esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Quantum computing is a promising technology that may provide breakthrough solutions to today's difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. An algorithm referred to as Quantum Approximate Optimization Algorithm (QAOA) has been recently proposed to approximately solve hard problems using a protocol know as bang-bang. The technique is based on unitary evolution using a Hamiltonian encoding of the objective function of the combinatorial optimization problem. The QAOA was explored in the context of several optimization problems such as the Max-Cut problem and the Traveling Salesman Problem (TSP). Due to the relatively small node-size solution capability of the available quantum computers and simulators, we developed a hybrid approach where sub-graphs of a TSP tour can be executed on a quantum computer, and the results from the quantum optimization are combined for a further optimization of the whole tour. Since the local optimization of a sub-graph is prone to becoming trapped in a local minimum, we overcame this problem by using a parallel Ant Colony Optimization (ACO) algorithm with periodic pheromone exchange between colonies. Our method exceeds existing approaches which have attempted partitioning a graph for small problems (less than 48 nodes) with sub-optimal results. We obtained optimum results for benchmarks with less than 150 nodes and results usually within 1% of the known optimal solution for benchmarks with around 2000 nodes.  
large-scale optimization; ant colony optimization; parallel ACO; quantum computing; QAOA; hybrid quantum-classical algorithm; HPACO
# Design and Optimization of Modular Solid Rocket Grain Matching Multi-Thrust Performance Curve
## 2 Design Methods
### Governing Equation  
Based on the aforementioned discussion, this section will provide nonlinear algebra equations for the design of modular grains. During any thrust phase, the total mass flow rate should be a constant value \(Q_{i,\text{target}}\) provided by design target, so there is  
\[\left\{\begin{array}{l}a_{i}=Q_{i,\text{target}}\\ b_{i}=0\\ c_{i}=0\end{array}\right. \tag{22}\]  
According to the pressure-flow rate relationship in a Laval nozzle, there is  
\[Q_{i,\text{target}}=\frac{A_{\text{t}}p_{\text{c},i}}{c*} \tag{23}\]  
where \(A_{\text{t}}\) is the nozzle throat area (m\({}^{2}\)) and \(c*\) is the characteristic velocity of the propellant (m/s). It means that the flow rate is directly determined by the combustion chamber pressure given by the design target.  
Now, there are \(3N\) equations. To make sure the \(i\)th segment burns out simultaneously at the end of the \(i\)th thrust phase, there are the following additional equations:  
\[\left\{\begin{array}{l}w_{\text{st},i,}+r_{\text{st},i,j}t_{\text{b},i}=w_{ \text{max},\text{st},i}\\ w_{\text{st},i,}+r_{\text{st},i,i}t_{\text{b},i}=w_{\text{max},\text{st},i} \end{array}\right. \tag{24}\]  
where \(w_{\text{max},\text{st},i}\) and \(w_{\text{max},\text{st},i}\) are the max web thickness of the \(i\)th segment's star grain and slot grain, respectively. The expanded form of the max web thickness can be found in Equations (3) and (9).  
Equations (22) and (24) can be gathered to form the following governing equations:  
\[\left\{\begin{array}{l}a_{i}=Q_{i,\text{target}}\\ b_{i}=0\\ c_{i}=0\\ w_{\text{st},i,j}+r_{\text{st},i,j}t_{\text{b},i}=w_{\text{max},\text{st},i}\\ w_{\text{st},i,j}+r_{\text{st},i,j}t_{\text{b},i}=w_{\text{max},\text{st},i} \end{array}\right.,i=1,\ldots,N \tag{25}\]  
Therefore, there are \(5N\) equations, and at most \(5N\) unknowns can be solved. All the unknowns are listed in Table 1.  
In fact, there are \(13N+1\) unknowns that need to be solved, which is much more than the number of equations. So, among them, only \(5N\) unknowns are picked out to be solved by governing equations, and others are provided by the designers.  
