esa-sceva/satcom-chunk-collection huggingface dataset:
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## 5 Preconditioners for the Blocks
### Schur Complement Preconditioner
#### 5.2.1 PCD Preconditioner  
The Pressure convection-diffusion (PCD) approach can be intuitively justified when considering the operator that the Schur complement represents: matrix \(S=BF^{-1}B^{T}\) is the discrete counterpart of the continuous operator \(\operatorname{div}(-\
u\Delta+\mathbf{w}\cdot\
abla)^{-1}\
abla\), where \(\mathbf{w}\) represents the local wind (given by the solution of the previous iteration in the case of the Oseen problem). We can think of a preconditioner for the Schur complement as an approximation of the discrete version of the inverse of this operator: then, calling \(F_{p}\) the convection-diffusion operator built in the pressure space and \(L_{p}\) the corresponding Laplacian, we can assume that matrix \(F_{p}L_{p}^{-1}\) is an approximation for \(S^{-1}\). However, in the case of diffusion-dominated problems, \(F_{p}\) and \(L_{p}\) tend to coincide, yielding the identity matrix as preconditioner. Premultiplying by \(Q_{p}\), the pressure mass matrix, we can assure that in this extreme case the preconditioner goes back to the known optimal alternative.  
This preconditioner was proposed and derived formally in [4] and studied thoroughly in [25]. It provides a significant improvement with respect to the scaled mass matrix, keeping the computational cost low: matrices \(F_{p}\) and \(L_{p}\) are indeed smaller than \(F\) and \(L\), making this preconditioner  
Figure 2: Ordering of the nodes for the smoothing.  
particularly efficient. However, the rate of convergence seems to suffer when \(\
u\) gets close to zero, even for simple problems. Moreover, matrix \(F_{p}\) is not used in the original problem and, thus, needs to be built at every Picard iteration; the proper boundary conditions to apply to it are not straightforward to choose and they do not necessarily coincide with the conditions applied to the original problem. An analysis of this issue can be found in [14].
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 6 Some Final Considerations about Benchmarking, Decoherence Effects, and Fidelity  
Through the history of QKD developments since the BB84 protocol, many other approaches and different aspects in the key distribution have been tried. Of course, one of the main aspects is the security against eavesdropping, but others aspects sometimes go in different directions, for instance, the economy in the quantum resources or the robustness against quantum computer attacks. In the last case, the reduction of quantum resources in terms of not only efficiency but a feasible operation is important. Clearly, in the procedure being developed here, the economy has not been the focus, instead of the security, particularly based on the distributed tasks to set the key.
## 5 Discussion  
Considering the bandwidth requirement, the required bandwidth of SPAWN is proportional to grid divisions, for its message is the probability of the grid. The data amount of NBP are proportional to the number of particles used to express potential function. SBPVEM and PVSPA fit the given distribution before sending parameters as messages, so their requirement in bandwidth is small. In summary, \(C_{SPA}\gg C_{NBP}\gg C_{SBPVEM}\approx C_{PVSPA}\).  
SBPVEM and PVSPA reduce the communication bandwidth. Experiments show that the SBPVEM algorithm can sufficiently cope with the challenging environment that the PVSPA algorithm cannot handle. SBPVEM can obtain accurate node localization while reducing bandwidth requirements and has a better expressive ability than PVSPA.
## 2 Overview of MIMO Technology and Multi-Band Systems  
MIMO technology is pivotal for optimising energy harvesting in IoT systems by enabling the simultaneous transmission and reception of multiple signals, which boosts  
Figure 3: Proposed RF\(-\)perovskite multi-source energy harvesting block diagram.  
Figure 2: RF energy harvesting block diagram.  
energy transfer efficiency. It enables efficient power transmission from RF sources [23] and ensures energy balance across network components by allowing each node to function as a transmitter and relay [29]. Through MIMO technology, Figure 4, IoT devices can efficiently harvest energy from multiple RF sources concurrently, resulting in improved energy efficiency and harvesting capabilities [25]. MIMO's resilience to noise and jitter ensures consistent energy transmission, which is vital for various IoT applications [10]. Additionally, it enhances overall system performance by facilitating directional energy transmission through beam-forming, making it ideal for multi-sourced MIMO multi-band hybrid RF-perovskite photovoltaic energy harvesting subsystems, thereby improving IoT device power supply capabilities in smart cities [6; 29].  
