esa-sceva/satcom-chunk-collection huggingface dataset:
# Dimensionless Energy Conversion Characteristics of an Air-Powered Hydraulic Vehicle
## 4 Dimensionless Modelling of HP Transformer
### Dimensionless Energy Equations of Pneumatic System  
The dimensionless energy equations for the discharge side and the charge side can be written as follows:  
\[m^{*}\frac{dT^{*}}{dt^{*}}=\frac{S^{*}}{S_{p}^{*}t_{d}^{*}}(1-T^{*})+(k-1)(p^{* }u^{*}-q^{*}T^{*}) \tag{17}\]  
\[m^{*}\frac{dT^{*}}{dt^{*}}=\left(\frac{S^{*}}{S_{p}^{*}t_{c}^{*}}+q^{*}\right) (1-T^{*})+(k-1)(q^{*}-p^{*}u^{*}) \tag{18}\]  
where, the parameter \(T_{d}^{*}\), which is the dimensionless temperature settling time of the discharge side, is the ratio of the temperature settling time constant, \(T_{hd}\), and the isothermal pressure time constant, \(T_{p}\). The dimensionless and dimensional time constant can be written as follows:  
\[t_{d}^{*}=\frac{t_{d}}{t_{\rm min}} \tag{19}\]  
\[t_{d}=\frac{C_{p}m_{\rm{pmax}}}{S_{p}t_{d}} \tag{20}\]  
\[S_{p}=2A_{p}+2L\sqrt{\pi A_{p}} \tag{21}\]  
The dimensionless maximum heat transfer area can be given as:  
\[S_{p}^{*}=\frac{2A_{p}+2L\sqrt{\pi A_{p}}}{A_{p}}=2+2L\sqrt{\frac{\pi}{A_{p}}} \tag{22}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & \multicolumn{2}{c}{**Reference Value**} & \multicolumn{1}{c}{**Dimensionless Variable**} \\ \hline Affective area & \(A_{p}\) & Area of e piston of the pneumatic chamber & \(A_{e}{}^{*}=\frac{A_{e}}{A_{p}}\) \\ Time & \(t_{min}\) & Time to totally exhaust \(W_{p}\) of air & \(t^{*}=\frac{t}{t_{\rm min}}\) \\ Velocity & \(\overline{u}=\frac{L}{t_{\rm{max}}}\) & Average velocity & \(u^{*}=\frac{u}{\overline{u}}\) \\ Pressure & \(P_{s}\) & Supply pressure & \(P^{*}=\frac{p}{P^{*}}\) \\ Temperature & \(T_{a}\) & Atmosphere temperature & \(T^{*}=\frac{T}{T_{s}}\) \\ Air mass flow & \(q_{\rm{max}}\) & Maximum air mass flow & \(q^{*}=\frac{q}{t_{\rm{max}}}\) \\ Volume flow & \(Q_{\rm{max}}=\frac{V_{s}}{t_{\rm{min}}}=A_{h}\overline{u}\) & Maximum oil volume flow & \(Q^{*}=\frac{Q}{Q_{\rm{max}}}\) \\ Air mass & \(m_{\rm{fpmax}}=roup*Vp\) & Maximum air mass & \(m^{*}=\frac{\overline{u}}{m_{\rm{fpmax}}}\) \\ Displacement & \(L\) & Piston stroke & \(x^{*}=\frac{L}{L}\) \\ Volume & \(V_{\rm{fpmax}}=L\times A_{p}\) & Maximum volume of pneumatic chamber & \(V^{*}=\frac{V}{V_{\rm{fpmax}}}\) \\ Power of compressed air & \(P_{\rm{fpmax}}=\frac{P_{\rm{fpmax}}}{\rho_{s}}\ln\frac{P_{s}}{\rho_{s}}\) & Maximum power of compressed air & \(P^{*}_{p}=\frac{P_{s}}{P^{*}_{\rm{fpmax}}}\) \\ Power of hydraulic oil & \(P_{\rm{fmax}}=p_{s}Q_{\rm{max}}\) & Maximum power of hydraulic oil when its pressure equals the supply pressure (\(p_{s}\)) & \(P^{*}_{h}=\frac{P_{s}}{P^{*}_{\rm{fpmax}}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference values and dimensionless variables.  
