esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary
### Ecdsa  
In cryptography, the Elliptic Curve Digital Signature Algorithm (ECDSA) offers a variant of the Digital Signature Algorithm (DSA), which uses elliptic curve cryptography [29]. As with elliptic-curve cryptography in general, the bit size of the public key believed to be needed for ECDSA is about twice the size of the security level, in bits. For example, at a security level of 80 bits (meaning an attacker requires a maximum of about \(2^{80}\) operations to find the private key), the size of an ECDSA public key would be 160 bits, whereas the size of a DSA public key is at least 1024 bits. On the other hand, the signature size is the same for both DSA and ECDSA: Approximately \(4t\) bits, where \(t\) is the security level measured in bits; that is, about 320 bits for a security level of 80 bits.  
The signature and verification process of ECDSA is as follows: Suppose [PERSON] wants to send a message to [PERSON]. Initially, both parties must reach a consensus on the curve parameters (CURVE, G, n). In addition to the field equation of the curve, the base point G on the curve and the multiplication order \(n\) of the base point G are also required. [PERSON] also needs a private key, \(d_{A}\) and a public key, \(Q_{A}\), where \(Q_{A}=d_{A}G\). If the message [PERSON] wants to send is \(m\), [PERSON] needs to choose a random value \(k\) between \([1,n-1]\): Calculate \(z=h(m)\), \((x_{1},y_{1})=kG\), \(r=x_{1}\text{mod}n\), \(s=k^{-1}(z+rd_{A})\text{mod}n\), and send the ECDSA signature pair \((r,s)\) together with the original message \(m\) to [PERSON]. After receiving the signature pair \((r,s)\) and the original message \(m\), [PERSON] will verify the correctness of the ECDSA signature. [PERSON] first calculates \(z^{\prime}=h(m)\), \(u_{1}=z^{\prime}s^{-1}\text{mod}n\), \(u_{2}=rs^{-1}\text{mod}n\), \((x_{1}{}^{\prime},y_{1}{}^{\prime})=u_{1}G+u_{2}Q_{A}\), \(r\overset{?}{=}x_{1}{}^{\prime}\text{mod}n\), and if it passes the verification, then [PERSON] confirms that the ECDSA signature and message \(m\) sent by [PERSON] are correct.
###### Abstract  
Multispeed transmissions are helpful for improvement of the economy and drivability of electric vehicles (EVs). In this paper, we propose a two-speed transmission based on dual planetary gear mechanism, in which shifts are realized by torque transfer between two brakes located on ring gears. To synthesize the dynamic and economic performances of the vehicle, a multiobjective optimization problem is constructed for gear ratio optimization and Pareto-optimal solutions of gear ratio combinations are obtained by Nondominated sorting genetic algorithm-II (NSGA-II). In particular, the minimum electric energy consumption of the EV is calculated with a fast Dynamic Programming (DP) in each iteration. Following this, a constant-output-torque control (COTC) scheme is adopted for the torque phase and inertia phase of gearshift process to ensure constant output torque on the wheel. To enhance transient responses, the feedforward-feedback controller structure is applied and a disturbance observer is integrated to improve robustness. Simulation results demonstrate that the two-speed transmission has much better performance in terms of acceleration time and energy economy compared to the fixed-ratio transmission, and the proposed gearshift control method is able to achieve fast and smooth gear shift robustly while maintaining constant output torque.  
