esa-sceva/satcom-chunk-collection huggingface dataset:
# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 2 Method
### Nominal RPAS Conditions and Contingencies into Non-Segregated Airspace
#### 2.3.1 Nominal and Contingency Flight Plans  
An RPAS mission process starts by defining the target mission, the RPAS airframe, and the flight scenario. Next, it is necessary to define the services and types of payload (communication, computation, sensors, batteries, etc.) to accomplish the mission. The payload must be organised in the airframe, services assigned to computation payload modules, and an operational flight plan defined.  
As part of the ERAINT project, we developed a software architecture that supports reproducing the whole RPAS operational process. The system includes a flight plan manager service framework that processes the RPAS flight plan as a collection of mission stages (take-off, landing, en-route, arrival, taxi, and approach), with each stage as a sequence of legs (portions of flights plans) and translates them into waypoints. The sequence of waypoints involved in all the stages of the RPAS mission defines the whole nominal route that the RPAS onboard autopilot should fly.  
However, at any point in the mission execution, an unexpected event may occur. We call contingencies the unforeseen events that may place the RPAS operation at risk. To mitigate the effect of unexpected RPAS behaviour, we introduce contingency management functions into RPAS, which means providing the onboard flight management system with the ability to handle alternatives that mitigate safety risks. The nominal flight plan incorporates additional alternative routes and landing sites to support the response to potential emergencies.  
Once an RPAS is in flight, its operation should be continuously monitored to check whether its behaviour is maintained within a nominal status; otherwise, an emergency alternative may need to be executed. Contingency flight plans are partial plans that aim to provide alternative flight trajectories when an emergency occurs. Given that an emergency may occur at any point within a mission, several contingency flight plans may be necessary and each one may require initiation at different points of space, also called initial legs. The selected leg to initiate a contingency plan would be the one best suited to the current RPAS location and, depending on the type of contingency; there is a set of predefined reactions, going from partial cancellation of the mission to complete cancellation of the flight.
# Deep Pixel-Level Matching via Attention for Video Co-Segmentation
## 3 Our Framework
### The Pipeline of Our Framework  
Our framework is depicted in Figure 1, \(F_{a}\) denotes the input frame from one video, and \(F_{b}\) the input frame from another video; \(f_{a}\) is the feature map of \(F_{a}\) and \(f_{b}\) is the feature map of \(F_{b}\); \(S_{a}\) and \(S_{b}\) stand for segmentation results corresponding to \(F_{a}\) and \(F_{b}\), respectively. As is shown in Figure 1: two frames \(\{F_{a},F_{b}\}\) from different videos are concatenated in the concat layer at first; then the concatenated data are received by the ResNet [14] as the baseline in our network. We selected the ResNet as the baseline because the networks of which baselines are ResNets, such as PSPNet [18], have achieved high accuracy in image segmentation; we selected the ResNet with 101 convolutional layers (ResNet-101) as the baseline in our network as it does not require large memory space. In the ResNet, the last convolutional layer generates a concatenated binary mask. In this binary mask, the feature map in one channel contains the features of the foreground objects, and the one in another channel contains the features of the background information. Because of the limitation of memory, we only selected the feature map with the foreground objects' features to our attention module for common information extraction; then, this feature map is split by a slice layer to generate two feature maps \(\{f_{a},f_{b}\}\) before being received by our attention module. However, the feature map with the features of the background information is sent to the silence layer so that it is not utilized by our framework anymore. At last, the slice layer, which follows our attention module, splits the concatenated segmentation result from our attention module into two segmentation results \(\{S_{a},S_{b}\}\).  
Figure 1: The pipeline of our framework with a new attention module for video co-segmentation. Two video frames \(\{F_{a},F_{b}\}\) are concatenated together in a concat layer before processing by the convolutional neural network; the concatenated data are then fed into the ResNet as the baseline for feature extraction. Then, two one-channel feature maps \(\{f_{a},f_{b}\}\) from the baseline are fed into our attention module for common information extraction. Finally, two segmentation results \(\{S_{a},S_{b}\}\) belonging to the two input frames \(\{F_{a},F_{b}\}\), respectively, are outputted.