For the charge side:  
\[t_{c}^{*}=\frac{t_{c}}{t_{\text{min}}} \tag{23}\]  
\[t_{c}=\frac{C_{\text{v}}m_{\text{pmax}}}{S_{p}h_{c}} \tag{24}\]
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 4 Experimental Results and Comparative Analyses  
In this section, performances of the Novel Hybrid-Copy Algorithm and analyses are presented. The experimental platform consists of three physical hosts, each with four eight-core 2.6 GHz Intel Xeon E5-2550 CPU, 32 GB DDR3 RAM, and Intel 82,576 gigabit network connections. All VMs used in our experiment are configured to use two VCPUs and 1 GB RAM. We use the latest stable version of Xen (version of xen is 4.8.0) as the VM monitor. The guest kernel is Linux 4.9.13 (stable version), and the host kernel is a modified version of Linux 4.9.13 for both the source and the destination. The storage is accessed via iSCSI protocol from the third physical host configured as the NAS. The workloads used in the experiments are as follow:  
_SPEC VIRT_SC 2013_: A benchmark addressing performance evaluation of datacenter servers used in virtualized server consolidation. SPEC VIRT_SC 2013 (v1.1) measures the end-to-end performance of all system components including hardware, the virtualization platform, and the virtualized guest operating system and application software. The benchmark supports hardware virtualization, operating system virtualization, and hardware partitioning schemes. It is a write-intensive workload.  
_Linux Kernel Compile_: A Linux 4.9.13 kernel is compiled in the migrated VM in our second experiment. It is an intensive workload.
# Structural Optimization Design of Spaceborne Microwave Probe Antenna
## 4 Size Optimization of the Planar Antenna Reinforcement Structure
### Optimization of the Size of the Planar Antenna Reinforcement Structure
As shown in Figure 11 below, after six iterations, the rotational inertia is minimized to \(1.18\times 10^{-3}\) Kg:m\({}^{2}\). Figure 10: Results of the sensitivity analysis of design variables to frequency. Figure 9: Results of the sensitivity analysis of design variables to the moment of inertia. Based on the iteration results, the reinforcement structure shown in Figure 12 below is established. Since the goal is to reduce the rotational inertia, the thickness and height of the reinforcement near the center are higher than the reinforcement away from the center. Unlike topology optimization, size optimization effectively reduces rotational inertia by precisely adjusting the dimensions of the reinforcement ribs, especially those near the rotational axis. The mass of the reinforcing bar after size optimization is 155.9 g, which is 26% less than the original structure; the rotational inertia is 39% less than the original structure. The disturbance moment generated during variable-speed motion is 0.1144 Nm, which is 39% less than the original structure, while the disturbance moment generated during uniform rotation of the antenna is 0.0290 Nm, which is 52% less than the original structure due to the reduction of the amount of dynamic inequality. The rotational inertia of the new structure is lower than that of the original structure, which is helpful for reducing the disturbance moment of the structure and helps to improve the motion control precision and stability of the system, which is suitable for scenarios that require high-precision rotation. It is conducive to the realization of high-stability attitude control of the satellite.
# Estimation of Association between Healthcare System Efficiency and Policy Factors for Public Health
## 2 Materials and Methods  
### DEA Bootstrap Approach  
Data envelopment analysis (DEA), which is a non-parametric approach, analyzes the efficiency of groups (commonly referred to as decision-making units (DMUs)) with an idea that efficient DMU produces more output than others with the same amount of input [18]. DEA has an advantage in testing efficiency among other approaches such as stochastic frontier analysis (SFA) as it does not require any assumptions about the production frontier and is able to deal with complex systems that have multiple inputs and outputs such as a healthcare system [19]. Specifically, we used the variable return to scale (VRS) model proposed by [PERSON] et al. [18] since the output (e.g., life expectancy) may not be proportionate with a change of input (e.g., health expenditure) due to the nature of the healthcare system.  
