esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
This section presents the results obtained from the selected studies [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78]. The results suggest that the pioneer efforts in the context of these four parameters (sensor types, energy harvesting techniques, damage inspection techniques, and damage detection methods) have been made during the first two decades of the twenty-first century. Therefore, most of the selected studies for this SLR are within this period. It has also been observed that most of the new articles in the domain of the deployment of WSN (SHM applications) focus on different aspects, such as communication protocol, time synchronization, feature extraction, and pattern recognition. These parameters such as communication protocol, time synchronization, feature extraction, and pattern recognition do not lie within the scope of this article.  
Data extraction and synthesis, as shown in Table 3, were performed to get the answers to our research questions. An overview of the classification of selected studies into three predefined categories (WSN platforms, energy harvesting techniques, and a combination of both) is provided (Section 3.1). In addition to the three main categories, there are three more design factors. These design factors/parameters are sensor utilization (Section 3.2), system inspection scale (Section 3.3), and response type (Section 3.4).  
Figure 3: Statistics of research articles according to the publication year.
## 2 Materials and Methods
### Antibiotic Susceptibility Testing  
AST was conducted on the _E. coli_ (n = 220) and coliform (n = 138) isolates with the Kirby-Bauer method [21] using the following antibiotics and the breakpoints determined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [22]: ciprofloxacin (CIP: 5 \(\mu\)g), tetracycline (TE: 30 \(\mu\)g), gentamicin (CN: 10 \(\mu\)g), trimethoprim/sulfamethoxazole (SXT: 1.25/23.75 \(\mu\)g), ampicillin (AM: 10 \(\mu\)g), amoxicillin/clavulanic acid (AMC: 15 \(\mu\)g), ceftazidime (CAZ: 10 \(\mu\)g), cefoxitin (FOX: 30 \(\mu\)g), cefotaxime (CTX: 5 \(\mu\)g), meropenem (MEM: 10 \(\mu\)g), cefadroxil (CFR: 30 \(\mu\)g), cefuroxime sodium (CXM: 30 \(\mu\)g), cefixime (CFM: 5 \(\mu\)g), imipenem (IMP: 10 \(\mu\)g), and cefepime (FEP: 30 \(\mu\)g). Antibiotic disks were supplied by Oxoid (Oxoid Limited, Basingstoke, UK). Reference strains of _E. coli_ ATCC 25922, _Klebsiella pneumoniae_ ATCC 700603, _Enterobacter cloaca_ ATCC 700323, _Serratia marcescens_ ATCC 8100, and _Citrobacter freundii_ ATCC 43864 were used for quality control for AST tests (bioMerieux Inc., Durham, UK).  
_E. coli_ and coliform isolates were characterized as an antibiotic (AMR), or multidrug-resistant (MDR), as proposed by others [23]. In brief, AMR is defined as non-susceptibility to at least one antibiotic group to which the test pathogen is typically susceptible, whilst MDR, as non-susceptibility to at least one agent from three antibiotic groups.
## 5 Discussion and Future Work
Hence, we will consider the comparison of performance evaluation of routing in an SDN only environment using ONOS/SDN-IP with other SDN routing approaches for future work. The next generation wireless networking viz. 5G/6G network is considered to be fully SDN based [39; 40]. The paradigm shifts in mobile communications by the conceptualization and implementation of 5G/6G added a strong requirement for SDN in the modern network environment. The wired/wireless network and server virtualization, high speed communications at highly dense smart devices with ultra low latency for real time mission critical applications, energy efficient smart network deployment with efficient radio access network (RAN) design, smart spectrum management and implementation are the part of 5G and beyond networks that directly concern people's modern living standards. Hence, from core network to end-access network service provisioning, cloud computing to fog computing to edge computing--they are mostly related to the availability of 5G and SDN, and the 4G to 5G migration and the performance evaluation of SDN based 5G network as compared with 4G networks are considered to be future work. Though network congestion optimization is not within the scope of this study, queue length analysis of the network device provides the trade-off between cost and performance of the network to understand the impact of congestion in SDN. Queue length analysis is also considered as future work. Routing implementations over hybrid SDN with multiple controllers and their placement, a large and varying number of data plane devices, and so forth, could be the subject of future work to reflect the situations of CG-ISP networks when implemented into reality. A single instance of an ONOS controller is used in the present study. Multiple instances can be run at the same time for high available services. Multiple instances help in the load balancing of the whole network and add reliability to the network. Additionally, other available network controllers can be used to replicate the results for comparison in addition to ONOS.
## 5 Discussion
### Variation in Cladding Parameters  
Firstly, the Euclidean distances--thicknesses curves under different cladding materials and thickness are discussed. Figure 6a is the Euclidean distances--thicknesses curves with and without the GS cladding, and Figure 6b is the errors between them. As shown in Figure 6b, the maximum error is 5.78%. It could be concluded that the feature is helpful to reduce the cladding-induced interference. This result is consistent with the analysis in Section 2.  