electric vehicles; automatic transmission; gear ratio optimization; gear shift control +
Footnote †: journal: Applied Sciences
###### Abstract  
The effectiveness of energy management systems is a great concern for wind-photovoltaic-storage electric vehicle systems, which coordinate operation optimization and flexible scheduling with the power grid. In order to save system operation cost and reduce the energy waste caused by wind and light abandonment, a time-sharing scheduling strategy based on the state of charge (SOC) and flexible equipment is proposed, and a quantum mayfly algorithm (QMA) is innovatively designed to implement the strategy. Firstly, a scheduling strategy is produced according to the SOC of the battery and electric vehicle (EV), as well as the output power of wind-photovoltaic generation. In addition, the minimum objective function of the comprehensive operation cost is established by considering the cost of each unit's operation and electricity market sale price. Secondly, QMA is creatively developed, including its optimization rule, whose performance evaluation is further carried out by comparisons with other typical bionics algorithms. The advantages of QMA in solving the low-power multivariable functions established in this paper are verified in the optimization results. Finally, using the empirical value of the power generation and loads collected in enterprise as the initial data, the mayfly algorithm (MA) and QMA are executed in MATLAB to solve the objective function. The scheduling results show that the time-sharing scheduling strategy can reduce the system's cost by 60%, and the method decreases energy waste compared with ordinary scheduling methods, especially when using QMA to solve the function.  
microgrid; economic scheduling; clean energy; quantum mayfly algorithm (QMA)
# Erasure Codes for Cold Data in Distributed Storage Systems
## 2 Related Works
### Bit Matrix Coding  
Bit matrix coding was first introduced in the paper on Cauchy Reed-Solomon coding as a parity check array coding technique [29]. CRS encoding is performed using the Cauchy distribution matrix to encode, and decoding is performed when the encoding fails [30]. Suppose in a finite field GF (\(2^{w}\)) [31; 32], we expand the distribution matrix \((k+m)\times k\) with \(w\) in both rows and columns to obtain a matrix with \(w\times(k+m)\) rows and \(w\times k\) columns, which we call the BDM (binary distribution matrix). The bottom \(m\times(w\times w)\) matrix is called the CDM (coded distribution matrix). Matrix-based coding is essentially a dot-product operation of matrices and vectors, and different coding methods will result in different coding matrices [33]. The distribution matrix and the vector containing the data blocks are multiplied together to obtain a vector containing the data blocks and the check blocks. When encoding, the check block is obtained by the dot-product operation of the encoding matrix and the data vector. For decoding, we only need to find the rows in the distribution matrix corresponding to the data blocks that are not damaged and form a decoding matrix from these rows. By transposing this matrix and multiplying it with the vector composed of the data blocks that are not damaged, we can calculate the damaged data blocks. We expand the vector to obtain a vector containing \(wk\) elements. Multiplying the matrix and the vector yields a vector containing \(w(k+m)\) elements. This is shown in Figure 2. We can see that the bit distribution matrix is composed of many \(w\times w\) submatrices.  
In matrices and vectors, each element is a bit, and we can use the XOR operation instead of the dot-product operation [34]. This is accomplished by performing an XOR operation on the data vector corresponding to the bit that is one in the BDM. By using this approach, we can significantly increase the speed of encoding and decoding. Moreover, since the number of XOR operations performed is related to the number of ones in the BDM, the performance of encoding can be determined by the number of ones in the BDM.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 4 Healthcare Data Protection Legislation and Framework
### Major Healthcare Initiative
#### 4.1.3 Personal Information Protection and Electronic Document Act (PIPEDA)  
The federal government of Canada introduced the Personal Information Protection and Electronic Document Act (PIPEDA) in 2001. Its main purpose is to provide individuals control, to some extent, over their personal information by enforcing policies on organizations that process and disclose the personal information of Canadian citizens. These policies include informing citizens concerning personal information processed or disclosed and this information is protected by using adequate security measures. The PIPEDA applies to all personal data, healthcare data, and other data that holds the private information of individuals. If any organization collects data, then it is fully accountable for the protection of the collected data. The PIPEDA is not applicable in all states of Canada. Every province has the right to enforce rules and policies as long as they are similar to PIPEDA [71]. PIPEDA provides basic guidelines to organizations for collecting and processing personal information for business purposes. The interconnected devices should use the security standards of data privacy principles according to PIPEDA.
