esa-sceva/satcom-chunk-collection huggingface dataset:
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 3 Our Proposed Method--FDCNet
### The Loss Function  
Mainstream works set pixel-wise Euclidean loss as their loss function in the training procedure. In the crowd scenes, the local patterns and texture features of the high-density regions vastly differ from other regions (low-density regions or background). However, the Euclidean loss is established on the pixel independence hypothesis and ignores them. The local correlation of density maps is not taken into consideration.  
To remedy the aforementioned problems, we combined the loss function based on Structural Similarity Index (SSIM) with the Euclidean loss as our final loss function. The SSIM can be used to estimate the local consistency, optimizing the final loss function to measure the difference between the estimation and the ground truth. Owing to the training accuracy, the generation of high-quality density maps was improved.
# Priority of Emergency Vehicle Dynamic Right-Of-Way Control Method in Networked Environment
## 2 PDR-EVs Control Method
### Avoidance of EVs Guidance Control Method
#### 2.3.2 Guided Control Methods
Case 3: In the interval of \(DMS(t_{s,j})\), \(\forall k\in K,\forall j\in J,\forall t_{s,j}\in\Psi\)\(H_{i+k}(t_{s,j})<Gap_{min}(t_{s,j},k,j)\)\(\left|v_{j}(t_{s,j})=v_{max}\right.\) There is no \(H_{i+k}(t)\) in \(DMS(t_{s,j})\) that satisfies the minimum safe entry distance. The \(H_{i+k}(t)\) with the shortest distance is selected as the target merging gap, and the CV's travel speed in the inner lane is adjusted to provide a gap to merge safely. The optimal target gap is determined by the function of \(J(t,k,j)\), and the target gap is denoted by \(H_{i+k}^{*}(t)\). After the vehicle infrastructure cooperation system sends the guidance signal, the CVs behind the \((i+k)\)-th vehicle decelerate cooperatively (including \(j\)), the speed change rate does not exceed \(a_{max}\), and the speed of the CVs in their front remains unchanged,as depicted in the avoidance process of Figure 3. At time \(t_{c,j}\), the target gap \(H_{i+k}^{*}(t)\) must satisfy:  
\[H_{i+k}^{*}(t_{c,j})=\int_{t_{s,j}}^{t_{s,j}+\tau+\theta_{j}}\{v_{i+k+1}(t)-v_{ i+k}(t)\}dt+H_{i+k}^{*}(t_{s,j})\geq Gap_{min}(t_{s,j},k,j)\big{|}(v_{j}(t_{s,j}) =v_{max}) \tag{20}\]  
In the same way, \(t_{c,j}\), \(a_{j}\) can be solved. Considering \(a_{j}^{*}=max|a_{j}|,\forall j,a_{j}\in\mathbf{A}\), we denote the solution of \(t_{c,j}\) as \(t_{c,j}^{*}\). Then the CVs' coordinated decelerations behind the vehicle are:  
\[a_{i+k}^{*}=\frac{v_{i+k}(t_{s,j})-max\Big{\{}v_{i+k}(t_{c,j}^{*}),v_{min}\Big{\}} }{t_{c,j}^{*}-\tau-t_{s,j}} \tag{21}\]  
At this point, the system sends \(t_{c,j}^{*},a_{j}^{*}\), and \(a_{i+k}^{*}\) to the CVs involved in the interaction.
## 6 Simulation and Case Study
### Simulation of Multiple Temporal Stages of Frequency Control
#### 6.1.3 Simulation of TFR  
The uncertainty in the power system includes the load forecast error, the renewable generation forecast error, and the outage of SGs. As a result, in each schedule interval, for a specific load reference setting, there is a tiny gap between the generation and the demand. Then the power imbalance is compensated through the process of IR, PFR, and SFR. When the load increase is 3% higher than the change of load reference, the dynamic responses of frequency control are shown in Figure 9.  
Figure 8: Simulation of SFR for a step change of load.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### Machine Learning Algorithms
#### 2.1.5 Clustering  
Clustering is an unsupervised method to classify elements into discrete groups based on their similarities or discovered patterns [46]. Contrary to the classification methods, in clustering there are no known labels to train a model.  
