esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Outage Probabilities
### Scenario 3: Repeated Transmission through a Successful Relay  
In the third scenario, as shown in Figure 5, the source transmits data to the receivers in the group, and one of the candidate relays repeatedly transmits the data to the receivers, assuming that at least one of the candidate relays around \((x_{R},y_{R})\) can receive data from the source. With given source and relay positions \((x_{S},y_{S})\) and \((x_{R},y_{R})\), the outage probability for a receiver at location \((x_{D},y_{D})\), denoted as \(P_{Outage(3)}(x_{D},y_{D})\), can be written as:  
\[\begin{array}{l}P_{Outage(3)}(x_{D},y_{D})=1-\left\{\left(1-P_{SD}^{Outage} \right)+P_{SD}^{Outage}\Big{(}1-P_{RD}^{Outage}\Big{)}\right\}\\ =P_{SD}^{Outage}P_{RD}^{Outage}\end{array} \tag{32}\]  
since \(P_{SR}^{Outage}=0\). The outage probability considering all receivers in the group can be expressed as:  
\[\begin{array}{l}P_{Outage(3)}^{Group}=\frac{1}{N_{D}}\iint_{Group}P_{Outage( 3)}(x_{D},y_{D})f_{D}(x_{D},y_{D})dx_{D}dy_{D}\\ =\frac{1}{N_{D}}\iint_{Group}P_{SD}^{Outage}P_{RD}^{Outage}f_{D}(x_{D},y_{D}) dx_{D}dy_{D}\\ =\frac{1}{A_{Group}}\iint_{Group}P_{SD}^{Outage}P_{RD}^{Outage}dx_{D}dy_{D} \end{array} \tag{33}\]assuming a uniform distribution of receivers.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 4 Results and Discussion
### High Latitudes Simulations  
To verify the effectiveness of the satellite selection algorithms in different locations, especially in high latitudes, the receiver is moved to the Mohe Gulian Airport whose latitude is higher than 50 degrees in the north. The simulations in the fix subset size scene and minimal subset size scene are carried out in sequence and the 24-h statistical results are summarized in Table 6. During the 12 satellite subset trial, the three algorithms reach similar accuracy performance while the proposed algorithm outperforms the other two in integrity with an average 15 to 27 m smaller HPL. During the LNAV and APV-I requirement trial, the LNAV requirement is well met by the three algorithms and the proposed algorithm obtains an average 1.31 smaller subset. The APV-I integrity requirement is violated by the three algorithms over a period of time, which means the GPS/BDS-2 single-frequency navigation system alone may not be suitable for the APV-I operation. Even so, the proposed algorithm obtains an average 2.06 to 3.63 smaller subset beyond these discordant time.  
As a result, the proposed PRDFSS algorithm performs well on both accuracy and integrity performances regardless of fixed subset size trial or minimal subset size trial. When 12 satellites are selected in the GPS/BDS-2 navigation system, the proposed algorithm can reach a comparable 2 drms and an about 15 m lower HPL than the geometric algorithm and the downdate algorithm. When the LNAV and APV-I performance is required, an average 1.3 to 2.0 satellites smaller subset can be achieved by the proposed algorithm if compared with the other two, regardless of the latitude of the receiver. Besides, the average consumed times of the three algorithms are very close and short, so the proposed PRDFSS algorithm is suitable for performance-requirement-based navigation applications with limited real-time processing capability.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Problem Taxonomy and Applications of Video Processing in VBDA
#### 2.4.1 Object Detection and Tracking  
Object detection and tracking are foundational components of VBDA, enabling automated monitoring of entities such as people, vehicles, and objects in dynamic environments. These tasks underpin applications in surveillance, traffic control, and sports analytics [5]. Whereas early systems relied on handcrafted feature-based techniques such as HOG and SVM [58], recent approaches have embraced deep learning models like YOLO, Faster R-CNN, and SSD, which are specifically optimized for edge deployment and real-time inference [59; 60]. Tracking is often handled using DeepSORT [61], FairMOT [62], or Siamese networks [63], while 3D object detectors like PointNet [64] and VoxelNet [65] are gaining traction in autonomous driving and robotics.