## 1 Introduction
Hence, an automated system is required to release the human's cognitive load. In order to understand network status and predict potential situations that might disrupt a network, an Endsley situational awareness model, proposed by [PERSON] et al. [6], was applied as a starting point for a knowledge-based cognitive system. In the Endsley situational awareness model, the role of a domain knowledge base in the cognitive process is missing. In this paper, a knowledge-based cognitive architecture is proposed that merges expert knowledge on the satellite infrastructure with the ability of machine learning algorithms to detect patterns from large amounts of data. The architecture is able to detect possible anomalies in the satellite infrastructure. The implemented architecture has (i) flexibility that enables to extend or replace existing machine learning algorithms to match the needs of the user; (ii) the ability to use knowledge of the satellite infrastructure to guide the machine learning algorithms during training and online deployment of the trained models;(iii) the capacity to adapt to the changing health conditions of a satellite network, and finally, (iv) the capacity to provide interpretable results to infrastructure engineers. The remainder of this paper is organised as follows. In Section 2, the related work is surveyed. In Section 3, a knowledge-based cognitive architecture is presented; this is followed by results of single-blind experiments carried out on an industrial case study using anonymised real satellite infrastructure data in Section 4. Further, an overview discussion is provided in Section 5, and, finally, the conclusions are given in Section 6.
## 4 Signal Acquisition  
### Centrifugal Pump Signal Acquisition  
Several sensors are coupled to the pump for measuring the three-phase motor current, the acoustic emission, the sound, the pressure, and the vibration signal. In this work, only the accelerometers are considered. Such sensors measure vibration signals under a constant rotation frequency of the motor. Four accelerometers: IMI Sensors 603C01 were coupled and labeled as \(A1\)-\(A4\) for measuring vibration signals. The accelerometer \(A1\) is located close to the suction section of the pump. The other sensors are located at the upper part of the pump, close to the coupling with the motor.  
The analog signals were transferred to a National Instruments NI-9234 acquisition card (cDAQ) with a coaxial cable. The cDAQ is connected to the NI-9188 chassis to stream the digital signals to a laptop through a 100 Mbps Ethernet Link. The LabView software controls the acquisition process on a laptop computer, and the data is stored on a hard disk for offline processing. The data acquisition runs under experimental conditions related to regulating pressure in the discharge valve. The healthy condition was the baseline, and the discharge valve was regulated with a pre-specified pressure. The experimental conditions are presented in Table 4. Each signal sample acquisition is performed at a sampling frequency of 50 kHz for 10 s. An experimental condition \(Ci\) is set and ten repetitions are run for each faulty condition \(P1\) to \(P13\). In this way, for each of the 13 fault types and considering the six pressure conditions, 60 vibration signals are recorded, and the entire dataset includes 780 vibration signals.
## 3 General Framework and Methodology  
### General Research Framework  
The overall research framework idea of this paper can be categorized into keyword expansion and the incorporation of cue template self-adaptive-attention-BERT-RCNN model training in two aspects. The specific research framework is shown in Figure 1.  
In the keyword expansion phase, keywords are first extracted from the text using TF-IWF. TF-IWF identifies words that appear frequently in documents but are not common in the overall document collection. Based on the list of extracted keywords, words with high relevance to symptoms and diseases are selected as keywords, and these keywords are used for subsequent semantic expansion. The keywords are mapped onto a high-dimensional vector space through Word2 Vec word vector modeling to capture the semantic relationships between words. The similarity calculation function of the word vector model is utilized to generate relevant synonyms and near-synonyms for each keyword.  
In the training phase of the self-adaptive-attention-BERT model incorporating cue templates, the symptom description text is first subjected to the adaptive attention mechanism to weight the keywords, which are then fed into the pre-trained BERT model to extract semantic features from the text. An RCNN layer is added on top of the output of the BERT model to extract local and global features of the text. A fully connected layer is added to the last layer of the model to map the extracted features to section categories for classification prediction.
## 4 Related Research on Service Discovery Protocols
### Directory-Less Architecture  
Due to MANETs' characteristics, a directory-less architecture is more appropriate than a directory-based architecture. There are no directory agents in a directory-less architecture. There are only user and service agents. User agents send service queries when they need to discover a service, and service agents announce service advertisements periodically in order to achieve a high service availability.  