Simultaneous wireless information and power transfer (SWIPT) technology ensures reliable and uninterrupted energy acquisition. To enhance the SWIPT system's energy efficiency, a complex physical layer technique considering data transmission rate and energy harvesting performance is crucial [30]. Two user grouping techniques and a dynamic power allocation solution were suggested for a multi-input, multi-output, non-orthogonal multiple access (MIMO-NOMA) system to optimise energy harvesting by including SWIPT. These techniques achieved maximum energy and information transmission using RF signals [31]. Multi-band technology enables system operation across various frequency bands, providing flexibility in capturing RF energy from diverse sources. This approach ensures that the IoT device can extract energy from various RF signals, optimise energy harvesting capacity, and enhance overall power output. The proposed design combines a PSCs array with RF energy harvesting subsystems, utilising abundant sunlight and RF waves from communication systems to build a multi-sourced MIMO hybrid system that efficiently powers IoT devices and supports future IoT paradigms.
## 4 Discussion
### Reliability of the Estimated GPP  
Eight extra GPP products were used in our study to validate the reliability of our results. As illustrated in Figure 9, the four schemes of NIRv-based GPP were nearly identical to seven of the eight products from the North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) presented in the boxplots according to the significance calculated by Kruskal-Wallis and Bonferroni-Dunn post-hoc tests, indicating their robustness. Specifically, the average value of bias was 0.075. The bias of dynamic GPP decreased by 0.002 compared to static GPP. We collated the eddy-covariance (EC) observed monthly GPP as the field observations to show the performance among the schemes. The geographical location of the three EC flux towers is shown in Figure S1, and the validation scatter plots are shown in Figure S2. The results of the reliability suggest that (i) the dynamic schemes had better performances than the static schemes, and (ii) our schemes were robust compared to a wide range of GPP products.  
Figure 8: The conceptual illustration of the impacts of land cover change (**a**) and the multisource products’ differences (**b**) on the estimation of GPP by NIRv (**c**) and their relative contribution (**d**).
# Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
###### Abstract  
This article copes with signal source localization by employing a receding horizon control approach with temporal logic planning in the light of a single mobile robot. First, a temporal logic planning approach is proposed such that the task requirements from the temporal logic specifications can be effectively dealt with based on the product automaton in an offline fashion. Second, in order to label the key nodes of the product automaton, a particle filter is utilized to predict the source positions as the key nodes. Third, on the basis of the product automaton, a receding horizon control approach with temporal logic planning is developed to produce the robot's trajectory that satisfies a given linear temporal logic specification. Finally, the effectiveness of the proposed control approach is illustrated for signal source localization.  
mobile robots; receding horizon control; linear temporal logic; obstacle avoidance +
Footnote †: journal: Applied sciences
## 3 Proposed Anonymization Method, Iterative _k_-Anonymous Clustering Method (I\(k\)-ACM)
### Iterative Anonymization Model  
In the WSN, there are \(n\) sinks, \(m\) sensors and \(m.(n-1)\) symmetric encryption keys. Each sensor shares different key with each sink. Sharing is performed by physically deploying of keys to the sensors and to the sinks during network set-up phase of WSN. Since, it is not known which sensor will be group head during the operation of WSN, all sensors share different key with each sink.  
\(l_{th}\) sensor shares key with \(p^{th}\) sink which is labelled as \(e_{p}^{l}\). \(i^{\textrm{th}}\) sink contains list of the keys of all sensors as \(e_{i}^{l},e_{i+1}^{l},...,e_{n-1}^{l}\) where \(0<l<m\).  
