esa-sceva/satcom-chunk-collection huggingface dataset:
# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 2 System Overview  
The method proposed in this paper is mainly improved based on the VINS-Mono system. The designed system block diagram is shown in Figure 1, which is mainly divided into measurement preprocessing, local sliding window optimization and closed-loop detection. The initialization process adopts the same strategy as that of VINS-Mono. Based on the loose coupling strategy of visual information IMU information, the pose of all frames in the sliding window and the inverse depth of 3D points are estimated by using the pure visual structure from motion (SFM), and finally aligned with the result of IMU pre-integration. The initialization parameters are solved.  
For feature extraction and tracking, one must firstly extract the Shi-Tomasi [23] feature points from the input images, and then match and track feature points based on the optical flow method. For line features, the proposed adaptive threshold line segment extraction algorithm is used to extract line segments, LBD [24] descriptors are calculated, and the KNN [25] algorithm is used to match the distance between descriptor and line segment angle. This process is described in detail in Section 3 of this article.  
After system initialization, the point-line feature results are sent to the sliding window optimization section, and the sliding window optimization will pre-integrate IMU data. The nonlinear estimator based on the sliding window model can construct the joint optimization function according to the point-line constraints, IMU constraints and loopback constraints, and solve the position, velocity, rotation and bias of all frames in the sliding window. The detailed contents will be introduced in the third and fourth chapters of this paper.  
Figure 1: The system of PLI-VINS.  
In the loop detection part, we follow the strategy of VINS-Mono. Firstly, whether to insert key frames is determined according to the parallax between the two frames. If a key frame is inserted, loop detection is performed through the DBoW [26] word bag model and BRIEF [27] descriptor. If there is a loopback, the relocation process is used to maintain alignment between the current the sliding window and the poses map of the past time, and all the poses of the loopback is taken as a constant, and all the IMU measurements, local visual measurements and corresponding feature values extracted from the loopback are used to optimize the sliding window, so as to reduce the cumulative error and calculation amount of the system. However, visual inertia information can provide roll angle and pitch angle data, so there are only four degrees-of-freedom (DOF) errors (triaxial position error and heading angle error). The consistency of the global trajectory can be guaranteed only by adding key frames to the bitmap and optimizing its 4 DOF:
# Study on Heat Transfer Performance and Anti-Fouling Mechanism of Ternary Ni-W-P Coating
## 1 Introduction
Due to the ions, Ca\({}^{2+}\) and [CO\({}_{3}\)]\({}^{2-}\) seem to be easily combined. In a real environment, dissolved metal cations in CaCO\({}_{3}\) solution also affect the growth of CaCO\({}_{3}\), resulting in irregular nucleation during its crystal formation and inadequacy connecting of valence bonds. Thereupon, because of the unsaturation of valence bond, its electrostatic attraction, active centers and surface energy are all large, thus, it has a greater attraction to the surrounding ions. However, the nucleation energy of calcite, aragonite, and vaterite, which is a critical problem of industrial efficiency, remains elusive and is a field of intense research. Based on this, we specially built a fouling environment. Ca\({}^{2+}\), as well as [CO\({}_{3}\)]\({}^{2-}\), were induced to deposit on the surface of substrate and coating. The object of this project is to study the forming mechanism of calcium carbonate fouling and heat transfer performance on Ni-W-P electroless coating. Comparison between the heat transfer performance of a substrate with and without the Ni-W-P coating after fouling experiment was also investigated. Moreover, we give a theoretical simulation on the reaction path, transition state, and energy gradient change of calcite, aragonite, and vaterite to figure out that why calcite, aragonite, and vaterite deposit on the different surfaces.
# Optimized Unconventional Geometric Gates in Superconducting Circuits
## Appendix A Derivation of the General Method  
We give the specific steps of Equation (4)'s derivation in this appendix. We know that the Schrodinger equation is \(i\frac{\partial}{\partial t}U=H\cdot U\). Suppose the Hilbert space is spanned by \(\{|\tilde{\psi_{1}}\rangle,...,|\tilde{\psi_{L}}\rangle\}\). Then, the evolution operator is written as \(U=\sum_{i=1}^{L}|\psi_{i}(t)\rangle\,\langle\tilde{\psi_{i}}(0)|\) with  
Figure 7: **(Top)** Gate fidelity of the nontrivial controlled phase gate as a function of parameters \(\Delta_{1}\) and \(\beta\) with \(\eta=2\). **(Bottom)** The state population and state-fidelity dynamics with initial state \((|01\rangle+|11\rangle)/\sqrt{2}\).  