In recent years, various protocols have been proposed. [PERSON] _et al._[34] present a probabilistic, hybrid, directory-less service discovery mechanism called ADDER. ADDER has been designed for military IPv6-based MANETs. It achieves very low service acquisition time through the exchange of a very small number of short messages. The propagation of service descriptions is based on a distance vector algorithm. [PERSON] _et al._[35] define the Pervasive Discovery Protocol (PDP), a fully-distributed protocol that merges characteristics of both pull and push solutions. Devices maintain a list of services previously announced by others. For each advertisement, an availability time is included. PDP assumes that the underlying network is either a one-hop network or a multi-hop network with multicast network support. [PERSON] _et al._[36] propose a service discovery protocol that applies a data aggregation scheme in intermediate nodes to reduce messages replies in MANET networks. [PERSON] _et al._[37] propose a Group-based Service Discovery protocol (GSD) for pervasive environments. Nodes in GSD send periodic advertisements of services. The broadcast is limited not to consume a lot of bandwidth. GSD does not describe how a route to a server node is obtained. [PERSON] _et al._[38] present a novel service discovery protocol using mobile agents that select their route dynamically and exchange service information with the nodes in order to speed up the process. The smooth random mobility model (SRMM) is used to estimate node location at a particular time. [PERSON] _et al._[39] present an adaptive directory-less service discovery system for MANETs that couples to the underlying geographic routing algorithm and sets configurable service renewal. There are other solutions where intermediate nodes answer service queries, such as [40], where [PERSON] proposes a novel service discovery approach based on a fully-distributed and parallel search model that does not require any centralized intelligence, fixed roles and a stable communication infrastructure. Any node in the network that receives service announcements from service providers stores the services in order to answer service queries. This way, service queries are not forwarded up to the service providers.
## 4 Performance Analysis for Different ERSR Radius Sets
### User Relay Selection Ratio Analysis  
The UR selection ratio is defined as the ratio of URs to the total users in MG. It represents the probability that a MG user is selected as UR.  
Firstly, we analyze the number of URs in annular area \(A_{n}\). In the second stage of COM-NOMA, due to the restriction of ERSR, benefiting from small path loss between the transceiver ends, the candidate UR can provide higher reception SINR than \(\sigma_{0}\) with large probability. The probability is approximately equal to 1, which has been verified in [10]. Therefore, it is reasonable to assume that the SU within the ERSR of USU is UR. The number of UR can be obtained by analyzing the number of SU within ERSR.  
When MG users are uniformly distributed in the cell, after the first stage of COM-NOMA, since ERSR is far smaller than the coverage area of cell, the number of SUs in the ERSR follows Poisson distribution. According to the character of Poisson distribution that its density is equal to the value of expectation, the average number of SUs who are in the ERSR of one USU can be calculated, when the distance between this USU and BS is \(\gamma_{u}\).
# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 3 Methods
### Weight-GAN Sub-Network
We define the width and height of the sample as \(2w^{c}\), and \(2h^{c}\) respectively, and \(w^{*}\) is the horizontal distance between the center point and the point of the feature map horizontally, while \(h^{*}\) is the vertical distance between the center point and the point of the feature map vertically. The closer the point of the feature map is to the center point, the higher heavier weight it has. With \(\omega_{centness}\), we can enhance the features of the center point, which has better feature representation than the edges. \[\omega_{centness}=1-\sqrt{\frac{min(w^{*},w^{c})}{max(w^{*},w^{c})}\times \frac{min(h^{*},h^{c})}{max(h^{*},h^{c})}} \tag{2}\]  
Generator network: The generator network is mainly based on deep residual learning blocks which are easier to train. In order to improve the detection accuracy, the generator network aims to generate super-resolved representations for low resolution targets. In the generator network, since the details are absent from the low resolution feature, the deep residual blocks are trained, rather than the generator network being trained directly. First, the initial features are obtained by the feature extraction module and passed to \(3\times 3\) convolutional filters, then \(1\times 1\) convolutional filters are used to increase the feature dimension to align it with the output layer. The residual blocks consist of convolutional layers, batch normalization and ReLU activation. Discriminator network: As shown in Figure 7, the discriminator network is mainly trained to distinguish the difference between the high-resolution target features and the super-resolved features which are generated based on low-resolution target features. There is one adversarial branch consisting of two fully-connected layers and an output layer with the sigmoid activation. The adversarial loss \(L_{adversarial}\) is defined as below:  
\[L_{adversarial}=-\log D_{\theta}(\omega_{centness}G_{\theta}(F_{s})) \tag{3}\]  
We denote \(D_{\theta}\) as the adversarial function with the parameter of \(\theta\) and take the generator function \(G_{\theta}(F_{s})\) as the input of the discriminator network. In order to enhance the local features and suppress the features of the edges, we introduce a parameter \(\omega_{centness}\), which calculates the proximity between the point of center and the points of the feature map from the super-resolved generator.