Each sensors store \(n-1\) keys. Practically, the number of multiple sinks may be between 2 and 5, sensors can easily store this number of keys in their limited memory. On the other side each sink contains the number of keys between 0 and \(m.(n-1)\) keys. Since sinks can have memories with higher sizes, these number of keys can be stored in sinks easily.  
In the literature, there exist various group head selection and group formation methods [11]. It is assumed that appropriate one used during formation of groups and selection of group head sensor. The keys are assumed to be physically distributed to the sinks and to the sensors before deployment.  
In the proposed method, anonymization is completed in \(n\) iterative steps as shown Figure 5 in each group head sensor. Assume that we operate on \(l^{th}\) sensor which is selected as a group head sensor during WSN operation. In the first step, by using only generalization operation, input data is \(k1\)-anonymized. In the second step, \(k1\)-anonymized data is \(k2\)-anonymized by encrypting the chosen data parts with \(e_{1}^{l}\). For each \(i^{\mbox{th}}\) step to \(n^{\mbox{th}}\) step, anonymization is done by encryption using key, \(e_{i-1}^{l}\). The output after \(n^{\mbox{th}}\) step is multicasted to all sinks.  
After the arrival of anonymous data to sink from \(l^{th}\) group head sensor, each sink decrypts the data with their keys. The resulting data after decryption actually has the level of privacy required for that sink. \(i^{th}\) sink can only decrypt the data which is encrypted after the \(i^{\mbox{th}}\) iterations; because it has the corresponding keys. Data parts encrypted by the keys, \(e_{1}^{l},e_{2}^{l},...,e_{i-2}^{l}\), cannot be decrypted, therefore they can be considered as suppression operations for that sink. \(1^{\mbox{st}}\) sink, which has to get data with lowest privacy criteria, can decrypt all the encrypted parts and the result data is actually \(k_{1}\)-anonymized. On the other hand, \(n^{\mbox{th}}\) sink has no key and gathers data as \(kn\)-anonymized.
## 4 Roundness Error Evaluation
### Particle Swarm Algorithm  
#### 4.2.1 Common Particle Swarm Algorithm  
Particle swarm optimization (PSO) [35] is an optimization algorithm based on swarm intelligence theory, which is essentially a random search algorithm developed by simulating the foraging behavior of birds. In the foraging behavior of the birds, each individual's foraging behavior seems to be random, but the individuals that have found food play a certain role in guiding the whole foraging population. The individual's current foraging state preserves relevant experience for subsequent foraging behavior, and the individual in the birds judges that it is constantly moving to the area around the nearest individual to find the area where food is most likely to be present.  
In the particle iteration process, let the position of the \(I\) particle in the d-dimensional solution space be expressed as \(X_{i}=\left(x_{i1},x_{i2},x_{i3}\cdots x_{id}\right)\), and its velocity is \(V_{i}=\left(v_{i1},v_{i2},v_{i3}\cdots v_{id}\right)\). The particle updates the velocity and position of the individual particle according to the individual optimal value \(p_{i}=\left(p_{i1},p_{i2},p_{i3}\cdots p_{id}\right)\) and the global or local optimal value \(p_{g}=\left(p_{g1},p_{g2},p_{g3}\cdots p_{gd}\right)\) through Equations (9) and (10):  
\[v_{id}^{t+1}=\omega v_{id}^{t}+c_{1}r_{1}\left(p_{id}^{t}-x_{id}^{t}\right)+c _{2}r_{2}\left(p_{gd}^{t}-x_{gd}^{t}\right) \tag{9}\]  
\[x_{id}^{t+1}=x_{id}^{t}+v_{id}^{t+1} \tag{10}\]  
where \(t\) is the number of iterations, \(\omega\) is the inertia weight, \(r_{1}\) and \(r_{2}\) are two independent random numbers uniformly distributed in [0,1] and \(c_{1}\) and \(c_{2}\) are acceleration factors, which are generally constants.