\(\ket{\psi_{i}(t)}\) being the solution of the Schrodinger equation. Substituting the evolution operator into the Schrodinger equation gives  
\[H=i\sum_{i=1}^{L}\ket{\psi_{i}}\bra{\psi_{i}}; \tag{10}\]  
we next substitute Equations (1) and (3) into Equation (10) and we have  
\[H =i\sum_{i=1}^{L}\left[i(1+\eta)\bra{\tilde{\psi}_{i}}id_{t}\ket{ \tilde{\psi}_{i}}\ket{\tilde{\psi}_{i}}\bra{\tilde{\psi}_{i}}+\ket{\tilde{\psi}_ {i}}\bra{\tilde{\psi}_{i}}\right] \tag{11}\] \[=i\sum_{i=1}^{L}\left[-(1+\eta)\bra{\tilde{\psi}_{i}}\ket{\tilde{ \psi}_{i}}\ket{\tilde{\psi}_{i}}+\sum_{l=1}^{L}\bra{\tilde{\psi}_{l}}\ket{\tilde {\psi}_{i}}\ket{\tilde{\psi}_{l}}\bra{\tilde{\psi}_{i}}\right]\] \[=i\sum_{i=1}^{L}\left[-(1+\eta)\bra{\tilde{\psi}_{i}}\ket{\tilde{ \psi}_{i}}\ket{\tilde{\psi}_{i}}+\sum_{k=i}^{L}\bra{\tilde{\psi}_{k}}\ket{ \tilde{\psi}_{i}}\ket{\tilde{\psi}_{k}}\bra{\tilde{\psi}_{i}}+\sum_{i\
eq l}^{ L}\bra{\tilde{\psi}_{l}}\ket{\tilde{\psi}_{l}}\bra{\tilde{\psi}_{i}}\right]\] \[=i\sum_{i\
eq l}^{L}\bra{\tilde{\psi}_{l}}\ket{\tilde{\psi}_{i}} \ket{\tilde{\psi}_{l}}\bra{\tilde{\psi}_{i}}-i\eta\sum_{i=1}^{L}\bra{\tilde{ \psi}_{i}}\ket{\tilde{\psi}_{i}}\ket{\tilde{\psi}_{i}}\bra{\tilde{\psi}_{i}}\]  
In the derivation process, we did not make any constraints on the auxiliary state \(\ket{\tilde{\psi}_{i}}\) and the space dimension. Therefore, the formula Equation (11) gives the general Hamiltonian for constructing control gates with any number of qubits. At the same time, this Hamiltonian is not only applicable to quantum computing and quantum information but also to general condensed matter problems. For example, the auxiliary state can be taken as the wave function of the phonon.
## 4 Materials and Methods
### Cytotoxic Activity
#### 4.3.3 Molecular Dynamics Simulations and Free Energies of Binding  
Amber 2022 [45] was employed for molecular dynamics (MD) simulations as previously described elsewhere [36]. Briefly, all analyzed complexes underwent the same energy minimization, heating, equilibration, and production runs protocol. Proteins were parameterized with the ff19 SB force field, and parameters for compound **17** were obtained with the gaff2 force field. Parameters for the NADP cofactor present in the bifunctional DHFR-TS enzyme were retrieved from the Amber parameter database maintained by the Bryce Group at The University of Manchester ([[http://amber.manchester.ac.uk/index.html](http://amber.manchester.ac.uk/index.html)]([http://amber.manchester.ac.uk/index.html](http://amber.manchester.ac.uk/index.html)), accessed on 16 February 2023). The Zn\({}^{2+}\) cations present in the HDAC and PPD proteins were parameterized using the cationic dummy atom (CADA) method [46,47]. The receptor-compound **17** complexes were enclosed in truncated octahedron boxes, solvated with water molecules of type OPC, and neutralized by adding Na\({}^{+}\) and Cl\({}^{-}\) countertions at a concentration of 150 mM. A first step of energy minimization was then performed with everything except the solvent and counterions constrained. This was followed by a second energy minimization step with no constraints applied. Afterward, the complexes were gradually heated from 0 K to 300 K at constant volume for 20 ps. The heated systems were next equilibrated for 100 ps at constant temperature (300 K) and pressure (1 bar). The equilibrated systems were the starting point for the production runs. Five different short (4 ns) production runs were carried out per complex. Each production run was initialized with random initial atomic velocities for a better exploration of each complex's conformational space.  