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 4 System and Attack Models
### Attack Model
#### 4.2.2 Cooperative Attacks by Multiple Malicious Entities  
* **Malicious clients and CS**: If the CS cooperates with malicious clients, it can access the local model parameters of other clients because the CS can decide which clients participate in every iteration. For example, the CS can determine the list of participants with one client and the other malicious clients and calculate the average of encrypted local model parameters based on the HE scheme to obtain encrypted global model parameters. Then, the malicious clients can access the local parameters of the honest client by offsetting the local model parameter of the malicious clients by sharing their parameters because malicious clients can decrypt the global model parameter from the CS. This threat can be eliminated by ensuring more than one honest client at each iteration. In the proposed system, the threat can be eliminated by delivering the sum of local parameters through the cooperation of two or more honest clients. In addition, the privacy threat can be kept very low by ensuring the randomness of client selection through the KGC. When one honest client participates in the learning process, the conditional probability of all remaining participants being malicious clients can be expressed as \(P_{m}^{N_{c}-1}\), where \(P_{m}\) is the ratio of the number of malicious clients to the total number of clients, and \(N_{c}\) is the number of clients participating in the local training. Thus, the probability of having access to local parameters of honest clients becomes very small, despite multiple malicious clients and CS collaborating. For example, even if half of all clients are malicious and \(N_{c}\) is 20, the probability is less than \(2\times 10^{-6}\).
* **Malicious clients and CP**: The CP cannot access the client information because random noise is added to the client information by the CS. Even if several malicious clients cooperate with the CP, the CP cannot access the local model parameters because the CS samples random noise for each client.
* **Malicious CS and CP**: When the CS and the CP cooperate to access a shared local model parameter, the client's information may be leaked. If all the distributed servers participating in the secure aggregation algorithm of SMC are compromised and cooperate with each other, there is no way to protect personal information. This paper assumes that the CS and the CP may be compromised simultaneously but do not cooperate to access client data. These assumptions are needed for building SMC-based DHC. To improve security in practice, we can increase the number of CPs participating in the secure aggregation algorithm, reducing the probability that multiple CPs and CS are malicious servers that cooperate with each other. In addition, an authentication procedure for the distributed cloud servers can be performed at the KGC to guarantee the servers participating in SMC are honest.
## 2 System Description, Dynamic Model, and Controls
### Dynamic Model  
Consider the following equation for system dynamics:  
\[\tau=(M(q)+\Delta M(q))\ \ddot{q}+(C(q,\ \ \dot{q})+\Delta C(q,\ \dot{q}))\ \dot{q}+g(q)+d, \tag{1}\]  
where \(\tau\), \(M\), \(C\), and \(g\) denotes the control torque, inertia matrix, Coriolis and centrifugal force, and gravity acting on each joint, respectively. In real systems, unknown nonlinear terms are the forms of \(\Delta M(q)\), \(\Delta C\big{(}q,\ \dot{q}\big{)}\), and disturbance \(d\)[23].
## 2 Theoretical Analysis and Proposed Hybrid IPT System
### The Proposed Hybrid Compensation IPT System  
This study's objectives are to use the control of the \(\Pi\)-style resonant network arranged in the receiver side to omit the wireless communication links and design a hybrid compensation topology that does not require additional compensation components and complex control logic. We propose a hybrid \(-\)compensated IPT system that combines the charac  
Figure 4: A \(\Pi\)-style resonant network.  
teristics of the \(\Pi\)-circuit and the DS\(-\)LCC compensated topology, in addition to realizing CC/CV charging modes regardless of load variation.  
The proposed IPT system's circuit diagram is shown in Figure 5. The full-bridge HFI converts the DC voltage source \(U_{DC}\) into a square-wave voltage source \(U_{AB}\) through MOSFETs (\(Q_{1}\)\(-\)\(Q_{4}\)). The AC generated by the electromagnetic induction is rectified by a rectifier consisting of four diodes (\(D_{1}\)\(-\)\(D_{4}\)) and converted into DC through the filter capacitor \(C_{0}\) for battery charging. As shown in Figure 1, the LCT's main components include copper wire coils, ferrite, and aluminum shielding plates. The self-inductors \(L_{p}\), \(L_{s}\) and the resonant components \(L_{1}\), \(L_{2}\), \(C_{p1}\), \(C_{p2}\), \(C_{f1}\), and \(C_{f2}\) can realize the transformation of the magnetic and electric fields on the premise of satisfying specific resonance conditions. Among them, \(C_{f2}\) can be decomposed into two parts, i.e., \(C_{fcc}\) is adopted to realize the CC mode, and \(C_{fcv}\) is adopted to realize the CV mode. Details regarding parameter settings are presented in Section 3. The transmitter's resonant network consists of \(L_{1}\), \(C_{p1}\), \(C_{f1}\), and \(L_{p}\), and that of the receiver consists of \(L_{2}\), \(C_{p2}\), \(C_{f2}\), and \(L_{s}\). \(M\) represents the mutual inductance.