## 2 Materials and Methods
### CmAP Cloning, Expression and Purification  
The nucleotide sequence for the native _CmAP_ gene was submitted to GenBank (accession number: ABD92772.1). We optimised a 1608-bp DNA sequence (including the N-terminal signal peptide sequence) for better protein expression in _E. coli_. The whole optimised gene was synthesised and inserted into a cloning vector PUC57 between the Nde I and BamH I restriction enzyme cutting sites by Sangon Biotech. The PUC57-_CmAP_ and the expression vector pET16b were simultaneously digested using the restriction enzymes Nde I and BamH I. The digested product was ligated with T4 DNA ligase after agarose gel purification. Finally, the recombinant plasmid was transformed into _E. coli_ BL21 (DE3). The _CmAP_ sequence was confirmed by Sangon Biotech (Shanghai, China).  
The recombinant strains were cultured at 37 \({}^{\circ}\)C in 2 L of LB medium, with shaking at 180 rpm. When OD600 reached 0.8, a solution of IPTG was added into the culture to a final concentration of 0.5 mM. Then, the culture was allowed to grow overnight at 16 \({}^{\circ}\)C. The cells were harvested after centrifugation at 6000\(\times\)\(g\) for 10 min and suspended in buffer A (20 mM PBS, 0.05% NaN\({}_{3}\), and 0.5% Tween-20; pH 8.0). The suspended cells were lysed through ultrasonication and recentrifuged at 12,000\(\times\)\(g\) for 20 min to remove cell walls. The supernatant was loaded onto a High Q anion exchange column previously equilibrated with buffer A, and the enzyme was eluted under a concentration gradient of 0.1-1.0 M NaCl in buffer A. Afterwards, Phenyl HP chromatography, with a linear gradient of 1.0-0 M (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) in buffer A, was performed. The enzyme solution was finally concentrated and loaded onto a SephacrylTM S-300 gel filtration column, and then eluted using buffer B (20 mM PBS, 0.05% NaN\({}_{3}\), 0.5% Tween-20, and 0.15 M NaCl; pH 8.0) at 1 mL/min. The fractions with enzymatic activity were desalted through dialysis after each step of chromatography. The purity of the protein was evaluated using sodium dodecyl sulphate-polyacrylamideamide gel electrophoresis (SDS-PAGE), and the protein concentration was determined through the Bradford method by using BSA as the standard. The purified enzyme was concentrated and stored at 4 \({}^{\circ}\)C.
## 3 Hybrid Visual SLAM with Deep Keypoints, Depths, Poses, and Line Features
### Line-Point Bundle Adjustment and Online BoW in the Back End
#### 3.4.2 Binary Descriptors and Online-Learning-Based BoW  
Loop closure in SLAM systems is a critical function that identifies when a robot or autonomous vehicle revisits a previously explored area, allowing the system to correct accumulated localization errors. This module also enhances map consistency and improves overall system robustness. The loop closure module optimizes localization by leveraging the information of already mapped environments. This process is essential for maintaining the integrity and accuracy of the map, especially in dynamic or large-scale environments. The main component of loop closure is the bag-of-words (BoW) models. The BoW models can abstract scene information and obtain the global features of each scene. By comparing the euclidean distance of global features, we can find out the loop nodes in the sequence.  
We have developed a learned local feature extractor at the front end that produces floating-point descriptors with values ranging between \((-1,1)\). The existing visual SLAM loop closure modules, however, rely on offline BoW models designed for binary descriptors. Additionally, deep learning methods often face challenges with generalization; a BoW model trained offline may not perform well in novel environments. To address these issues, we employ an online-trained BoW model (iBoW) [39] for the loop closure module.  
As shown in [15], floating-point descriptors are difficult to organize into a tree-structured dictionary and thus are challenging to use in loop closure. Ref. [15] also indicates that the distribution of learned descriptor values is normally distributed. Therefore, a binary hashing transformation is applied to the learned descriptors. We binarize the descriptors by mapping vector values above zero to 1 and values equal to or below zero to 0. The transformed descriptors are 256-bit binary descriptors, with values being either 0 or 1. Subsequently, following the descriptor generation process of ORB, the binary descriptors are converted into integer descriptors. The conversion method involves transforming each 8-bit binary descriptor into a decimal integer, allowing 256 binary elements to be converted into 32 integer elements. Finally, the descriptors input into iBoW is 32-bit integer descriptors. The process is illustrated in Figure 4.