Furthermore, except for the GS cladding, a stainless-steel sheet cladding (SS cladding) is also commonly used in petrochemical and power generation applications. Then, performances of features under SS cladding are studied. Figure 7 shows features obtained with and without a 0.5 mm thick SS cladding. They are calculated through the analytical mode by setting the relative magnetic permeability and conductivity of the SS cladding to 1 and 1.35 MS/m, respectively. As shown in Figure 7, for the SS cladding, the maximum differences in Euclidean distance could be calculated by (0.3877-0.3809)/0.3877 = 1.8%. This indicates that the similarity measurement based feature is also available for SS cladding. Thus, the feature is insensitive to cladding materials.  
In addition, except for the cladding materials, features with different cladding thicknesses are also analyzed. According to a previous study [24], the designed cladding thicknesses are always between 0.3 and 0.7 mm, so 0.3 mm, 0.5 mm, and 0.7 mm thick claddings are examined in this study. Moreover, as the difference in Euclidean distances obtained with and without the GS cladding is larger, the GS cladding materials is used herein. Figure 8 shows the Euclidean distances with different GS cladding thicknesses. The maximum relative errors of the Euclidean distances obtained from the 0.3 mm and 0.5 mm thick cladding is calculated as (0.3763 \(-\) 0.3653)/0.3653 = 3.01%, and that for the 0.7 mm  
Figure 6: (**a**) Euclidean distances obtained with and without the GS cladding. (**b**) associated relative error.  
and 0.5 mm thick cladding is calculated as \((0.3516-0.3653)/0.3653=3.75\%\). They are both small, which indicates that the Euclidean distance is independent of the cladding thickness.  
In conclusion, the similarity measurement based feature is insensitive to the cladding materials and the thicknesses.
## 5 Application Case Study
### System Architecture and Software Development  
The functional architecture of the projected system includes multiple aspects, including a perception layer, network layer, data layer, twin layer, application layer, and user layer. The perception layer collects real-time energy station operation and maintenance information through IoT devices. The network layer is responsible for transmitting information within the system. The data layer is responsible for organizing, storing, and analyzing spatiotemporal data. The twin layer presents the actual operational status of energy stations in digital space. The application layer is a functional application that revolves around the intelligent operation of energy stations, including operation condition monitoring, load forecasting, energy-saving analysis, and other functions. The user layer sets different permissions for different users such as energy investment units, government management agencies, and social public organizations to achieve the effective utilization of the system. A diagram of the projected system's architecture is shown in Figure 5.  
At the software development level, this study first used the Revit 2023 software to establish the BIM model of the energy station. Then, based on the low code twin engine, we achieved the fusion of digital twin models. To acquire the real-time operation status of energy stations, energy information within the system is collected through IoT. At the same time, we utilized the API interface of the low code twin engine, integrated AI algorithms, and processed and analyzed the energy operation data. The ultimate goal was to achieve real-time, dynamic, and intelligent construction of the intelligent operation and management platform for digital twin energy stations. Taking the WSHP energy station in Hankou Binjiang International Business District as an example, the system development process is shown in Figure 6.  
Figure 4: Project system functional design diagram.  
Figure 3: Location of each system in the project and internal sectional view of the energy station.
## 5 Discussion and Outlook  
In this study, we have analyzed the topology of the Bitcoin transaction graph and the dynamics on it. The results have implications for the anonymity of users. Our contributions are:  
* Reintroducing the notion of entity merging [5], we were able to observe _structural patterns_ in merging events previously unknown (_cf._ Figure 9). In general, the merging of public addresses by simultaneous usage of several addresses is the most important challenge to Bitcoin anonymity;
* The _size_ of the merged entities (measured in number of public addresses) follows a scale-free distribution (note that the _size_ distribution we are concerned with is different from the _link_ distribution considered by [PERSON] and [PERSON] [5].) This alone is an interesting finding, excluding several generating mechanisms. In particular, it is hardly conceivable that the Bitcoin merging events are centrally controlled (as some conspiracy theories (see for example [15]) suggest);  
Figure 10: Time evolution of dormant coins. (**a**) Fraction of dormant coins for different cutoffs; (**b**) Probability \(P(x>\Delta t)\) that a bitcoin was not used for a certain amount of time \(\Delta t\). The black line shows empirical data and the red line a fitted exponential function.  
* The exponent \(\Gamma\) in the abovementioned law and Equation 1 increases in absolute value over time. This shows that larger entities occur less often, thus overall anonymity increases over time. Furthermore, our results suggest that \(\Gamma\) has become stationary in recent times;
* Stationarity could be found in several additional parameters describing topology and dynamics of the transaction network, e.g., the ratio of active entities for various time thresholds is nowadays constant, with implications on future investigations on Bitcoin anonymity;
* The dynamics of dormant coins were investigated. This effect seems to largely come into existence by independent processes (e.g., lost passwords of individual users). Statistical knowledge on dormant coins might (probabilistically) reduce the Bitcoin transaction anonymity set.