DEA has been observed to be easily affected by the presence of outliers, especially with a small sample size [20]. Therefore, we introduced the bootstrapping approach [21] to improve the reliability of the efficiency estimates. For this approach, we repeated the re-sampling process and obtained efficiency estimates for many instances (e.g., 2000 times), then the result was approximated to the sampling distribution of the original estimates. That is, it was able to obtain bias-corrected efficiency estimates and confidence intervals by using the bootstrapping approach.  
To get the bias-corrected efficiency scores, we obtained the standard DEA model to gain the efficiency score \(\hat{\mathcal{D}}_{k}\)\((k=1,2,3,\cdots L)\) of individual DMUs. Additionally, we generated random samples in the size L from standard DEA efficiency scores \(\{\hat{\mathcal{D}}_{k}\)\((k=1,2,3,\cdots L)\}\) and applied kernel density estimation to obtain \(\{\mathcal{D}^{*}_{1b}\), \(\mathcal{D}^{*}_{2b}\), \(\mathcal{D}^{*}_{3b}\)\(,\cdots\mathcal{D}^{*}_{Lb}\}\). Next, we calculated the pseudo dataset \(\{(x_{k},\ y^{*}_{kb}),\ k=1,2,3,\cdots L\}\) to generate RBT (reference bootstrap technology) and calculated the bootstrap efficiency score \(\hat{\mathcal{D}}^{*}_{kb}\)\((k=1,2,3,\cdots L)\) of the standard DEA efficiency score \(\hat{\mathcal{D}}_{k}\)\((k=1,2,3,\cdots L)\) of individual DMUs, finding the bootstrap corresponding to the model's values. Finally, in order to obtain the bootstrap mean value, it was repeatedly performed 2,000 times and the gap in the standard DEA efficiency score and bootstrap efficiency score was a bias score of this methodology.
## 2 Related Work on Space DTN  
DTN was originally developed for space communications. Considering the similarity in communication characteristics, DTN has been extended to terrestrial wireless networks and wireless sensor networks (WSNs) [31].  
DTN was introduced to the geo-stationary orbit (GEO)-satellite communication systems in [32]. Some experiment-type work has been conducted for DTN architecture/protocols in satellite and space communications (including deep space). A series of Deep Impact Network Experiments (DINETs) of DTN [33] was conducted by Jet Propulsion Laboratory (JPL). These experiments proved the feasibility of DTN application in deep-space communications, although without a solid performance evaluation. Tests and experimental deployment of DTN were also performed on the International Space Station (ISS) [34; 35].  
Some analytical studies have also been conducted with DTN-type space network protocols, such as the CCSDS File Delivery Protocol (CFDP), whose functionalities were split between BP and LTP [36; 37; 38; 39]. The reader can refer to [38; 39] for the details.  
In the past decade, some significant collaborative work has been carried out in developing space networks, including deep-space networks. A part of this work focused on DTN's LTP [40; 41; 42; 43; 44; 45], while the rest examined the design and performance of BP [46; 47; 48; 49]. A novel approach for the estimation of bundle delivery time was proposed in [50]. In [51], a model was proposed for a retransmission timer length for BP to optimize the transmission performance. Some studies have explored the impact of link disruption on BP [52; 53; 54].  
Some work has also focused on the energy consumption and transmission overhead of BP [55; 56].  
However, none of the aforementioned relative work studied the performance of DTN protocols over different cislunar relay paths in a controlled comparative manner.