Free energies of binding were computed from the MD production trajectories with the MM-PBSA method as implemented in the MMPBSA.py script [48] provided with Amber 2022. Calculations took place over 100 MD snapshots extracted from every production run and covering the 1-4 ns time interval. That is, 20 MD snapshots were extracted from each production trajectory for MM-PBSA calculations. The ionic strength for estimating the free energies of binding was set to 150 mM, and default implicit solvent parameters were employed.
## 1 Introduction  
The complexity of the carbon cycle arises from combining various carbon reservoirs in the Earth system (e.g., atmospheric carbon dioxide (CO\({}_{2}\)) in the atmosphere, soil organic carbon and vegetation), including fluxes and feedbacks which regulate the dynamics of these reservoirs [1]. Photosynthetic and respiration processes regulate the CO\({}_{2}\) net flux at the surface, affecting atmospheric CO\({}_{2}\) concentrations, surface energy balance and the hydrological cycle [2]. Mainly, Africa plays an essential role in the carbon cycle in the 21 st century as it has large areas of moist tropical forest, seasonal and semi-arid woodland, savannah, grassland and desert.  
The performance of the offline land surface or regional coupled regional climate-vegetation models (for simulating LAI and GPP) has been evaluated over Africa (e.g., [3]). For instance, ref [4] showed that a coupled climate-dynamic vegetation model overestimates both GPP and LAI concerning the Machine Tree Ensemble (MTE; [5]).  
Ref. [6] examined the role of LAI parameterization in controlling the surface energy balance and climate of Africa with RegCM4 coupled with Community Land Model version 4.5 (CLM45), including a module of carbon-nitrogen cycling (CN) (RegCM4-CLM45-CN).  
They reported that when the modified LAI formula is used, the model performance is improved relative to the original LAI formula, and it shows a similar performance relative to the static vegetation case. Additionally, the LAI parameterization notably influences the total evapotranspiration and surface energy balance budgets. This study aims to examine the role of LAI parameterization in simulating the terrestrial carbon fluxes in Africa using a regional coupled climate-vegetation model (RegCM4-CLM45-CN). Section 2 describes the study area, data and experiment design; Section 3 discusses the results of simulations over the African domain. Finally, Section 4 shows the summary and conclusion.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 3 Proposed Methods
### Spoofing Interference Direct Location Based on Carrier Phase Single-Difference
\[\mathbf{\delta}\mathbf{p}=\left(\mathbf{H}^{T}\mathbf{H}\right)^{-1}\mathbf{H}^{T}\mathbf{b} \tag{19}\]  
Upon obtaining \(\mathbf{\delta}\mathbf{p}\), the solution of Equation (9) can be updated from \(\mathbf{p_{t,k-1}}\) to \(\mathbf{p_{t,k}}\), denoted specifically as \(\mathbf{p_{t,k}}=\mathbf{p_{t,k-1}}+\mathbf{\delta}\mathbf{p}\). To enhance the location accuracy of the method, we can perform multiple phase carrier phase measurements at the same observation point and use the weighted least squares method described in reference [35] to further reduce the impact of measurement errors on the effectiveness of the method. If \(\sigma_{ji}\) represents the standard deviation obtained from multiple measurements of \(u_{ji}\), the corresponding weight assigned to \(u_{ji}\) is as follows. \[w_{j,i}=\frac{1}{\sigma_{j,i}} \tag{20}\]  
Add weights to Equation (12), which can be rewritten as:  
\[\mathbf{WH\delta}\mathbf{p}=\mathbf{Wb} \tag{21}\]in which \(\mathbf{W}\) is given as follows. \[\mathbf{W}= \begin{bmatrix}w_{1,1}&&\\ &w_{1,2}&\\ &&\ldots&\\ &&&w_{M,6}\end{bmatrix}  \tag{22}\]  
For Equation (20), we directly apply the least squares solution Equation (18) of Equation (11) to obtain the weighted least squares solution of Equation (20) as follows. \[\mathbf{\delta p}=\left(\mathbf{H}^{T}\mathbf{W}^{T}\mathbf{W}\mathbf{H}\right)^{-1}\mathbf{H}^{T}\mathbf{ W}^{T}\mathbf{W}\mathbf{b} \tag{23}\]  
In this paper, the spoofing interference location is solved iteratively using Equation (23).