The design steps are simple. First, according to the \(p\)-\(t\) curve, determine the grain segment number \(N\), \(t_{\text{b},i}\), and \(Q_{i,\text{target}}\) during each thrust phase. Second, according to experience, provide the unknowns marked with \"Given by designer\" in Table 1. Third, solve the governing equations to obtain the unknowns marked with \"Needs solving\" in Table 1. In theory, the grain design result shall match the given internal ballistic curve well.  
To summarize, the design process is governed by \(5N\) algebraic equations, providing an easy way to design a multi-thrust grain.
# Reasons for the High Electrical Conductivity of Bismuth Ferrite and Ways to Minimize It
###### Abstract  
The reasons for the high electrical conductivity of bismuth ferrite due to its natural composite structure, structural non-stoichiometry, redox processes, and the boundary position in the perovskite family have been considered. It has been shown that it is possible to significantly (2-3 orders of magnitude) reduce the conductivity of BiFeO\({}_{3}\) by introducing large-sized ions of rare-earth elements (REE: La, Pr, Nd, Sm, Eu, Gd with 0.94 \(\leq\)\(\overline{R}\)\(\leq\) 1.04 A) in amounts of up to 10 mol %. An interpretation of the observed effects has been given. A consideration about the appropriateness of taking into account the presented results when developing devices using materials of the BiFeO\({}_{3}\)/REE type has been expressed.  
ceramic; BiFeO\({}_{3}\); rare-earth elements; electrical conductivity; Maskwell-Wagner relaxation 1  
11202020 January 202120 January 20  
cell, make it extremely difficult to perform a comprehensive study of this material or put it into practice in the areas most in demand.  
In a number of works it has been shown experimentally [7; 8] and theoretically [9] that under the conditions of solid-phase synthesis and sintering using conventional ceramic technology (without externally applied pressure) BiFeO\({}_{3}\) is not thermodynamically stable. The resulting product inevitably contains one or another amount of impurities, which, of course, leads to a decrease in its electrical resistance. At the same time, a low level of reproducibility and inconsistency of the results obtained by various groups of researchers becomes another important problem, which is due to the critical dependence of the target product not only on the conditions of synthesis and sintering, but also on the whole of its thermodynamic history.  
Based on a series of experiments, the features of BiFeO\({}_{3}\) phase formation have been studied in detail [10]. It has been shown that a significant role is played by processes caused by the structural non-stoichiometry of the objects, the appearance of which, due to both the high stability of the intermediate phases and the loss of oxygen as a result of partial reduction of the elements with a variable oxidation state, can also influence the BiFeO\({}_{3}\) macroresponses and its electrical conductivity. Based on the results obtained we proposed the methods for optimizing the technology of obtaining materials involving BiFeO\({}_{3}\).  
In this paper, we examine in greater detail the reasons for the increased conductivity of BiFeO\({}_{3}\) and the possibility of minimizing it by variations in the cationic composition upon modification of this multiferroic with rare-earth elements (REE).
###### Abstract  
Currently, the performance analysis of positioning algorithms and optimization of ground station deployment schemes are predominantly based on pure TOA or TDOA measurement information, and the relevant theoretical analysis is primarily the geometric analysis of optimal station deployment for fixed point targets, with few placement ranges and amount of station constraints. In practice, however, there are typically several measurements from TOA and TDOA stations, with a focus on positioning precision within a certain region or line trajectory, as well as the necessity for constraints on the ground station placement range. This paper proposes an efficient method for hybrid source localization using TOA and TDOA measurement information, establishes a mathematical model for hybrid source localization based on TOA and TDOA measurement information, derives and simulates the Gauss-Newton iterative localization algorithm with the least squares criterion, and performs a theoretical analysis of the least squares error and CRLB boundary to improve the accuracy of target localization in the aforementioned scenarios. Taking the average CRLB value of target line trajectory positioning error as the objective function, the ground station placement scheme of TOA- and TDOA-receiving sensors is optimized by utilizing a Genetic Algorithm with strong global optimization capability under the constraints of station placement range and station quantities, and a station placement geometry with better performance than typical station placement is obtained. Meanwhile, we summarize the general placement principles for TOA and TDOA hybrid source localization of target line trajectories.  