# On Blockchain-Based Cross-Service Communication and Resource Orchestration on Edge Clouds
## 1 Introduction
The transactions are stored in a ledger in the form of blocks and the order of the blocks is based on cryptographic hashes. The process that the blocks are produced ensures the immutability, data integrity, non-repudiation and security of the Blockchain ledger. Furthermore, Blockchain platforms such as Hyperledger Fabric ([PERSON], 2016), provide smart contracts that enable an event-based execution of transactions based on predefined rules and conditions. This is of high importance in incorporating any business logic as automated functionality in the Blockchain network. This article proposes a Blockchain-based NSM and a resource orchestration mechanism that enables the CSC in edge cloud (EC) and the establishment of the NSM. This mechanism is based on Hyperledger Fabric, a permissioned Blockchain platform, and leverages the functionality of smart contracts to automate the interaction among network services that are part of different service chains within an EC. Capitalizing on and extending our previous work ([PERSON], 2016), the main contributions of the article are summarized as follows:  
* At the user layer, we specify the essential functionalities, i.e., registration, service discovery, service lease and billing, for both network service providers and consumers to become stakeholders of the marketplace. * At the Blockchain layer, we update the asset definitions of ([PERSON], 2016) to facilitate cross-service orchestration with minimum possible data volume on the distributed ledger and preserve privacy of the users regarding their Network Slice information and structure. * At the NFV layer, we provide an analytical description of our novel Service Orchestrator that establishes CSC orchestration leveraging Blockchain capabilities. * We provide proof of concept results that demonstrate the applicability and efficiency of the proposed approach in terms of deployment time and reserved resources. The rest of the paper is organized as follows. In Section 2, the related literature is presented. Section 3 describes the architecture and its layers, while Section 4 presents the functionality of the NSM. Section 5 describes the interactions of the automated CSC and Section 6 highlights the benefits of the proposed approach in a real experimental use case. Finally, Section 7 draws the conclusions and proposed possible future extensions of the presented work.
## 2 Background
### Pre-Processing: X-ray 10-Correction  
During CT data acquisition, the patient body is irradiated with the photons emanating from the X-ray source. Some of the photons are attenuated during the photon-matter interaction. An X-ray detector detects the unattenuated or transmitted photons generating projection images. X-ray detection involves the two-level conversion process, where the X-ray photons are converted to light, and the photo-diode array converts them to electrical signals. Analog-to-digital converters (ADCs) of the acquisition system will transform electric signals into digital signals and store them in a compressed format. These 2D images are called detector data projections and are denoted by \(d(u,v)\), where \(u\) and \(v\) are the detector row and column indices. Conventionally, the detector data projections are transformed into X-ray intensity projections as per the following equation:  
\[I(u,v)=c\cdot e^{-d(u,v)} \tag{1}\]  
where c is a scaling factor. \(I(u,v)\) is the intensity image data. The exponential decay of the X-ray intensity during X-ray transmission through the patient body is given by the Beer-Lambert law [20; 23]. The total attenuation values along the X-ray path are given by  
\[P(u,v)=ln\bigg{(}\frac{I_{0}(u,v)}{I(u,v)}\bigg{)} \tag{2}\]  
where \(I_{0}\) projections are stored in the CT system during the calibration of the scanner by acquiring the intensity projections without any object. Formula (2) is also called _10-correction_. A CT 3D volume is reconstructed from the attenuation projections using state-of-the-art reconstruction algorithms.  
From the detector, the DAS collects the raw sensor data which must be multiplied with a factor of f to obtain the detector data projections, as given by  
\[d(u,v)=f\cdot Raw(u,v) \tag{3}\]  
Figure 1: CT scanner components. Reprinted/adapted with permission from Ref. [18]. Copyright 2019, IEEE.  
Commercial CTs usually do not provide the projections as detector data projections, but they convert them to intensity image data. In fact, pre-processing algorithms usually process projections in intensity image data and then apply the _10-correction_ for the reconstruction step.  
In Section 4, we propose a mathematical optimization of the _10-correction_ that uses directly the raw sensor data, instead of using the converted intensity image data.
## 4 Proposed Protocol
### Data Flow Model  
The data are collected from the deployed SNs in their respective assigned TDMA slots. Each SN sends data packets to the specific CH, which is the UAV. We employ selective repeat automatic repeat request (ARQ) protocol for reliable data transmission [39]. In EEUCH, the SN will discard all data packets that have been sent to the UAV and are acknowledged by the UAV due to the SN's memory constraints. For those data packets which are sent to the UAV but are not acknowledged yet, the SN must transmit these packets again in the subsequent round to UAV to ensure reliability.  
The reliable transmission of data between SNs and a UAV is essential in WSNs. This reliable transmission is affected by various factors such as the duration of TDMA slot assigned by the UAV and the channel behavior. It is essential to ensure that the channel conditions remain stable for reliable data transmission. If the channel conditions change frequently, the probability of receiving Ack of data packets from the UAV is reduced, leading to longer waiting time for the SN and higher energy consumption. Additionally, in such scenarios, the SN retains the data packets for the next round of UAV, and no further sensed data can be stored because of the limited memory capacity of the SN.  