# The Concept of Using 2D Self-Assembly of Magnetic Nanoparticles for Bioassays
## 2 Materials and Methods
### Preparation of Magnetic Chain Structures  
In a 25 mL volumetric flask, the concentration of APTES-modified magnetic nanoparticles in suspension was lowered and its pH was adjusted to around 4 by adding 2 M HCl solution. Next, 1 mL of this prepared suspension was deposited onto a low-density polyethylene (LDPE) film and allowed to dry in ambient conditions. The method requires an asymmetric surface and, therefore, LDPE film was prepared in the stretched form, where a sample width of 1 cm was stretched to 5 cm. The drying time for a 1 mL droplet at 20 C is about one hour. After the droplet dries, a deposit with ring structures of APTES-stabilized iron-oxide nanoparticles remains on the LDPE substrate.
###### Abstract  
This paper proposes novel unbiased minimum-variance receding-horizon fixed-lag (UMVRHF) smoothers in batch and recursive forms for linear discrete time-varying state space models in order to improve the computational efficiency and the estimation performance of receding-horizon fixed-lag (RHF) smoothers. First, an UMVRHF smoother in batch form is proposed by combining independent receding-horizon local estimators for two separated sub-horizons. The local estimates and their error covariance matrices are obtained based on an optimal receding horizon filter and the smoother in terms of the unbiased minimum variance; they are then optimally combined using Millman's theorem. Next, the recursive form of the proposed UMVRHF smoother is derived to improve its computational efficiency and extendibility. Additionally, we introduce a method for extending the proposed recursive smoothing algorithm to a posteriori state estimations and propose the Rauch-Tung-Striebel receding-horizon fixed-lag smoother in recursive form. Furthermore, a computational complexity reduction technique that periodically switches the two proposed recursive smoothing algorithms is proposed. The performance and effectiveness of the proposed smoothers are demonstrated by comparing their estimation results with those of previous algorithms for Kalman and receding-horizon fixed-lag smoothers via numerical experiments.  
Fixed-lag smoothing; receding-horizon estimation; minimum variance; unbiased estimation; Millman's theorem; Rauch-Tung-Striebel smoothing +
Footnote †: journal: Applied Sciences
# Reinforcement Learning Based Resource Management for Network Slicing
###### Abstract  
Network slicing to create multiple virtual networks, called _network slice_, is a promising technology to enable networking resource sharing among multiple tenants for the 5 th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant's point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users.  
network slicing; dynamic resource adjustment; Q-learning +
Footnote †: journal: Applied Sciences
## 3 Use Case and Recommendation System  
We consider a smart city including several streets. Here, a street with the possibility of car park is considered as a segment. In order to increase performance, very long streets are divided into several segments.  
The drivers have a list of streets and the number of available segments in every street (i.e., for a long street, the segments are numbered starting from beginning of the street).  
In order to get a recommendation, a driver first sends a request including his destination location (i.e., street name and segment number) and a time period that he intends to be there. When the system receives the request, it applies a MLNN algorithm to the historical data of the requested parking segment to predict the number of free parking places for the requested time interval.  
Our recommendation system considers three levels of parking place availability called L1 (zero or very low available places), L2 (several available places) and L3 (high availability) using two thresholds (i.e., T1 and T2) as follows:  
* less than or equal to T1 free places is considered as level L1;
* between T1 and T2 free places is considered as level L2;
* equal or above T2 free places is considered as level L3.  
As Algorithm 1 shows, if the level of the selected parking segment by a driver is predicted as L3 for the requested time interval, the recommendation system recommends the selected segment to the driver.  
If the level of the selected parking segment by a driver is predicted as L2, the system defines a circle area with center of the requested segment and radius of X (e.g., X = 500) meters. It only considers and evaluates the historical data of those parking segments that are located in the circle area. In this case, the system separately trains the best model on the data sets of each parking segment in the circle area. Finally, among the eligible parking segments (i.e., the segments in the circle area which their availability levels are L3 for the requested time interval), the closest parking segment to the requested destination is recommended to the driver. However, if the recommendation system can not find a segment with level of L3 in the circle, it offers the selected segment itself to the driver.  
If the level of the selected parking segment by driver is predicted as L1, the closest L3 level parking segment to the requested destination is recommended to the driver. If there is no segment with level of L3, but there are one or more segments with L2, it suggests the closest segment with L2 to the requested segment to driver. Otherwise, the system sends a warning message to the driver.  