## 5 Physical Layer: Five Decades of Evolution (from 45 Mb/s to 100+ Tb/s)
### Physical Layer Evolution in Access  
The physical layer requirements for the access part of the network are relaxed compared with the backbone due to the limited distance. The fiber attenuation and dispersion are insignificant constraints here, as the distance of the access domain is typically smaller than 10 km. The key target of access is to increase the offered bit rate in order to support the ever-increasing bandwidth hungry services. For this purpose, various architectural designs can be projected, as shown in Figure 32, featuring different physical layer characteristics. For example, TDM-PON has a limited power budget compared with the active optical network (AON) due to the lack of optical amplification. This can limit the attainable number of users due to the lower number of splits. The power budget is a critical metric as it can define the maximum number of users, since after each power split, the number of users can be doubled, but the optical power is depleted by a factor of two at the splitter egress. The power budget can also determine the attainable transparent length. The attainable length and the number of users are antagonistic parameters due to their dependence on the power budget, and as a consequence they cannot be maximized concurrently. In particular, network engineers need to sacrifice reach when the number of users is large, e.g., in dense urban areas, or the number of serviced users, when the network topology is extended, e.g., in rural areas.  
The main physical layer parameters for the various PON standards are shown in Table 4. As is evident, the maximum reach has been increased over time, from 10 km from the first standard to 60 km from the XG(S)-PON, in an attempt to support more distant users. The maximum number of users is also increased from 32 (or 5 power splits) to up to 256 (or 8 power splits). This happens due to the increased power budget, which could be achieved with later standards such as XG(S)-PON. The attainable rate has also increased by more than 50 times in less than two decades, from 622 Mb/s with A/BPON to 40 Gb/s with NG-PON2. This is a result of the advances in the transceiver operational parameters, such as modulation format, operational wavelength, etc., as is also shown in Figures 29-31. Comparing Table 4 with Figures 29 and 31, we can observe that the attainable bit rate of each PON standard is significantly lower compared with the capability of the corresponding commercially available technology. This is attributed to the fact that state-of-the-art technology, e.g., PM-QPSK, is cost-ineffective for access applications, and as a consequence, direct detection technology is qualified for access.  
Figure 32: Architectural illustration of various access variants [53].
## 1 Introduction
In this way, DTLCL trained with labelled data of significant faults can be used to effectively predict unlabelled or poorly labelled micro-fault diagnostic data. Case studies of varying complexity have shown that the DTLCL method has advantages over any base model and other existing TL methods. The case studies also illustrate the relevance of the DTLCL method for wireless charging in a real environment with signal interference and noise. The summary of the contributions is as follows: a combined learning method with inheritance depth transfer based on CNN, LSTM and weighted tuning algorithms is proposed for the adaptive diagnosis of minor faults in rolling bearings of wireless chargers. This study makes the following contributions: (1) This method makes use of Deep Learning and Transfer Learning. Moreover, DNN autonomously extracts features from unprocessed vibration data in wireless charger manufacturing, which provides excellent flexibility without the need to manually convert and extract features; (2) MTL is used to prepare the source domain data to initialise the target domain model and give it a solid foundation; (3) A linear combination of Gaussian kernels of WV is used to create the MMD to better assess the differences between the source domain and the target domain; (4) Compare the case studies with Deep Learning without transfer and the existing traditional transfer learning. The effectiveness of the method was verified using actual measurement data. The training time is 19 s, and the accuracy exceeds 94.5%. The explanation results show that the proposed DTLCL method can be more accurate and solid than the current combination of integrated combinations and single models without transfer or transfer in identifying small errors. The rest of this paper is structured as follows. Section 2 introduces the basic theory of DL and TL. Section 3 explains the framework for deriving the DTLCL method. Section 4 explains the experiment and analyses the experimental results. Section 5 concludes and discusses limitations, possible applications and difficulties.