TDOA; TDOA; hybrid source localization; least-square algorithm; Cramer Rao Low Bound (CRLB); Genetic Algorithm (GA)
## 2 Materials and Methods
### Proteomic Analyses  
Transgenic AcV5-tagged GPD2 protein was purified from cells in the middle of the logarithmic phase maintained in HS medium (HS) or exposed to 100 mM NaCl for 6 h (HS+NaCl) by using a previously described protocol [39] with some modifications. Briefly, for each treatment, \(\sim\)10\({}^{10}\) cells were resuspended in 10 mL of lysis buffer [20 mM Tris-HCl (pH 7.0), 150 mM NaCl, 0.1 mM EDTA, 2.0 mM Mg(CH\({}_{3}\)COO)\({}_{2}\), 2.0 mM Benzamidine, 0.2 mM phenylmethanesulfonyl fluoride (PMSF), and 10% glycerol] containing 5 \(\upmu\)L mL\({}^{-1}\) plant protease inhibitor cocktail (Sigma, P9599), and phosphatase inhibitor cocktail (Sigma, PhosSTOP, PHOSS-RO; 1 tablet in 10 mL of buffer). Cells were broken by two passages through a French press at -5000 psi and the lysate was clarified by centrifugation at 16,000\(\times\)\(g\) for 30 min at 4 \({}^{\circ}\)C. The extract was centrifuged again at 80,000\(\times\)\(g\) for 90 min and the supernatant was then incubated at 4 \({}^{\circ}\)C with 20 \(\upmu\)g of a mouse anti-AcV5 antibody (Thermo Fisher Scientific, 14-6995) for 1 h followed by overnight incubation with protein G-agarose beads (Sigma, P7700; -200 \(\upmu\)L of packed resin per 40 mL of original cell extract). The beads were collected by centrifugation (5000\(\times\)\(g\) for 5 min) and washed five times with wash buffer [20 mM Tris-HCl (pH 7.0), 300 mM NaCl, 0.1 mM EDTA, 2.0 mM Mg(CH\({}_{3}\)COO)\({}_{2}\), 2.0 mM Benzamidine, 0.2 mM PMSF, 0.1% Triton X-100, and 10% glycerol] containing5 \(\mu\)L mL\({}^{-1}\) plant protease inhibitor cocktail and phosphatase inhibitor cocktail. Bead-bound proteins were then processed for liquid chromatography with tandem mass spectrometry (LC-MS/MS) analyses, as previously described [40]. Data were analyzed in Proteome Discoverer 2.2 software (Thermo Fisher Scientific) with MS Amanda 2.0 and SeQuest HT as search tools. The analytical pipeline has been described in detail before [40].
# Enhancing E-Grocery-Delivery-Network Resilience with Autonomous Delivery Robots
## 1 Introduction  
Recently, Autonomous Delivery Robots (ADR) are becoming an essential component in E-grocery delivery network systems, due to the increasing need for non-contact last-mile distribution in some emerging situations, such as the COVID-19 pandemic, natural disasters etc. Their ability to navigate through urban environments, efficiently transport goods, and provide contactless delivery options has made them a vital solution. However, the successful integration of ADRs into these networks is not without its challenges.  
One of the primary challenges is the inherent unpredictability of service times and demands in the delivery ecosystem. Stochastic service times, which refer to the variable time it takes to complete a delivery, and stochastic demands, which represent the fluctuating volume of orders, add layers of complexity to the system. These uncertainties can disrupt the seamless flow of deliveries, leading to potential inefficiencies and delays. Therefore, the resilient planning and operation of ADR-assisted E-grocery delivery systems considering the stochastic service time and demands have become a matter of paramount importance.  