The duration of the TDMA slot assigned by the UAV is another vital factor that must be considered to ensure reliable data transmission. If the TDMA slots have a short duration, not all data packets can be reliably communicated within that period. As a result, the remaining data packets will have to wait for the next round of UAV. Therefore, the TDMA slot duration should be set so that all data packets can be communicated reliably during the assigned slot. However, if the TDMA slots have a more extended duration, the UAV hover time increases.  
In summary, for reliable data transmission, it is crucial to carefully tune the parameters such as the TDMA slot duration, channel condition, and UAV hover time. By optimizing these parameters, the reliability of data transmission can be improved, and the SN can efficiently transmit the sensed data to the UAV without experiencing significant delays or data losses. The whole operation procedure of the EEUCH protocol is depicted in Figure 5.
# Challenge-Response Pair Mechanisms and Multi-Factor Authentication Schemes to Protect Private Keys
## 1 Introduction
An important technology presented in this paper is to allow each user to always keep the private keys secret, and away from the Keystores and HSM, while establishing clear authentication for users during the key pair generation cycles. The paper is organized as follows:  
* In Section 2, the paper describes secure MFA schemes that are easy to use, combined with methods to generate private keys that truly protect users' privacy. We describe a challenge-response pair (CRP) mechanism that is zero-knowledge, with N authentication factors such as tokens, biometry, and blockchain-based virtual tokens. During normal operations, the terminal device uses all factors of authentication to retrieve the private keys Sk and sign a document or a financial transaction. * In Section 3, we suggest protocols based on a scheme in which the private keys are generated and kept secret by the user, while a service entity, a Keysstore corporation, can authenticate the user, and transmit the public keys to the cloud, or a certificate authority (CA). The standardized LWE post-quantum cryptographic CRYSTALS Dilithium is suggested in the implementation of this last feature. * In Section 4, we present the experimental work, the generation of reference tables from three important factors of authentication: PUF-based physical tokens, template-less biometry, and blockchain-based virtual tokens. * In Section 5, we further analyze and benchmark the protocols and discuss methods to handle the loss of the factors of authentication. This is followed by the conclusion in Section 6 and suggested future work.
# Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form
## 2 Materials and Methods
### Model Evaluation
#### 2.3.3 Jensen-Shannon Divergence  
Since the confusion matrix is based on the MAP (Maximum a Posteriori) rule, some interesting behaviors (how wrong are the results, what are the situations where the output is completely uniform, etc.) may be invisible. For this reason, we evaluated the Jensen-Shannon (JS) divergence between \(b_{C}(c)\) and \(f_{C}(c)\). The JS divergence is based on Kullback Laibler (KL) divergence but has the advantage to be symmetric. Suppose we have two distribution \(P\) and \(Q\) over the same set \(\mathcal{X}\), then the JS divergence is defined as \(JS(P,Q)=\frac{1}{2}KL(P,M)+\frac{1}{2}KL(Q,M)\); where \(M=\frac{1}{2}(P+Q)\), and \(KL(P,M)\) and \(KL(Q,M)\) are respectively the KL divergence between \(P\) and \(M\) and \(Q\) and \(M\).
## 6 Proposed Algorithms
### Deep Neural Network-Based Tuning Algorithm
#### 6.2.1 Dataset Generation and DNN Structure  
The dataset for the development of the algorithm is created using experimental setup I. A specific amount (60 MB) of data is transferred from one sender node to a varying number of receiving nodes (from 1 to 9) with different congestion control algorithms, such as OLIA, LIA, BALIA, Cubic, and wVegas. For each case (a certain number of receiving nodes and a congestion control algorithm), the average throughput and corresponding energy consumption are measured when a single interface (WiFi or LTE) is used or both interfaces are. To train the DNN, 30 samples are used for each case. Similarly, 15 samples are used for testing with the DNN. The regressor used is based on a DNN following a multilayer perceptron (MLP) architecture with five hidden layers and 200 neurons in each layer. The 'ReLU' activation function is used for the hidden layer. The DNN is trained for a large number of iterations on the generated dataset to obtain accurate results. The neural network model is depicted in Figure 8.