## 3 Results
### Accuracy  
To quantify the accuracy of the proposed models, two metrics are usually employed. The first one is related to the core losses obtained by the area integral \(A\) of the hysteresis loops, where the percentage error can be evaluated as:  
\[e=\frac{|A_{m}-A_{c}|}{A_{m}}\cdot 100 \tag{7}\]  
\(A_{m}\) and \(A_{c}\) are the measured and calculated areas, respectively. The second one is presented to elaborate the goodness of fit of the proposed models (i.e., the Pearson correlation coefficient), which is expressed as [24]:  
\[r^{2}=1-\frac{\sum_{k=1}^{N_{m}}(B_{m,k}-B_{c,k})^{2}}{\sum_{k=1}^{N_{m}}\left( B_{m,k}-B_{m,avg}\right)^{2}} \tag{8}\]  
where \(N_{m}\) is the total number of sampled measurement points, \(B_{m,k}\) is the \(k\)-th measured point, \(B_{c,k}\) is the \(k\)-th calculated point and \(B_{m,avg}\) is the average value of all measured points. The value range of \(r^{2}\) is [0, 1], and the closer it is to 1, the more accurate the fitting is.  
Figure 1: Comparison between measured (solid) and calculated (dashed colored) hysteresis loops. (**a**) _MnZn_ ferrite. (**b**) _Fe-Si_ loop 11. (**c**) _NdFeB_ at 80 \({}^{\circ}\)C. (**d**) _NdFeB_ at 27 \({}^{\circ}\)C.  
Table 2 summarizes the accuracy values obtained by the several models for the considered materials. As can be noted, the J-A model results are the most accurate for soft materials but suffer for semi-hard and hard materials, and vice versa for the CPM. Instead, the proposed D-D-D model shows good accuracy for all materials with an error in the core losses always below 5% and a Pearson coefficient higher than 0.97.
## 4 Numerical Experiments
### Configuration of Network Parameters
#### 4.1.2 Network Model and Algorithm Configurations  
The parameters of the TT-WSN network model were as follows: \(\alpha=2\), \(\beta=1\), \(k=128\) KB, \(r_{s}=15\) m, \(r_{c}=30\) m, \(amp=100\) pJ/bit/m\({}^{2}\) and \(ie=10\) J [31].  
Three other optimization algorithms, namely, Genetic Algorithm (GA) [12], Simulated Annealing Algorithm (SA) [12], and ABC, were employed to check the effectiveness of the pdABC. The population size of all the algorithms was set to 40. The maximum number of iterations for each algorithm was 300. The calculation was repeated 30 times for each instance. The composition of ABC, GA, and SA individuals was the same as that of pdABC. The comparison algorithms all used parameters that needed to be adjusted. For GA, these parameters were the crossover rate and the mutation rate; for SA they were the initial temperature and temperature drop rate; pdABC used the constant parameter C; finally, ABC was set to half the population size multiplied by the dimension of the problem. The parameter selection ranges of the three algorithms that require parameter tuning are shown in Table 1. Each parameter combination was run 30 times with a maximum number of iterations of 100, using \(100\times 100\) terrain to deploy six relay nodes. Comparing the final average results, the final selected optimal parameter combinations are listed in the fourth column of Table 1.
## 6 Conclusions  
In this paper, a method based on the combination of machine vision and deep learning is proposed to achieve the classification of different grades of nonferrous aluminum according to surface features and color space only, therefore no longer requiring complex instruments for element detection, effectively reducing detection cost and improving detection efficiency. With the help of the machine vision algorithm, we compared the surface features of RGB, HSV, and LBP images of three aluminum blocks with grades 1060, 5052, and 6061 and created the corresponding datasets, training with Pytorch as the deep learning framework, ResNet18 as the selected model, Relu as the chosen activation function, and with gradient update performed using the Adam optimization algorithm. The main conclusions are as follows.  
