HAP) [15; 16]. There are two types of Wi-Fi stations, namely QSTA and non-QoS STA, depending on whether they have QoS support. Among the \(N_{STA}\) Wi-Fi stations, there are \(N_{QSTA}\) QSTAs.  
Figure 1: Hyper access point (HAP) architecture.  
Figure 2: A scenario of a coexistence environment.  
To be consistent with the LTE-U standard, this paper adopts the concept of carrier sense adaptive transmission (CSAT) to control the time-division multiplexing between LTE and Wi-Fi. The LTE-U standard, proposed by Qualcomm, enables LTE users and Wi-Fi stations to effectively and fairly access the unlicensed band. An unlicensed band can be divided into repeated Repetition Intervals in the time domain. Each repetition interval can be further divided into a contention period (CP) and a contention-free period (CFP), as shown in Figure 3. LTE users are allowed to access the unlicensed band during the contention-free period with the point coordination function (PCF) mechanism. On the other hand, Wi-Fi stations can transmit data using the traditional CSMA/CA mechanism during the contention period. The length of the repetition interval is denoted by \(L\) in Figure 3. We denote the ratio of the CP to the repetition interval by \(\delta\). That is, \(\delta=CP/L\). The ratio for the contention-free period is then \((1-\delta)=CFP/L\).
## 3 Experiment Description and GNSS Data Processing
### GNSS Data Processing Strategy  
In this paper, the precise pointing positioning (PPP) technique [29; 30] is used to process the multi-GNSS observations by the conventional ionosphere-free (IF) code and phase measurements based on the multi-GNSS PPP software developed by our research group, which has been validated with the amount of GNSS observations [31]. The receiver coordinates, the troposphere zenith wet delay, the receiver clock error and the IF phase ambiguities are regarded as unknown parameters, which are estimated using the extended Kalman Filter. The station coordinates were estimated in static mode, and the strategy of GNSS PPP is summarized in Table 2. The orbit and satellite clock errors are corrected using the final products downloaded from [[ftp://ftp.gfz-potsdam.de/](ftp://ftp.gfz-potsdam.de/)]([ftp://ftp.gfz-potsdam.de/](ftp://ftp.gfz-potsdam.de/)). Some points need to be noted that [32; 33]: (a) one more inner-frequency bias (IFB) parameter is introduced for each GLONASS satellite during the data processing due to its application of FDMA Frequency Division Multiple Access; (b) one more system bias, which refers to the inter-system bias (ISB), is added for BDS observation with respect to GPS system. The prior variances of GNSS observations are determined as follows:  
\[\sigma=\sigma_{0}^{2}\Big{(}1+1/sin^{2}e\Big{)}, \tag{14}\]  
where \(e\) is the satellite elevation angle, the values of \(\sigma_{0}\) for GPS are set to 0.3 m and 0.003 m for the original pseudorange and phase observables, respectively. The values of \(\sigma_{0}\) for GLONASS pseudorange observables is enlarged as 0.9 m while the value for GLONASS phase data can be set as the same as that of GPS [34]. As for the BDS observations, the values of \(\sigma_{0}\) for GEO (Geostationary Earth Orbit) satellite can be set as 1.2 m and 0.009 m for raw pseudorange and phase observables, respectively [35; 36]. For IGSO (Inclined Geosynchronous Satellite Orbit)/MEO (Medium Earth Orbit) satellites, the value of \(\sigma_{0}\) is set to 0.4 m for pseudorange observables [37], while the value of that for phase observations is the same as that of GPS [35]. The detailed description of processing strategy is listed in Table 2.  
One point should be noted that, when the ZTD parameter is estimated by the above strategy, the accurate surface pressure parameter is derived from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-Interim products with the resolution of 0.125\({}^{\circ}\)\(\times\) 0.125\({}^{\circ}\) so as to obtain the accurate ZHD parameter by Saastamoinen model [21]. Then, the ZWD is retrieved from ZTD by extracting the ZHD. Finally, the SWD is calculated based on the wet mapping function (NMF) [38] and the calculated ZWD parameter, which is considered as the input information for wet refractivity tomography [7; 39; 40].
# Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms
## 3 Solution Algorithms
### Key Element Design of Hybrid Metaheuristic Algorithms
#### 3.1.4. Infeasible Solution Repair Mechanisms  
In metaheuristic algorithms, every chromosome is required to represent a potential solution. However, following the crossover and mutation operations, certain generated chromosomes may no longer adhere to the given constraints, rendering them infeasible. Consequently, these chromosomes need to be repaired to regain their viability within the problem's limitations.