# Operator and Graph Theoretic Techniques for Distinguishing Quantum States via One-Way LOCC
## 1 Introduction  
The communication paradigm called local (quantum) operations and classical communication, usually denoted by its acronym LOCC, is fundamental to quantum information theory, and includes many central topics such as quantum teleportation, data hiding, and many of their derivations [1, 2, 3]. The somewhat more restricted version called one-way LOCC, in which communicating parties must perform their measurements in a prescribed order, has received expanded attention due it being more tractable mathematically while still capturing many of the more important communication scenarios [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. A particularly important subclass of problems in this field involves the determination of when sets of known quantum states can be distinguished using only LOCC operations or some subset thereof.  
Our work in the theory of LOCC [10, 11, 12, 13] has for the first time brought techniques and tools from operator theory, operator algebras, and graph theory to the basic theory of quantum state distinguishability in one-way LOCC. Given the overlapping nature of some of our results and applications, including improvements on some results as our work progressed, we felt a review paper bringing together a selection of main features from our work could be a useful contribution to the literature. In addition to this exposition, we derive a new graph-theoretic description of one-way distinguishability in an important special case, that of a single-qubit sender.  
This paper is organized as follows. In Section 2, we give necessary preliminaries, including the mathematical description of one-way LOCC in terms of operator relations. Section 3 includes a brief introduction to the relevant operator structures in our analysis, and then we present some of our main results and applications from [10, 11, 12]. We finish in Section 4 by first giving a brief introduction to our necessary notions from graph theory, then we present one of our main results from [13] and some examples, and we derive a new graph-theoretic description for the case of a single-qubit sender.
## 3 Results
### Raman A\({}_{1}\) (LO) Phonon of GaN Epilayer  
As shown in Figure 5, observing the line width of the A\({}_{1}\) (LO) phonons of Raman spectra of GaN with different interlayers, the A\({}_{1}\) (LO) phonon lifetime can be determined from the FWHM based on the energy-time uncertainty relationship [33; 34], in order to analyze the growth factors that affected the GaN Raman scattering. The formula about FWHM and phonon lifetime is as follows [34].  
\[\frac{\Gamma}{\hbar}=\frac{1}{\tau}, \tag{2}\]  
in which \(\tau\) was the phonon lifetimes and unit was picoseconds, \(\hbar\) was the modified Planck constant (5.3 \(\times\) 10\({}^{-12}\) cm\({}^{-1}\) s), and \(\Gamma\) was representative to the true FWHM in units of cm\({}^{-1}\). With graphene/sputtered AlN interlayer, it is noted that A\({}_{1}\) (LO) phonons in the Raman spectrum (Figure 5a) were almost stretched, so the FWHM of the A\({}_{1}\) (LO) phonon scattering peak was almost invisible in this case. In addition, the FWHM of A\({}_{1}\) (LO) phonon mode was larger as the quality of the buffer layer improved. Under the constraint of the law of conservation of energy, Figure 5b was drawn by using the relationship between the FWHM of A\({}_{1}\) (LO) phonon and phonon lifetimes [34]. It was very clear that A\({}_{1}\) (LO) phonon lifetimes were shorter after the addition of the graphene layer, and that A\({}_{1}\) (LO) phonon lifetimes were shortest when GaN films were directly deposited on the graphene/sputtered AlN buffer layer (sample 1), which was in good agreement with the above results of Figure 5a. A more critical issue was the sufficient lifetime of longitudinal optical (LO) phonons greatly weakening the rate at which hot electrons released energy and inhibited carrier mobility. Hence, the A\({}_{1}\) (LO) phonon mode of Raman spectrum of GaN grown with graphene/sputtered AlN buffer layer had the shortest phonon lifetimes, which were most conducive to carrier migration in this situation. This also reflected that during the thermal movement of carriers, the probability of collisions with crystal lattices, impurities, defects, etc. was reduced, and that the probability of scattering was also reduced. The lifetime variations of A\({}_{1}\) (LO) phonons of GaN had been explored on different buffer layers, which contributed more to the subsequent research and preparation of high-speed optoelectronic devices dominated by photon-phonon carrier interaction on GaN films.
# Ring-Overlap: A Storage Scaling Mechanism for Hyperledger Fabric
## 6 Simulation Experiment
### Experimental Result
#### 6.3.3 Experiment 3  
The experimental results are shown in Figure 8, where the \(x\)-axis is the number of nodes and the \(y\)-axis is the total amount of storage. The graph shows that when the number of nodes increases, the storage space occupied by the ring-overlap mechanism is significantly reduced compared to the Fabric blockchain system and the scalable storage model.  