To address these challenges, researchers and practitioners are delving into innovative approaches [1; 2; 3]. While progress has been made in addressing the challenges of stochastic demands and service time in last-mile delivery [4; 5; 6], we have identified limitations in current models, including inefficiently handling the planning issues of ADR in an assisted delivery network, the inability to flexibly consolidate parcels in micro hubs amidst stochastic service times and unpredictable delivery disruptions. Thus, in this study, we developed a deterministic mixed-integer model and a chance-constraint integer model for the ADR-assisted delivery-network system and to fill these gaps. We formulated the model as a two-echelon vehicle routing problem (VRP) with stochastic service time and stochastic demands (2E-VRP-SSSD). The aim of this study was to develop a novel formulation for efficiently solving the 2E-VRP-SSSD problem, and then assess the resilience performanceof the van-ADR delivery system. Thus, the contributions of this study are mainly in the following aspects:  
First, we introduce a novel two-echelon van-ADR delivery network with parcel consolidation designed specifically for resilient delivery settings. Both a deterministic model and chance-constraint model were used together for the 2E-VRP-SSSD, in which the uncertainty of service time and demand were considered.  
Second, we applied both of the models to the experiments to evaluate the performance of the ADR-assisted E-grocery delivery network, and tested the impact of three sensitive factors on the delivery network: the speed ratio between the ADR and van (RSAV), the service deadline (DL), and confidence level of constraints on variables. This study will potentially help transportation managers and operators to efficiently solve the related planning issues and deeply understand future resilience benefits when ADRs are adopted.
# Protection of Buried Pipelines from High-Speed Train Vibrations with Expanded Polystyrene Geofoam
## 5 Conclusions  
The main contribution of the current study is that it presents the impact of HST-induced vibrations on buried pipelines vertically crossing under an HSR line. It has to be stressed that the continuous expansion of HSR in urban environments will increase the occurrences of crossings with various types of lifelines. Another novelty is that it highlights the beneficial role of the proposed mitigation scheme using EPS for the reduction in the distress of the pipelines. Several researchers have applied the examined mitigation measure, i.e., EPS blocks, in order to protect buried pipelines subjected to permanent ground displacements, yet they have not included HST moving loads and the consequent vibrations. The impact of the proposed mitigation measure on various types of pipes has been presented for varying dynamic loading conditions. The present investigation concludes that the implementation of the proposed mitigation measure could successfully protect the buried pipelines from such dynamic loads. Hence, the application of a thin EPS layer above any type of pipe constitutes an easily applicable and cost-efficient protection method.  
The findings of this study will be useful for designing lifelines close to HSR lines and could be the basis for formulating practical guidelines. Nonetheless, since this is a first attempt to investigate the effectiveness of EPS between an HSR line and a buried pipe, further investigation is required to ensure the efficiency of the proposed mitigation measure. For instance, different soil conditions, train geometry, and loads that affect the frequency content of the vibrations, material non-linearities, EPS properties, and dimensions, issues related to the life-cycle performance of EPS, should also be examined in future extensions of the current work. Evidently, the effectiveness of the proposed mitigation measure would be further verified by performing a real-scale experiment.  
Conceptualization, A.L., Y.T. and P.N.P.; methodology, A.L., Y.T. and P.N.P.; software, A.L.; validation, A.L. and Y.T.; formal analysis, A.L., Y.T. and P.N.P.; investigation, A.L., Y.T. and P.N.P.; resources, A.L. and Y.T.; data curation, A.L., Y.T. and P.N.P.; writing--original draft preparation, A.L., Y.T. and P.N.P.; writing--review and editing, A.L., Y.T. and P.N.P.; visualization, A.L., Y.T. and P.N.P.; supervision, Y.T.; project administration, Y.T. All authors have read and agreed to the published version of the manuscript.  
This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme \"Human Resources Development, Education and Lifelong Learning 2014-2020\" in the context of the project \"Strengthening Human Resources Research Potential via Doctorate Research--2 nd Cycle\" (MIS 5000432).