## 4 Evaluation
### Transmission Overhead and Average Energy Consumption vs. Fairness  
As shown in Figure 13, FLARoute experiences only about 26.5% of PRoPHET's transmission overhead, which can be as low as 9.3% with CCAF. FLARoute incurs more transmission overhead than CCAF due to being less biased towards popular nodes and allowing more number of less popular nodes participate in routing. Consequently, FLARoute also records more average energy consumption than CCAF (cf., Figure 13). Besides more number of nodes participating in routing, a reasonable part of FLARoute's energy consumption is due to energy expended in failed transmissions. The number of failed transmissions is expected to increase when more number of less popular nodes are involved in routing (cf., Figure 16). Since less popular nodes are significantly higher in population, failed transmissions--as they try to find alternative routes--also increases the average  
Figure 15: Comparing PRoPHET, CCAF and FLARoute in terms of average buffer time, average hop count and number of dropped messages.  
energy consumption. With PRoPHET, however, poor fairness leads to higher buffer occupancy and failed transmissions on popular nodes instead, as shorter encounter durations become insufficient for them to complete forwarding transactions. Hence, as far as fairness is concerned, lower average energy consumption does not always signify better performance, and vice versa. What is more important is that the energy consumption on both set of nodes (i.e., the central nodes and the other nodes) is comparable and does not exceed a tolerable rate.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Ablation Study
#### 4.8.2 Effects of the CSLB  
To assess the importance of CSLB, we conduct an effectiveness analysis of GRSB and CSAIM, as shown in Table 8. Firstly, we replace the GRSB with 3 \(\times\) 3 convolution \(\rightarrow\) ReLU and use it as the feature extraction module for CSLB. Comparing the first and third rows in the table, although the 3 \(\times\) 3 convolution improved accuracy, the model's parameter count increased by more than twofold. Considering the lightweight design, our use of the GRSB significantly reduced the model's parameters and computational load, making the slight loss in accuracy worthwhile. Secondly, we replace our CSAIM with the cross-fusion method from [46], which utilizes channel attention to compute weighting coefficients for the weighted cross-combination of features from the upper and lower branches. As seen from the second and third rows in the table, our CSAIM has fewer parameters and consistently performs better on RS-T1, BSD100, and Urban100, achieving better PSNR values on RS-T2. This indicates that CSAIM is more beneficial for enriching feature extraction and promoting information exchange between the two branches of the lattice structure.  
To further demonstrate the effectiveness of CSLB, we conduct tests on the SR results of CSLB and its two variants at a scaling factor of \(\times\)4, and Figure 9 presents the visualization results. It can be observed that the SR results of CSLB achieve superior quality in overall imaging. To facilitate a more detailed observation of the SR results, we enlarge the image patches within the red box in the HR image. Through detailed comparison, it is found that the SR results of CSLB exhibit better performance in details such as the edges of vehicles, the edges and textures of sidewalks, and the textures of shrubs, compared with the other two methods. This fully proves the rationality of the design and the superiority of the performance of CSLB.