Despite the additional network overhead introduced by this solution, in a real Hyperledger Fabric application, unless the network state is extremely poor and network communication takes up more time in the whole query transaction process, the increased network communication overhead is well worth it relative to the storage space savings.
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 2 Methodology
### Orbit Accuracy Assessment  
The difference of orbit overlap is obtained as an internal accuracy indicator by comparing the two adjacent multi-day arc solutions, where the middle day of the first multi-day arc is compared with the corresponding day of the second multi-day arc. Meanwhile, the external accuracy of the multi-day arc solutions is evaluated by comparing the difference with microwave-based precise orbit derived from WUM. The orbit differences are decomposed into RTN directions at 15-min intervals, and their corresponding daily RMS are calculated. Affected by the different quantities of the SLR observation and the types of BDS satellites, the accuracy of the SLR-only orbit may sometimes be unacceptable. To obtain the credible average RMS of long-term time series, the RMS of multi-day arc solutions are eliminated empirically and regarded as a failure in SLR-only orbit determination when the average RMS exceeds the threshold, which is 1, 5, and 5 m in R, T, and N directions for BDS MEO satellites, and 2, 10, and 10 m for BDS IGSO, as well as 10, 50, and 50 m for BDS GEO C01. Then the corresponding average RMS is calculated as the overall accuracy indicators. Additionally, to reflect the dispersion of the average RMSs, an index--which refers to the proportion of the RMS values that are under a quarter of the RMS threshold--is defined as \(D\) -value. Its calculation equation is as follows:  
\[D=\frac{M}{N}\times 100 \tag{1}\]  
where \(N\) refers to the number of success solutions and \(M\) means the number of success solutions, the RMS of which is under the one-fourth RMS threshold.
## 4 Discussion
### Changes in the Evaluation Results of Three Stages in Urban Infrastructure
#### 4.2.2 States  
In Figure 4, only Chongqing had relatively high levels of state resilience development, with an apparent upward trend and leading ahead, while the others fluctuated steadily around 0.500. In other words, the overall state of infrastructure in China's municipalities all showed moderate health. Over the years, Chongqing was strived to build a comprehensive transportation hub in southwest China. Its transportation infrastructure had been continuously improved, manifesting that the number of public vehicles per 10,000 persons doubled in 2018 compared with the initial stage of the study. Besides, urban green space per capita also had increased at a rate of 0.14 square meters per year in 17 years. Thus, the evaluation results in Chongqing leapt from 0.518 in 2002 to 0.648 in 2018, with an obvious upward trend, shown in Table A3. Nevertheless, it was worth noting that there is still a significant gap in Chongqing compared with other municipalities, embodied by the inadequate public transport facilities and the incomplete coverage of water and gas. Incredibly, the number of public vehicles per 10,000 persons was far below the average level of municipalities.  
There was no significant difference in state resilience development among the remaining cities, as they were all at a moderate health state. The state resilience of infrastructure in Beijing and Shanghai was relatively vulnerable, only about 0.520, at a low level. As political and economic centres in China, Beijing and Shanghai had attracted a large population inflow. Over the past 17 years, the permanent population gross increased by 46.43% on average compared to 2000, well above the pace of infrastructure construction and improvement. As a result, the per capita indicators of infrastructure in these two cities had been in a low state, even a negative growth phenomenon, such as per capita area of paved roads. Secondly, the loss effect spread more widely after the disaster due to mass building density and more high-rise buildings in Beijing and Shanghai, especially in Shanghai. Compared with the initial stage, the losses converted into cash by fires in 2018 almost quadrupled, severely restricting the improvement of state resilience in Shanghai and increasing the possibility of its deterioration to the fragile state. Per capita area of paved roads and density of drainage pipe network in the built-up area in Tianjin were the highest among the four municipalities. Meanwhile, its ability to resist fire was also prominent, which was the main reason why it was superior to Beijing and Shanghai in state resilience. However, it was constrained by massive energy consumption, manifesting per capita consumption far exceeding the 66% average of the four municipalities above 66%.