## 6 Performance Evaluation of ASC-RL
### Prediction Accuracy and Energy Efficiency with Combined Reward Score  
ASC-RL has been tested with using three performance metrics: prediction accuracy \((PA)\), energy efficiency \((EE)\), and a combined reward score \((CR)\). The values start with a range of 0.0 to 1.0 with increments of 0.01, as shown in Figure 8. This means that 0 values indicate that the sensors are inactive in saving energy. On the other hand, the value of 1 confirms that sensors are in any of the three states, with a maximum of \(S_{Act}\) state sensors. With the increasing \(S_{Act}\) states of multiple sensors, the PA increases (blue color) because more \(S_{Act}\) sensors collect more data and information, resulting in more accurate results. But the results do not follow linear growth, with little fluctuation. This deviation in obtained values may be due to some environmental factors like noise and interference. The weighted accuracy and efficiency (red color) is the reward score over \(E_{P}=0.3\) and \(A_{P}=0.7\), and indicates that the expected \(PA\) is approximately 70%. However, at this stage, \(EE\) is around 30%, which reveals a balance between resource conservation and predictive performance. With increasing activation of many \(S_{Slp}\) sensors, it increases the \(PA\), but a broad activation of all sensors may affect the \(EE\) due to continuous sending. First, the reward curve increases up to a peak level and then starts declining in the region of 0.6 and 0.8 activation levels inside the network.  
Figure 7: ASC-RL event correlation.
## 4 RIPK1-Mediated Necroptosis  
Necroptosis, a pro-inflammatory form of programmed cell death, is characterized by cell membrane rupture and the release of damage-associated molecular patterns (DMAPs). While not essential for embryogenesis, necrotosis plays a vital role in immune defense against pathogens and is implicated in various human diseases, including inflammation, tissue damage, and neurodegeneration [39].  
Necroptosis is initially suppressed by apoptosis, primarily through the cleavage of RIPK1 by activated Caspase-8 in complex II, for example, when induced by TNF and Smac-mimetic or CHX [40; 41]. However, when Caspase-8 is inactivated by specific inhibitors (such as Z-VAD-FMK) or genetic elimination, activated RIPK1 in complex IIb recruits RIPK3 through their respective RHIM domains, initiating the formation of another protein complex, called the necrosome (Figure 2) [5; 42; 43; 44; 45; 46]. Oligomerized RIPK3 then recruits the casein kinase 1 family proteins CK1\(\alpha/\delta/\varepsilon\), which phosphorylate Ser227 of human RIPK3 [47]. Phosphorylated RIPK3 subsequently recruits MLKL and phosphorolates human MLKL at Thr357 and Ser358 [48; 49]. Consequently, MLKL undergoes oligomerization into tetramers and amyloid-like polymers, which translocate to the plasma membrane, resulting in plasma membrane permeabilization [50; 51; 52; 53; 54; 55]. In addition, activated MLKL translocates to the lysosomal membrane, where it forms amyloid-like polymers to facilitate lysosomal membrane permeabilization and the release of lysosomal proteases, thereby promoting cell death [57].  
RIPK1-mediated necrotosis requires its kinase activity, similar to its involvement in RIPK1-dependent apoptosis. For example, the RIPK1 inhibitor Nec-1 effectively prevents necrotosis induced by TNF, TLR ligands, and interferons [43; 58; 59]. Moreover, mice with kinase-dead RIPK1 knock-in mutations are resistant to TNF-induced necrotosis and systemic inflammatory response syndrome (SIRS), similar to RIPK3 knockout mice, and demonstrate superior resistance compared to MLKL knockout mice [38; 60; 61].  
Multiple innate immune signaling molecules, including death receptors (such as TNFR1), pathogen recognition receptors (such as Toll-like Receptor TLR3 and TLR4), and the cytosolic RNA sensor ZBP1, can induce necrotosis. Activation of these pathways leads to the interaction between the RHIM domains of proteins, such as RIPK1, TRIF,or ZBP1, with the RHIM domain of RIPK3, activating RIPK3 and MLKL to promote neeroptosis [58; 62; 63; 64].  
The role of apoptosis in suppressing neeroptosis is crucial for embryonic development. Deficiency in apoptosis components, such as the knockout of Caspase-8 or FADD, often results in late-gestation embryonic lethality, primarily due to hyperactivity of neeroptosis [65; 66]. Simultaneous deletion of RIPK3 or MLKL can rescue the embryonic lethality of these mice, albeit with immune deficiencies in adulthood [67; 68; 69].