```
foreachrequestdo  run MLNN algorithm for SegId if(SegId.availability==L3)then  return(SegId) if(SegId.availability==L2)then  run MLNN algorithm for the related circle ifthere are some L3 availability in the circlethen  return(closest L3 to SegId) else ifthere are some L2 availability in the circlethen  return(closest to SegId) else  return 0
```  
**Algorithm 1**Segment-Selector (\(SegId\), timeInterval)
# A Study on the Stress Field Around an Elliptical Wellbore with an Eccentric Casing in Deep Wells
## 3 Result Analysis and Discussion
### The Influence of the Elastic Modulus of Cement Sheath on Casing  
3.1 The Influence of Elastic Modulus of Cement Sheath on the Maximum Stress Component and Equivalent Stress of Casing  
Figure 13 shows the distribution of the maximum stress value on the outer wall of the casing under different eccentricities while keeping other conditions unchanged and changing the elastic modulus of the cement sheath.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(\varepsilon\)**=0.0** & \(\varepsilon\)**=0.3** & \(\varepsilon\)**=0.6** & \(\varepsilon\)**=0.9** \\ \hline \(\Delta\sigma_{\tau}\)/MPa & 12.33 & 11.79 & 29.09 & 55.68 \\ \(\Delta\sigma_{\text{c}}\)/MPa & 84.88 & 91.74 & 93.94 & 96.16 \\ \(\Delta\tau\)/MPa & 12.69 & 19.57 & 27.58 & 35.31 \\ \(\Delta\sigma_{\text{c}}\)/MPa & 64.93 & 66.97 & 68.19 & 69.74 \\ \hline \end{tabular}
\end{table}
Table 8: The increase in casing stress under different eccentricities when the unevenness of geostress increases.  
Figure 12: Maximum stress distribution on the outer wall of the casing under different geostress non-uniformities and eccentricities. (**a**) Radial stress; (**b**) circumferential stress; (**c**) shearing stress; (**d**) Mises equivalent stress.  
When eccentricity is fixed, an increase in the elastic modulus of the cement sheath has a relatively small impact on the variation of shear stress and radial stress, while it has a relatively large impact on the variation of equivalent stress and hoop stress. Specifically, let us take the changes in radial stress and equivalent stress as examples. (1) Radial stress is when eccentricity is fixed and as the elastic modulus increases, the stress value first slightly decreases and then increases. This indicates that the variation of the elastic modulus of the cement sheath has a minor impact on the radial stress. (2) Equivalent stress is when the stress variation is similar at different eccentricities, exhibiting the same pattern and peak values. When \(E_{2}\) is 25 GPa, the hoop stress values reach their peak at each eccentricity. This indicates that the hoop stress value is greater under a higher elastic modulus.
## 6 Conclusions  
In this paper, the theoretical basis of STAP was presented first. The individual steps of the classical SMI STAP algorithm were discussed. The importance of the characteristic parameters describing the STAP linear processor was highlighted by discussing the performance parameters, and the most important features of MIMO radar were presented.  
This was followed by a critical analysis of the past and current methods of estimating the clutter covariance matrix. Statistical and non-statistical methods were compared. An area of research development on current methods was indicated, which prompted the search for another method of estimating the clutter covariance matrix. As a result of a series of analyses, the third chapter presents a description and course of a novel method for estimating the clutter covariance matrix based on LU decomposition.  
Current analyses of the literature each unequivocally state that various methods of estimating the clutter covariance matrix are appropriate in STAP, with a move away from statistical methods. Currently, the most developed are non-statistical methods for estimating the clutter covariance matrix. However, the authors of the article noted the  
Figure 10: Values of received signals by the MIMO radar antenna array as a function of range after transmitting the first pulse and SMI-LU processing.  
possibility of reducing the computational complexity and software implementation of the SMI method, which has existed for years.  
This article is devoted to the presentation of a new statistical STAP algorithm based on LU decomposition. The theoretical analyses performed in the course of the work, the calculations, and the obtained results of simulation studies allowed us to make a clear conclusion that the SMI-LU method effectively eliminates clutter in heterogeneous environments, which should be considered a great advantage.  
In conclusion, this paper proves that it is possible to estimate the covariance matrix of STAP clutter by using the MIMO radar geometry model and SMI-LU algorithm.  
Conceptualization, A.K., A.S. and B.S.; methodology, A.K. and A.S.; software, B.S.; validation, A.K.; formal analysis, A.K. and A.S.; investigation, A.S. and B.S.; resources, A.S. and B.S.; data curation, A.S. and B.S.; writing--original draft preparation, A.S. and B.S.; writing--review and editing, A.S. and B.S.; visualization, A.S. and B.S.; supervision, A.K.; project administration, A.K.; funding acquisition, A.K., A.S. and B.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All Data are available upon reasonable request made to the corresponding author.  
The authors declare no conflict of interest.