## 2 Research Background  
YOLOv8n, released by Ultralytics on 10 January 2023, is designed to accomplish tasks within the realm of computer vision, encompassing picture classification, object detection, and image segmentation. To adapt to different application scenarios and computational resource requirements, with the increase in model complexity and network depth, it is sequentially divided into five versions: n, s, m, l, and x. The depth and width of the network models increase sequentially. Among them, although the YOLOv8n model has relatively lower accuracy, due to its small size and rapid detection speed, it is highly suitable for detection tasks.  
The YOLOv8n model consists of four core components: Inputs, Backbone, Neck, and Head, as illustrated in Figure 1. The Inputs component employs the Mosaic data augmentation technique, which adjusts certain hyperparameters dependent on the size of the model in use. This method effectively expands and enriches the dataset, consequently enhancing the model's generalization proficiency and enabling it to maintain stable performance when confronted with various unknown or complex scenarios, ultimately bolstering the model's robustness. In the Backbone and Neck components, the design approach of stacking multiple ELAN modules from YOLOv7 is referenced. The C3 module from YOLOv5 is modified to create the C2f structural module. While still maintaining a lightweight model, this significantly enhances the ability to capture gradient flow information and provides flexibility in adjusting the number of channels based on changes in model scale, resulting in a substantial improvement in model performance. In the Head component, compared to YOLOv5, the currently prevalent decoupled head structure is adopted. The model design separates the classification and detection heads, using two parallel branches to extract category feature information and location feature information, respectively. Then, it employs a 1 \(\times\) 1 convolution layer for each branch to complete the classification and localization tasks, significantly diminishing the model's parameter number, scale, and computational burden while enhancing its generalization ability and robustness.  
Figure 1: The Network Structure of YOLOv8n.  
YOLOv8n has abandoned the anchor-based detection method used in previous versions and adopted a new anchor-free detection strategy. Using this method, the midpoint coordinates and width-to-height proportion of the intended target can be directly predicted, greatly optimizing the detection process and significantly reducing the number of anchor boxes used. Therefore, YOLOv8n provides a more efficient and accurate solution for real-time object detection.
## 2 Method
### Trajectory Replanning Using Gradient-Based Optimization
#### 2.2.2 Dynamic Feasibility Cost Function  
The dynamic feasibility cost function ensures the trajectory adheres to physical constraints, such as a feasible airspeed and turn rate.  
Let \(\vec{S}_{v}=\begin{bmatrix}\vec{I}_{2}&\vec{0}_{2\times 1}\end{bmatrix}\) and \(\vec{S}_{\varphi}=\begin{bmatrix}0&0&1\end{bmatrix}\) be the selection matrices for the airspeed and turn rate, respectively. Then, we can design the dynamic feasibility cost function for the airspeed as  
\[\mathbb{J}_{d,v}=\sum_{i=0}^{n}\exp\left(\alpha_{v}(\|\vec{S}_{v}\frac{\vec{S} _{i+1}-\xi_{i}}{\Delta t}\|-v_{max})\right)+\exp\left(-\alpha_{v}(\|\vec{S}_{ v}\frac{\vec{S}_{i+1}-\xi_{i}}{\Delta t}\|-v_{min})\right) \tag{23}\]  
Similarly, we can design the dynamic feasibility cost function for the turn rate as follows:  
\[\mathbb{J}_{d,\omega}=\sum_{i=0}^{n}\exp\left(\alpha_{\omega}(\|\vec{S}_{ \varphi}\frac{\vec{S}_{i+1}-\xi_{i}}{\Delta t}\|^{2}-\omega_{max}^{2})\right) \tag{24}\]  
where \(\alpha_{v},\alpha_{\omega}\) are positive scaling factors. Thus, the dynamic feasibility cost function is represented as follows:  
\[\mathbb{J}_{d}=\lambda_{v}\mathbb{J}_{d,v}+\lambda_{\omega}\mathbb{J}_{d,\omega} \tag{25}\]  
where \(\lambda_{v},\lambda_{\omega}\) are the weighting coefficients.
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 3 Cimkg
### Co-Interactive Module
#### 3.2.2 Co-Interactive Attention Layer  
The co-interactive attention layer explicitly establishes a bidirectional relationship between intent detection and slot filling tasks. The co-interactive attention layer enablesintent detection and slot filling to interact with each other, so that the slot representation \(H_{S}\) is complemented by the intent information and the intent representation \(H_{I}\) is enhanced by the slot information.  