## 1 Introduction
Monitoring is often intermittent, and data must be periodically downloaded and analyzed [28]. A literature review reveals a lack of studies on the detection of developing cracks, especially in the nozzles of internal combustion engine injectors, during the operating phase. It was found that, although research has been performed on a wide range of methods, only a selection of studies are mostly focused on the cracking of injector nozzles of marine engines. Furthermore, the use of neural network classification for the detection of such damage has not been encountered in the literature. An attempt to fill these gaps in the existing diagnostic studies of internal combustion engine components of assemblies is undertaken in this article. This research presents the problems related to the identification of parameters for the detection of the growth of fatigue cracks in an injector nozzle of a medium-speed marine diesel engine using signal analysis methods in the decimation, windowed, time, amplitude, frequency, time-frequency domain, and wavelet transforms. The developing damage led to a critical failure that ended the operation of the internal combustion engine, which was detected in many domains of signal analysis. This article offers a methodology for planning an experiment to select the parameters for the real-time monitoring of fatigue crack initiation and propagation, which is very important for the safe operation of transportation means. This allowed us to obtain many diagnostic symptoms of vibration signals in various domains, from which the most useful and new ones were selected against the background of existing ones. This contribution consists of finding the most useful symptoms and determining the lower and upper limits that separate the state of a developing crack in an injector nozzle.
## 6 Numerical Results
### Verification of the Analytical Model
However, in the scenario under consideration, the case is different, as storing (and duplicating) one additional item in the \(n_{access}\) AMEN caches means removing \(n_{access}\) VoD items from the MCEN cache and having them consequently delivered, when requested, from the origin server. Moreover, we highlight that the value of \(k\), i.e., the number of items stored in AMEN caches, which guarantees the minimum amount of network resource occupation, significantly differs from one case to another. This is to be expected, as it strictly depends on the available amount of storage capacity, the number of caches deployed, the content catalog size and the popularity distribution. In particular, we focus our comparison on cases 1 and 3 that only differ in the skew parameter of the popularity distribution (for Case #1, \(\alpha=0.9\), while for Case #3 \(\alpha=1.1\)). The value of \(k\) which guarantees minimum resource occupation is 267 for Case #1, whereas it is 377 for Case #3. This shows the significant impact the popularity distribution has on the optimal cache deployment. We further investigate the impact of \(\alpha\) on the optimal cache deployment in Section 6.3. In Table 3 we compare the simulative and analytical results with those of the analytical model. We denote by \(k_{sim}\) the value of \(k\) that guarantees the optimal storage distribution found through simulations, and by \(k_{model}\) the value calculated utilizing the closed-form formula. With a negligible percentage error, the results prove the correctness of the closed-form formula.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 3 Data Collection and Methodology
### Message Collection Structure
#### 3.2.2 Data Collection Process  
The Tx Data Collector, linked to the Cosmos Hub and Osmosis clients, seeks and obtains the most recent blockchain data from the associated nodes via a synchronization technique during the data collection. The block data contains information about the inter-blockchain communication transactions generated by the transaction generation controller, such as their commitment status and timestamp. The Cosmos Hub client functions in the same way as other normal network nodes. The existing Osmosis client's RPC receiving port waits for incoming requests on the same TCP port 26657 as the Cosmos Hub. The Osmosis client on the Measurement Node utilizes TCP port 26757 to avoid port duplication within the same node.  
Figure 5: Database design for measuring message delivery time.  
The Tx Data Collector, linked to the Cosmos Hub and Osmosis clients, queries transaction data via the RPC commands given by each client, namely 'gaiad' and 'osmosiod.' The Gaiad command 'gaiad query txs -height [height] -events' [eventType].[eventAttribute]=[value]? [flag]' [17] can be used to obtain transactions included in a block. Osmosis uses the same command structure as Gaiad, but substitutes 'gaiad' with 'osmosiod' and adds the '-node= \"tcp://127.0.0.1:26757\"\" parameter to specify the receiving RPC port number for querying transactions [18]. The inter-blockchain communication (IBC) transaction created by the IBC Tx Generation Controller is the transaction data used in the measurement and analysis. It queries the IBC Tx Generation Controller's transactions by giving the block height at the beginning of the transaction generation process and adding the transaction event types maintained by each client. Table 2 shows the event types that were employed in data gathering.  