In [26], the authors suggest a Fuzzy C-Means clustering method in order to make short-term traffic predictions. While in a regular clustering method, an instance has to belong to one of the examined classes, in Fuzzy C-Means, every instance belongs to a given class to a certain degree. This degree is described by the so called membership function. In the experiment described in this study, the authors use a traffic simulator with real traffic data from the road network in Japan. They compare the results of the Fuzzy C-Means method to the \(k\)-Nearest Neighbour (\(k\)-NN) method, which will be discussed in the instance based algorithms section. The suggested method shows about 26% smaller error rate compared to the \(k\)-NN. The authors attribute this superiority to the fact that the fuzzy method takes into consideration the inflow and outflow of cars to the selected area rather than using only the data from the area itself.  
One of the most popular clustering algorithms is K-Means. The K-Means algorithm's goal is to separate data into a k number of clusters, such that the given data and their cluster's mean value have a minimum squared error [47].  
In [12], the purpose is to optimize the traffic network configuration by estimating the best number and location of the processing centers for a sum of suppliers. The authors use K-Means to calculate the distance of the suppliers and the centers, so as to minimize the cost. Prior to the K-Means clustering, the real-time data obtain from GIS devices, are pre-processed with a Deep Belief Network algorithm. The DBN actually finds the initial k spots for the k-Means. To find the location of road anomalies such as bumps, speed breakers, and potholes, the suggested method in [33] uses mobile devices to collect data from their accelerometers. The data are then sent to nearest fog node for processing. K-Means is used to cluster the data and find the threshold values that distinguish a road anomaly from a normal road. A similar approach for road anomalies detection is suggested in [34]. The authors use mobile phones to get raw data of accelerometers and GPS. The data are pre-processed, root mean square is applied on the accelerometer data, threshold values are defined for the z-axis and k-Means is used to cluster the data used to train the system.
## 4 Model Validations and Simulations
### Data and Parameter Estimations
Varied curves are functions of \(N=\phi(X)\) by letting \(e=2.42\), \(d_{g}=0.00028\), \(d_{w}=0.0025\), \(\beta_{g}=33\), \(\delta_{g}=0.99\), \(\delta_{n}=0.261\), and \(\beta_{n}=35.2\) (short-dashed curve shows a small-size colony as colony approaches to \(N^{*}=37.94\), \(X^{*}=0.0732\) under those values), \(\beta_{n}=74.6\) (solid curve shows a median colony size as colony approaches to \(N^{*}=60\), \(X^{*}=0.1114\) under those values), \(\beta_{n}=145.2\) (long-dashed curve shows a large-size colony as colony approaches to \(N^{*}=90.67\), \(X^{*}=0.16\) under those values), respectively. number of gamergates \(N_{g}^{*}=5\), and the reproductive ratio \(X^{*}=0.098\). In Figure 4b, Colony SAFC12 best fits are \(\beta_{n}=121.15,\delta_{g}=1.59\), and \(\delta_{n}=0.67\) which reaches a size of \(N^{*}=97\), number of gamergates \(N_{g}^{*}=15\), and reproductive ratio \(X^{*}=0.155\). Our estimations seem to be in agreement with values of parameters presented in literature, and the predictions from our model are reasonable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Colony**} & \multirow{2}{*}{**Day**} & \multirow{2}{*}{**Colony Size (N)**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Reproductive**} \\  & & & & **(N\({}_{g}\))** \\ \hline \multirow{3}{*}{F44} & 0 & 42 & 3 & 0.0714 \\  & 94 & 41 & 3 & 0.0732 \\  & 613 & 62 & 7 & 0.1129 \\ \hline \multirow{3}{*}{SAFC12} & 0 & 127 & 4 & 0.0315 \\  & 94 & 109 & 4 & 0.0360 \\  & 125 & 91 & 4 & 0.0440 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time series data for colonies F44 and SAFC12.
# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 3 Hybrid Multi-Agent Orchestration Algorithm  
In the multi-agent-driven Intelligent Mine Gas State Decision-Making System, the task orchestration algorithm is a core component that directly impacts the system's efficiency and accuracy. The mine environment is a typical complex dynamic system characterized by complexity, dynamism, and variability in local conditions. Although traditional centralized management systems excel in resource allocation, they often lack adaptability in complex environments, leading to slow responses. On the other hand, while distributed systems possess the ability to respond quickly, they face significant challenges in overall coordination, which can result in resource wastage. Therefore, this research adopts a hybrid multi-agent orchestration algorithm, aiming to combine the advantages of both centralized and distributed algorithms to achieve efficient and flexible task allocation and execution.  
The algorithm adopts a hierarchical architecture, dividing tasks into global and local levels, as illustrated in Figure 1. At the global level, a centralized algorithm is used for overall task planning, resource allocation, and coordination control, ensuring the smooth achievement of system objectives and optimized resource allocation. At the local level, a distributed algorithm is employed, allowing each agent to make autonomous decisions and execute tasks based on its perceived information and local objectives while exchanging information and collaborating with other agents to achieve local optimization and rapid response.
## 6 Experimental Validation Studies
### Validation of the Rediscovered SICN Process Models  
First of all, it is necessary to validate the rediscovered process models of structured information control nets (SICN), which are mined from the experimental datasets, respectively. The process mining subsystem of the integrated functional architecture and its implemented system is applied to the Experimental Dataset 1 and the Experimental Dataset 2 and successfully mined the rediscovered SICN process models.  
As you can see in Figure 10, the experimental result of the process mining subsystem is the rediscovered SICN process model containing 113 activities with all the primitive process patterns and all the activities' execution occurrences. That is, the left hand side of the figure is a captured screen representing the SICN process model rediscovered from the Experimental Dataset 1 of 10,000 all-nonoise 50 MB.xes that contains 10,000 process instance event traces and 678,864 activity event logs under the control of enacting 113 business activities of the large bank transaction process model. The enlarged process block indicates all the primitive process patterns by the labeled boxes, in particular. Moreover, the process mining subsystem supports the printout form of the rediscovered SICN process model, as shown on the right hand side of the figure. Secondly, another experiment of the process mining subsystem was carried out on the Experimental Dataset 2, and it was able to mine the rediscovered SICN process model of the loan application example (Configuration 1), as shown in Figure 11. The captured-screen and its printout form in the figure show the rediscovered SICN process model containing nine activities with their execution occurrences. The Experimental Dataset 2 is built under the control of nine business activities of the loan application example process model with enacting 100 process instance event traces and storing 590 event logs (the average number of events per trace is 5.9 event logs).  
Through these SICN process model rediscovery experiments, the process mining functionality of the integrated functional architecture and its implemented system ought to be functionally validated as you have seen. In other words, the system is able to rediscover all the structural control-flow primitive patterns, such as the conjunctive process patterns of AND-split and AND-join, the disjunctive process patterns of OR-split and OR-join, and the iterative process patterns of LOOP-split and LOOP-join, from both of the Experimental Datasets 1 and 2. Additionally, it is able to mine the number of execution occurrences on each of the 113 business activities from the Experimental Dataset 1, and the number of execution occurrences on each of the seven business activities from the Experimental Dataset 2, as well.
## 3 Dataset and Features
### Feature Selection for Real Data  
We implemented a rigorous feature selection process to identify the most relevant predictors of information diffusion. First, we examined multicollinearity among the features using correlation analysis.  
We identified 55 pairs of highly correlated features (r \(>\) 0.8). For example, density showed a perfect correlation with avg_degree_centrality (r = 1.000) and strong correlation  
Figure 4: Heatmap of coverage by graph type and delay type.  
Figure 3: Coverage by spread probability in real data. Box plots show standard statistical summaries with outliers marked as circles.  
with avg_eigenvector_centrality (r = 0.993). Similarly, max_degree had a strong correlation with std_degree (r = 0.996).  
The top features by correlation with average coverage were as follows (Figure 5):  
\(\bullet\) density (r = 0.682);  
\(\bullet\) avg_degree_centrality (r = 0.682);  
\(\bullet\) avg_eigenvector_centrality (r = 0.668);  
\(\bullet\) edges (r = \(-\)0.605, negative correlation);  
\(\bullet\) nodes (r = \(-\)0.598, negative correlation).  