Specifically, the intent representation \(H_{I}\) and the slot representation \(H_{S}\), obtained from the intent and slot label attention layers, are mapped through different linear transformations to form query \((Q_{I},Q_{S})\), key \((K_{I},K_{S})\), and value \((V_{I},V_{S})\) matrices. Using \(Q_{I}\) as the query, \(K_{S}\) as the key, and \(V_{S}\) as the value, the dot product of the query vector \(Q_{I}\) and the transpose vector \(K_{S}^{T}\) of the slot key vector \(K_{S}\) is computed, scaled, and then passed through the softmax function. This is then multiplied by the slot value vector \(V_{S}\) to obtain the intent attention vector \(C_{I}\) containing slot information as shown in (9).  
\[C_{I}=softmax\left(\frac{Q_{I}K_{S}^{T}}{\sqrt{d_{k}}}\right)V_{S} \tag{9}\]  
The updated intent representation \(H^{\prime}{}_{I}\in\mathbb{R}^{n\times d}\), which incorporates slot information, is obtained by adding \(C_{I}\) to the original intent representation \(H_{I}\) and then applying layer normalization as shown in (10).  
\[H^{\prime}{}_{I}=LN(H_{I}+C_{I}) \tag{10}\]  
where \(LN\) denotes the layer normalization function.  
By aligning the intent with its corresponding slot information, a representation of the intent that contains the corresponding slot information is obtained. The degree of association between the intent and slot is measured using the attention weights, and the intent representation is adjusted to obtain the most relevant intent representation of the slot information.  
Similarly, using \(Q_{S}\) as the query, \(K_{I}\) as the key, and \(V_{I}\) as the value, the slot representation \(H^{\prime}{}_{S}\in\mathbb{R}^{n\times d}\) containing intent information can be obtained.
## 6 Discussion
### Comparison
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Simulation Case** & **Number of Nodes** & **Average Absolute Error** & **Error Standard Deviation** \\ \hline \multirow{4}{*}{All Node Neural Control (TICC)} & 5 & \(1.6117\times 10^{-5}\) & \(2.1981\times 10^{-6}\) \\  & 10 & \(8.3432\times 10^{-6}\) & \(1.1087\times 10^{-6}\) \\  & 20 & \(9.2308\times 10^{-6}\) & \(1.2627\times 10^{-6}\) \\  & 80 & \(1.0098\times 10^{-5}\) & \(1.3814\times 10^{-6}\) \\ \hline \multirow{4}{*}{All Node Neural Control (TVCC)} & 5 & 0.0539 & \(6.3989\times 10^{-4}\) \\  & 10 & 0.0460 & \(4.4217\times 10^{-4}\) \\  & 20 & 0.0597 & \(6.3153\times 10^{-4}\) \\  & 80 & 0.0611 & \(7.7512\times 10^{-4}\) \\ \hline \multirow{4}{*}{Pinning Neural Control (TICC)} & 5 & 0.0075 & \(3.6965\times 10^{-4}\) \\  & 10 & 0.0136 & \(4.3424\times 10^{-4}\) \\  & 20 & 0.0157 & \(3.3183\times 10^{-4}\) \\  & 80 & 0.0172 & \(4.4303\times 10^{-4}\) \\ \hline \multirow{4}{*}{All Node Neural Control (TVCC)} & 5 & 0.0198 & \(3.6965\times 10^{-4}\) \\  & 10 & 0.0254 & \(6.4048\times 10^{-4}\) \\ \cline{1-1}  & 20 & 0.0457 & \(2.126\times 10^{-4}\) \\ \cline{1-1}  & 80 & 0.0648 & \(4.4303\times 10^{-4}\) \\ \hline \multirow{2}{*}{Pinning Proportional Control (TICC)} & 5 & 0.1017 & \(2.6879\times 10^{-2}\) \\ \cline{1-1}  & 10 & 0.3254 & \(5.0476\times 10^{-2}\) \\ \hline \multirow{2}{*}{Pinning Proportional Control (TVCC)} & 5 & 0.3597 & \(6.3759\) \\ \cline{1-1}  & 10 & 4 & \(9.3485\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Controller comparative analysis.