Figure 7 depicts the process of gathering committed transaction data from Cosmos Hub and Osmosis clients using RPC instructions. Only one-way transfers of fungible tokens from the Cosmos Hub to Osmosis are undertaken in this study. As a result, the sorts of transactions to be collected from each client are predetermined. The inter-blockchain communication transactions generated and committed by the IBC Tx Generation Controller are saved as IBC Transfer Tx, IBC Recv Tx, and IBC Ack Tx data throughout the data collection process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Key** & **Value** \\ \hline \multirow{6}{*}{message} & \multirow{6}{*}{action} & /ibc.applications.transfer.v1.MsgTransfer \\ \cline{3-3}  & & /ibc.core.channel.v1.MsgRecvPacket \\ \cline{3-3}  & & /ibc.core.channel.v1.MsgAcknowledgement \\ \cline{2-3}  & & sender & cosmos1q5 prlm6 riru5 cpxajmugts6 gyrhnd5 luzshg4w \\ \hline fungible\_token\_ & & \\ \cline{1-1} \cline{2-3}   packet & receiver & osmo1 exmtwm3 vt6 sk2 plmhg4 xjhjd8 at2 scdg6 tww \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cosmos IBC transactions.  
Figure 6: Control process for transaction creation.
## 3 Materials and Methods
### BeePIV
#### 3.3.2 Difference Smoothing  
A difference frame \(D_{t,\Delta}\) may contain not only bee motions detected in the corresponding contextualized frame \(F_{t,\Delta}\) but also bee motions from the other frames in the context \(K_{t,\Delta}\) or motions caused by flying bees' shadows or occasional blurriness. In BeePIV, smoothing is applied to \(D_{t,\Delta}\) to replace each pixel with a local average of the neighboring pixels. Insomuch as the neighboring pixels measure the same hidden variable, averaging reduces the impact of bee shadows and blurriness without necessarily biasing the measurement, which results in more accurate frame intensity values. An important objective of difference smoothing is to concentrate intensity energy in those areas of \(D_{l,\Delta}\) that represent actual bee motions in \(F_{l,\Delta}\).  
The smoothing operator, \(H(D_{t,\Delta},r,c)\), is defined in Equation (9), where \(\alpha\) is a real positive number and \(\omega\) is a weighting function assigning relative importance to each neighborhood position. In the current implementation of BeePIV, \(\alpha=8\). We will use the notation \(S_{t,\Delta}\) to denote the smoothed difference frame obtained from \(D_{t,\Delta}\) so that \(S_{t,\Delta}(r,c)=H(D_{t,\Delta},r,c)\). We will use the notation \(H(D_{t,\Delta})=S_{t,\Delta}\) as a shorthand for the application of \(H(D_{t,\Delta},r,c)\) to every position \((r,c)\) in \(D_{t,\Delta}\) to obtain \(S_{t,\Delta}\). Figure 5 shows several smoothed difference frames obtained from the corresponding difference frames where the weights are assigned using the weight function \(\omega(k,l)\) in Equation (10).  
\[H(D_{t,\Delta},r,c)= \frac{1}{\alpha}\sum_{k=r-1}^{r+1}\sum_{l=c-1}^{c+1}\omega(k,l)D_ {t,\Delta}(k,l) \tag{9}\] \[\omega(k,l)=\begin{cases}1&\text{if }(k=r\wedge l\in\{c-1,c+1\}) \vee(l=c\wedge k\in\{r-1,r+1\});\\ 4&\text{if }k=r\wedge l=c;\\ 0&\text{otherwise}.\end{cases} \tag{10}\]  
Let \(V=(F_{t},\ldots,F_{t+k})\) and let \(D^{*}_{\Delta}(V)=(D^{V}_{t,\Delta},\ldots,D^{V}_{t+k,\Delta})\). The video smoothing operator \(H^{*}(V)\) applies the smoothing operator \(H\) to every frame in \(D^{*}_{\Delta}(V)\) and re turns a sequence of smoothed difference frames \(S^{V}_{t,\Delta}\), as defined in Equation (11), where \(Z=(D^{V}_{t,\Delta},\dots,D^{V}_{t+k,\Delta})\) is a sequence of difference frames.  
\[H^{*}_{\Delta}(Z)=H^{*}_{\Delta}\Big{(}\Big{(}D^{V}_{t,\Delta},\dots,D^{V}_{t+ k,\Delta}\Big{)}\Big{)}=\Big{(}S^{V}_{t,\Delta},\dots,S^{V}_{t+k,\Delta}\Big{)} \tag{11}\]