1. The surface features of the aluminum blocks were extracted by machine vision, and results under different light sources and angles were compared. A blue light source and 45\({}^{\circ}\) angle lighting were selected, and the features of the acquired images in the RGB and HSV color spaces were compared. It could be seen that 1060 significantly differed from 5052 and 6061, and 5052 and 6061 could also be distinguished by setting the threshold value.
2. Datasets of RGB, HSV, and LBP images of the different grades of aluminum were created; these three datasets were selected to train the model using ResNet18, comparing the accuracy of the three datasets for aluminum classification and recognition, and ultimately RGB images were selected as the best dataset.
3. Using the RGB dataset to train the ResNet18 model with a learning rate of 0.00001 and a batch size of 8, the classification and recognition rate for different grades of aluminum could reach 100%; comparing the training effects of different network models, ResNet18 had significant advantages in model stability and accuracy, compared with the traditional use of spectra for classification and recognition of different grades of similar materials. This paper achieved faster, higher-volume, and lower-cost classification and recognition of different grades of aluminum.  
Conceptualization, [PERSON] ([PERSON]) and J.L.; methodology, [PERSON] ([PERSON]), Q.Z. and J.L.; software, Q.Z. and J.L.; validation, [PERSON] ([PERSON]) and X.L.; formal analysis, X.L.; investigation, [PERSON] ([PERSON]); resources, B.H.; data curation, B.H. and J.L.; writing--original draft preparation, J.L.; writing--review and editing, J.L.; visualization, J.L.; supervision, [PERSON] ([PERSON]); project administration, B.H. and J.L.; funding acquisition, B.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Foundation of Artificial Intelligence Key Laboratory of Sichuan Province, grant number 2020 RY01, and the Science and Technology Department of Sichuan Province, grant number 2021 YFG0050.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
All the authors are greatly acknowledged for their financial support in making this research possible.  
The authors declare no conflict of interest.
# PAGE--Practical AES-GCM Encryption for Low-End Microcontrollers
## 1 Introduction
### Extended Version of ICISC'19  
In this paper, we extended our previous work published in ICISC'19 [11]. In ICISC'19, only AES-CTR implementations for 128-bit, 192-bit, and 256-bit security levels on low-end microcontrollers were investigated (i.e., FACE-LIGHT). This work further optimized the well-known mode of operation for authenticated encryption (i.e., GCM) on low-end microcontrollers. In particular, the binary field multiplication is optimized with Karatsuba algorithm and compact register assignments. The proposed implementation (i.e., PAGE) is also secure against Simple Power Analysis (SPA), Timing Attack (TA), and Correlation Power Analysis (CPA).
# Landslide Prediction with Model Switching +
## 6 Conclusions  
To address the problems of imbalanced data, low true positive rate for learning, determining the prediction horizon, and the time for model re-training, MoSLaPS has been proposed as a novel method for landslide prediction. MoSLaPS employs the ADASYN method to balance the stable class (no landslide) with the unstable class (landslide), where the classification is based on the factor of safety calculated using the SHALSTAB model. To solve the problem of low true positive rate, a BPNN-based event-class predictor was proposed and two BPNN predictors were designed to learn the stable class pattern and the unstable class pattern. A novel prediction horizon tuning method was proposed, along with a learning-based model re-training technique. All of these optimizations contribute towards the goal of the proposed MoSLaPS; that is, accurate early warning of landslides.  
Compared with BPNN and Adaboost, though our event-class predictor has a higher FPR, it also has a much higher TPR of 98.40%. This means our classifier has a higher ratio of correct classification. According to the predicted class, our system can switch between different predictors to adapt to the environmental state. In addition, BPNN is employed to construct the error model to predict the future errors of our proposed MoSLaPS and compensate for these errors in the prediction phase. As a result, MoSLaPS has much lower MAPE and RMSE than BPNN and ADASYN+BPNN, which means that MoSLaPS is more accurate. In addition, the NRMSE of our method is larger than the NRMSE of the other methods, which means that our method is closer to the actual conditions. Statistically, our landslide prediction system could send warnings an average of 44.2 min prior to the actual occurrence of a landslide.  