Interestingly, the number of edges (r = \(-\)0.605) and nodes (r = \(-\)0.598) exhibited moderate negative correlations with coverage. This counterintuitive finding suggests that larger networks may actually hinder information spread rather than facilitate it. This could be attributed to several factors: (1) information dilution across larger networks requires more time to achieve similar proportional coverage, (2) increased path lengths create more opportunities for transmission failures, and (3) the emergence of structural bottlenecks that restrict information flow across certain network regions. This highlights the importance of targeted seeding strategies in larger networks rather than relying solely on organic spread.  
To reduce multicollinearity, we removed highly correlated features while retaining those most associated with the target variable. We also performed mutual information analysis to capture non-linear relationships.  
Figure 5: Feature correlation matrix.  
Our combined feature selection approach identified 14 key features for modeling (Figure 6):  
* min_degree;
* num_components;
* diameter;
* avg_community_size;
* density;
* p_spread;
* avg_path_length;
* avg_degree;
* max_degree;
* transitivity;
* degree_assortativity;
* target;
* avg_clustering;
* delay_type_exponential.  
This feature set balanced predictive power with model interpretability. The selection process helped avoid overfitting despite the limited sample size of the real data.
## 2 Integrated Modeling
### Disturbance Model  
RWA is the primary disturbance source in most spacecrafts and the disturbance model of the RWA has been studied for several decades. There are currently three different types of disruption disturbance models. They are the empirical model [34], the analytic model [8; 9], and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Mass & 65 kg \\ Orbit & 535 km@gSO \\ Image resolution & \(\leq\)1.1 m \\ Pointing accuracy & \(\leq\)0.05\({}^{\circ}\) (3\(\sigma\)) \\ Attitude stability & \(\leq\)0.0005\({}^{\circ}\)/s (3\(\sigma\)) \\ Swath width & \(>\)18.5 km \\ Focal length & 1600 mm \(\pm\) 30 mm \\ Image element size & 3.2 \(\mu\)m \\ Static MTF & \(\geq\)0.13 (Nyquist frequency) \\ Dynamic MTF & \(\geq\)0.10 (Nyquist frequency) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the satellite.  
Figure 3: Block diagram of the integrated model.  
Figure 2: 3D model of the satellite.  
the semi-analytic model [35]. This paper does not place a strong focus on the disturbance model, and in order to derive the integrated model, we use the empirical model.  
The disturbance of the RWA is modeled as a series of discrete harmonics with amplitudes proportional to the square of the wheel speed and frequencies that change linearly with wheel speed [9]:  
\[F_{j}(t)=\sum_{i=1}^{n}C_{i}\Omega^{2}\sin(2\pi h_{i}\Omega t+\alpha_{i})\ (j=1,\ 2, \cdots,6), \tag{2}\]  
where, \(F_{j}(t)\) is the \(j\)th component of \(\mathbf{F}\), \(\mathbf{F}=[F_{\rm x},F_{\rm y},F_{\rm z},M_{\rm x},M_{\rm y},M_{\rm z}]^{\rm T}\). \(n\) is the number of harmonics included in the model, \(C_{i}\) is the amplitude coefficient of the \(i\)th harmonic, \(\Omega\) is the wheel speed, \(h_{i}=\omega_{i}/\Omega\) is the \(i\)th harmonic number, equal to the ratio of the frequency \(\omega_{i}\) to the wheel speed \(\Omega\), and \(\alpha_{i}\) is a random phase (assumed to be uniform over \((0,2\pi)\)).  
The disturbance force and tongue of RWA in the hard-mounted boundary condition can be obtained using a Kistler table, as shown in Figure 4, while the results are shown in Figure 5. It can be seen that the main harmonic numbers of \(F_{\rm x}\), \(F_{\rm y}\), \(M_{\rm x}\), and \(M_{\rm y}\) are 1, 14.19, and 15.43, respectively, while the main harmonic numbers of \(F_{\rm z}\) are 14.86 and 18.62 and \(M_{\rm z}\) are 15.96 and 47.86.