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 4 Methodology  
Here, the training process and network frameworks of the proposed IQDN and SISNet are laid out. Then we elaborate on the metrics for evaluating performance.  
Figure 8: Schematic diagram of UNet.  
Figure 7: Schematic diagram of FCN network.
## 5 Results
### Experiment 2: A Traffic Light Adjustment System for an Emergency Vehicle
As shown in Table 3, the number of processed frames decreased substantially across all scenarios. For instance, in Scenario 2 (tracking a firetruck along the route monitored by Cameras 7, 8, 10, 9, and 11), the always-on system performed inference on all 12 edge devices, processing a total of 20,472 frames. In contrast, the TEDDY framework only processed 1697 frames, distributed among Cameras 7 (127 frames), 8 (464 frames), 10 (150 frames), 9 (625 frames), and 11 (331 frames). This substantial reduction in processed frames demonstrates the effectiveness of the TEDDY framework in optimizing computational efficiency. Specifically, in Experiment 1, the framework reduced power consumption by 20.96% compared to the always-on approach, while in Experiment 2 it reduced the total computational load by over 90%, reflecting its ability to deactivate most cameras when they are unlikely to capture the target. The computational load reduction exceeded 90% across all scenarios, with slight variations depending on the route length. This trend clearly indicates that in the TEDDY framework no single camera processed all frames, and that not all 12 edge devices performed inference simultaneously. Instead, only the activated edge devices executed inference, leading to a significant improvement in computational efficiency. Since this experiment focused on single-object tracking, the computational demand decreased proportionally to 1/N, where N was the number of cameras in the network. In this experiment, with 12 edge devices, the target was present in only one camera frame at any given time, further enhancing computational efficiency. Moreover, the time required for the camera selection process in the TEDDY framework was minimal and had no impact on tracking performance. As the number of edge devices increases, the computational efficiency of the framework is expected to further improve, making it highly scalable for larger networks. Furthermore, we measured the actual inference counts of both the detection model and the Re-ID model and compared the overall computational load. The results confirm that the TEDDY framework maintained the same tracking performance as the always-on approach while reducing computational costs by over 90%. These results validate the effectiveness of the TEDDY framework in significantly reducing the computational load while maintaining high tracking accuracy.
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 2 Background and Related Work
### Ambient Intelligence, Internet of Things, and Vehicular Edge Computing  
AmI is the ensemble of technologies embedded in human environments to seamlessly offer assistance and prediction in various contexts through a human-computer interface [1; 2]. Examples of AmI environments are smart homes [12], connected vehicles [13; 14; 15], healthcare services [16], smart grid [17], and smart cities [18]. AmI technology realization is an interdisciplinary area that includes sensor networks, artificial intelligence, and human-computer interaction. IoT, which is an enabling technology for promoting the interconnection and exchange of data between heterogeneous objects in smart environ ments by creating a connectivity network between sensors, embedded devices, and human-computer interaction, serves as the cornerstone of AmI [2,3].  
With the advancement of AmI and IoT, new automotive innovations, e.g., autonomous vehicles, smart navigation, and cooperative cruise control, are starting to make modern transportation systems safer and smarter [3,19, 20, 21]. The traditional technology-driven transportation systems are turning into data-driven intelligent transportation systems, which produce a large volume of data, contributing to increased communication, computing, and storage demands as well as stringent latency and network bandwidth capacity criteria. VEC is emerging as a promising approach to overcome these problems [20,21]. VEC drives efficient computing and storage capacities from distant clouds to the edges of networks near vehicular users, allowing low response time and decreased bandwidth usage. As shown in Figure 1, the architecture of VEC typically includes three layers: user layer (connected vehicles), edge layer (Roadside Units--RSU), and cloud layer (cloud servers). Connected vehicles have communicating capability, computing, and storage resources of their own. Connected vehicles can communicate via Vehicle-to-Vehicle (V2V) communications or with the RSU via Vehicle-to-Infrastructure (V2I) communications. RSU, operating as edge devices, are located near vehicles for real-time gathering, analyzing, and storing traffic information. Since vehicles have limited resources, they may shift latency-sensitive and compute-intensive tasks to edge devices, reducing delay and heavy workload on backhaul networks.  
Since its inception, VEC has attracted a great deal of interest from scholars. Many works and surveys have examined different facets and applications of VEC, such as resource allocation [22], traffic routing [14,15], and intersection control [23]. [PERSON] et al. [20] discussed the VEC architecture's technical issues and provided several recent solutions and future research challenges in the field. [PERSON] et al. [21] reviewed recent research in VEC by classifying the research topics, including task offloading, caching, information sharing, dynamic network control, privacy, and security.