## 5 Conclusions  
In agreement with many studies, most of the variation in Rs was explained by soil temperature. More significant are the differences in Rs we saw among the vegetation types when observed by sampling date and not for the annual mean. This demonstrates the importance of looking at Rs under various temporal and spatial scales, including weekly, monthly, and yearly, in order to achieve a better understanding of variation. Conducting the study over a number of years may provide us with more insight into vegetation's influence on Rs. The vegetation may still have driven the differences we saw in monthly Rs by their influence on litter decomposition rates, soil microbiology, and other soil microclimate characteristics. Future studies may want to gather more data on litter differences, partition the Rs into its autotrophic and heterotrophic components, and take the LAI under each vegetation type.  
The ability of remotely sensed VIs to predict Rs was limited in our study. NDLI was the only VI to show a significant difference between vegetation types, as well as correlate with certain monthly Rs rates. The lack of relationships we found otherwise may have been due to a scale discrepancy between our ground data and the VIs calculated from satellite data.  
Studies may want to focus on examining VIs, ability to predict processes under broader spatial scales as well as achieve a better understanding of VIs' spatial accuracy. Perhaps our vegetation types were at too fine of a scale compared to the satellite data.  
In conclusion, R\({}_{8}\) was mainly driven by soil temperature and moisture, but this relationship was significantly impacted by the vegetation types. That being said, our model may provide the basis for future models estimating R\({}_{8}\) in natural hardwood systems in the Appalachian Mountains. Remotely sensed VIs had very limited relationships with our fine-scale R\({}_{8}\) measurements.  
Conceptualization, R.L.H., J.R.S. and V.A.T.; methodology R.L.H., J.R.S. and J.A.P.; validation, R.L.H., J.R.S. and J.A.P.; formal analysis, R.L.H., J.R.S., J.A.P. and V.A.T.; data curation, R.L.H. and J.R.S.; writing--original draft preparation, R.L.H. and J.R.S.; writing--review and editing R.L.H., J.R.S., J.A.P. and V.A.T.; supervision, J.R.S.; project administration, J.R.S.; funding acquisition, J.R.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Forest Resources and Environmental Conservation, Virginia Tech; Mountain Lake Biological Station, Department of Biology, University of Virginia allowed experimental plot establishment.  
Data may be available for access by contacting the corresponding author.  
The authors declare no conflict of interest.
# Research on Multicore Key-Value Storage System for Domain Name Storage
## 3 Model Description
### System Structure  
We designed and implemented a full packet capture (FPC) system [35]. The system has multiple functions, such as packet receiving, nanosecond timestamp, load balancing, data packet preprocessing, application layer protocol analysis, data packet storage, and log management. As shown in Figure 1, there are two processes of auditor and FPC (Full Packet Capture) in Data Plane Development Kit (DPDK) [36]. The auditor process is mainly responsible for data packet capture and preprocessing, and the FPC process is responsible for in-depth processing of data packets. The whole system uses DPDK as a framework to process data packets, and uses a lot of optimization techniques in DPDK. After the network packet is received, the timestamp and packet information are first added to the inter-frame gap. Then, the data packets are load-balanced and distributed to multiple queues. After the data packet is transferred to the DPDK platform, it is copied. One is simply parsed in the FPC process and stored in the hard disk, and the other is transmitted to the high-level protocol parser module in the auditor process for complete parsing. The parsed information is stored in the form of logs. Each task is assigned one or more CPUcore according to the task complexity. The more complex the task, the more cores assigned. For example, the application layer protocol-parsing module is allocated four CPU cores, and the data-packet storage module is allocated eight CPU cores.  
In this paper, we focus on parsing the caching methods in the system, for which we design a new multicore caching model. The area marked in red in the Figure 1 is the cache model. We deploy a proxy cache table on each parsing core and set up a main cache table on the cache core.  
The protocol-parsing module writes cache entries in the main table and reads cache entries from the proxy table. After the main cache table is updated and written, it synchronizes the updated information to the proxy table to keep the proxy table in real-time. This read and write isolation is used to reduce the use of locks. In addition, lock usage is the most performance-impacting factor.  
To reduce the synchronization overhead further, we split the cache table into sub-tables. When the main cache table is synchronized to the proxy cache table, only those sub-tables that have just been updated need to be synchronized. If the sub-table was not updated in the previous sync cycle, then this sub-table does not need to be synced.