# Few-Shot Image Classification via Mutual Distillation
###### Abstract
With an escalation in network complexity, there is a corresponding expansion of the search space for network parameters, posing a heightened risk of overfitting. Particularly for models that learn both a feature embedding and a non-linear metric, such as relational networks, this matter will gain greater prominence. One of the effective ways to solve the above issue is the regularization technique. This paper uses knowledge distillation [17] to achieve regularization for metric-based meta-learning methods. The applied regularization technique encompasses response-based knowledge distillation, wherein the neural response from the final output layer of the teacher model is leveraged. This approach has become prevalent in the literature, showcasing its effectiveness in refining model predictions. Recent literature recognizes soft targets--the widely adopted response-based knowledge for image classification--as an effective regularizer [18]. Typically, a vanilla knowledge distillation framework comprises one or more extensive pre-trained teacher model alongside a compact student model [19]. Due to the lack of a pre-trained metric-based model instructor, the widely used framework is not suitable for the metric-based model. Consequently, we suggest the implementation of Mutual Distillation of Metric-based Meta-learning (MDMM) to enhance their performance. More precisely, it is composed of two separate metric-based networks that supply each other with a regularization term. The objective of the regularization term is to minimize the Kullback-Leibler divergence between the predictions of the two networks. As illustrated in Figure 1, owing to the decision boundaries of the networks being near the optimal boundary, the information exchange between these two networks will make each network in mutual learning refine its decision boundary to the optimal one. To sum up, our contributions are:We use response-based knowledge distillation to regularize metric-based meta-learning methods. As far as we know, we are the pioneering contributors to the integration of mutual distillation within the metric-based meta-learning framework for few-shot learning;
2. We implement mutual distillation between two distinct and well-established models with varying parameters, such as prototypical networks and relational networks. This mutual learning approach, implemented through distillation, effectively enhances the model's generalization under few-shot conditions;
3. Our approach achieves a state-of-the-art performance across two benchmarks in few-shot learning research, underscoring that each metric-based model can obtain an excellent performance when supported by mutual distillation.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 5 Single Scanner-Based DoA Estimation
### Measurement of RSS Profile over Heading Direction
Specifically, for a set of \(N_{s}\) data samples, we can represent the dataset as follows:  
\[x_{i}=\{t_{i},\,P_{r}(t_{i}),\,\theta(t_{i})\} \tag{1}\]  
for \(0\leq i<N_{s}\), where the parameters \(t_{i}\), \(P_{r}(t_{i})\), and \(\theta(t_{i})\) are the timestamp, RSS measurement data, and heading measurement data, respectively. In the next section, we will present a space-time data processing and fusion method that is used to derive reliable DOA estimation from the measurement dataset defined in Equation (1).
## 2 Preliminaries and Problem Statement
### Recovery Scheduling Layer  
The scheduling layer consists of three stages and the role of each stage is indicated as follows:  
1. In stage 1, the recovery time windows of the UAV swarm are calculated. Since the flight path of the mother aircraft is predetermined, the position of the mother aircraft at any time is fixed. The basic idea of this stage is that when a UAV following a feasible path is capable of arriving at a position earlier than the mother aircraft, the arrival time of the mother aircraft is contained in the recovery time window of the UAV. The UAV can reach the position at this arrival time following a new path with a longer length, in order to rendezvous with the mother aircraft and facilitate a successful recovery.
2. In stage 2, the recovery sequence is optimized using a GA. Even with relatively few UAVs, finding the optimal recovery sequence is quite complicated. The GA is an efficient heuristic searching algorithm, and it is suitable enough to solve this scheduling problem. The execution process of the GA is given in Section 4.
3. In stage 3, the recovery schedule for the UAVs is generated based on the optimal recovery sequence, based on a similar iteration to Equation (7). Since the mother aircraft trajectory is predefined, the recovery positions on the mother aircraft trajectory can be obtained based on the recovery schedule. Further, the recovery path lengths for the UAVs are obtained since the velocity of the UAVs is known in advance. These results, at this stage, are directly passed to the path planning layer as the input.  
Figure 2: Recovery planning framework.