In the future, we will further consider other advanced imbalanced learning algorithms to improve the performance of BPNN-based event-class predictors. For different applications, a larger number of categories (cases) can be considered for model switching. The maximum range of errors allowed in the re-training phase can also be limited, so that prediction models are more stable. As a result, the model switching strategy will be more accurate. Moreover, well-known time-series deep learning technologies, such as Recurrent Neural Networks (RNNs) or Long-Short Term Memory (LSTM) blocks, will be used to combine all three BPNN models into one.  
Conceptualization, P.-A.H.; methodology, D.U.; software, S.-F.C.; validation, S.-F.C. and D.U.; resources, P.-A.H.; data curation, S.-F.C.; writing--original draft preparation, S.-F.C.; writing--review and editing, D.U.; supervision, P.-A.H.; project administration, P.-A.H.; funding acquisition, P.-A.H.  
Figure 14: Landslide early warning time point.  
**Funding:** This research was funded by Ministry of Science and Technology, Taiwan, grant number MOST 140-2221-E-194-064.  
**Acknowledgments:** This research was supported partially by the project grant MOST-107-2221-E-194-001-MY3 from the Ministry of Science and Technology, Taiwan.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Scheme to Design Community Detection Algorithms in Various Networks
## 5 Demonstration
### Comparison Results  
We compare our algorithm with some popular ones that can generate communities with overlaps. We test them over LFR benchmark networks and calculate the normalized mutual information (NMI). In addition, we also provide some node-level performance measurement results: precision, recall and F-Score, which are defined as follows [39].  
Let \(C_{s}\) denote the set of communities generated by the LFR benchmark tests and \(C_{r}\) the one generated by the testing algorithm. Based on \(C_{s}\) and \(C_{r}\), we first define two sets of node pairs:  
\[O_{s} =\{(u,v)|\text{there exists a community in $C_{s}$ that contains $(u,v)$}\},\text{ and}\] \[O_{r} =\{(u,v)|\text{there exists a community in $C_{r}$ that contains $(u,v)$}\}.\]  
Then, we have  
\[precision =\frac{|O_{s}\cap O_{r}|}{|O_{s}|}\] and \[recall =\frac{|O_{s}\cap O_{r}|}{|O_{r}|}.\]  
In addition, F-Score, an indicator for the quality of the tested algorithm's solution, is  
\[F\text{-}Score=2\times\frac{precision\times recall}{precision+ recall}. \tag{4}\]  
Note that NMI, precision, recall and F-Score all have the range between zero and one. Their values have a positive relationship with the quality of the community detection results. That is, closer-to-one scores imply a better detection solution.  
Figure 5: [PERSON]’s karate club and its maximal communities by choosing \(CP=1.5\).  
Figure 6 plots the test results of our algorithm as well as the Community Overlap Propagation Algorithm (COPRA) [40], Connected Iterative Scan (CIS) [41], CFinder [42], Link [43] and Greedy Clique Expansion (GCE) [44]. In our experiments, we apply a similar configuration used by [PERSON] et al. [45]. In particular, the LFR benchmark networks contain 100 nodes for each instance. The node degree, governed by the power laws with exponent two, has an average of six and maximum 50. The community size, ranging from 20 to 100, also follows a power law but with exponent one. For a node, the expected fraction of links that connect to other communities is set to 0.3. We let ten percent of the nodes be shared by \(\mathcal{N}_{c}\) various communities, where \(\mathcal{N}_{c}=2,3,\cdots 8\). Since, in our algorithm, a pre-specified CP is required, we simply use the CP that gives the best performance.  
From Figure 6, we can observe that our algorithm has a competitive NMI. The F-score of our algorithm, like others, has a negative relationship with \(\mathcal{N}_{c}\). However, it still has a better performance. Remarkably, when \(\mathcal{N}_{c}\) equals two, the average F-score of our algorithm is around 0.9, which implies that the algorithm has found almost all local community structures. As \(\mathcal{N}_{c}\) increases, compared with other methods, our method maintains a higher score until \(\mathcal{N}_{c}=8\).