esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Comprehensive Sensing System Calibration
#### 2.3.1 PM Calibration  
As presented in Figure 2, the unit magnetic moment \(\mathbf{m}\) can be described by (\(\varphi\), \(\theta\)) in spherical coordinates:  
\[\mathbf{m}=\left[\begin{array}{cc}\sin\varphi\cos\theta&\sin\varphi\sin \theta&\cos\varphi\\ \end{array}\right]^{\mathrm{T}}, \tag{5}\]  
The deviation of the unit dipole moment \(\mathbf{m}\) in the PM local frame, such as the \(x_{mv}\)-\(y_{mv}\)-\(z_{mv}\) coordinates for the vertical magnet PM\({}_{v}\) illustrated in Figure 2, can be described with alignment angle \(\varphi_{m}\) and azimuth angle \(\theta_{m}\). Since the PM arrangement is optional, it is reasonable to assume that the alignment angle \(\varphi_{m}\) is fixed, but the azimuth angle \(\theta_{m}\) is essentially arbitrary. The on-axis magnetic field \(\mathbf{B}_{\theta}\) can be derived from Equation (1):  
Figure 3: Design of the adaptive Levenberg–Marquardt algorithm (aLMA).  
\[\mathbf{B}_{\theta}=\frac{\mu_{0}m}{4\pi d^{3}}\left[\begin{array}{c}-\sin\varphi_ {m}\cos\theta_{m}\\ -\sin\varphi_{m}\sin\theta_{m}\\ 2\cos\varphi_{m}\end{array}\right], \tag{6}\]  
where \(d\) is the distance from the observation point to the center of the dipole along its geometric axis.  
For small \(\varphi_{m}\), it is proper to make a reasonable approximation that \(\sin\varphi_{m}\approx\varphi_{m}\) and \(\cos\varphi_{m}\approx 1\). Then, the magnitude \(m\) and the deviation (\(\varphi_{m}\), \(\theta_{m}\)) can be determined from Equation (7):  
\[\left\{\begin{array}{c}m\approx\frac{2\pi d^{3}}{\mu_{0}}|B_{z}|\\ \varphi_{m}\approx\frac{2}{|B_{z}|}\sqrt{\left(B_{x}^{2}+B_{y}^{2}\right)}\\ \theta_{m}=\tan^{-1}\left(\frac{B_{y}}{B_{x}}\right)\end{array}\right., \tag{7}\]
## 4 PicknPack Practice
### Potential Applications  
Although the protocols and techniques for M2M communications may have overlapped application scenarios, they each have their specific fitting fields, such as ad hoc wireless sensor network, lightweight devices networking, P2P messaging over the internet, continuous data streaming transfer, and real-time collaborative operations. The industrial applications are characterised by high-performance computers with heterogeneous software platforms, which require real-time collaborative operations. Therefore, the described methods are designed for the practical manufacturing applications, which have the following features:  
* Powerful machines with reliable connection
* High reliability in connectivity and messaging
* Ubiquitous messaging, either P2P or one-to-multiple
* High-throughput transmission and concurrent messages
* Awareness of the states of peer machines
* Real-time messaging for machine collaborations
* Highly dynamic and adaptive functionalities
* Decentralised machine network structure.  
According to its features, the proposed CoM2M is an appropriate candidate for communication intensive smart machine integrated manufacturing applications, where sensors and actuators are applied to collect product data and perform automated production. In addition to the case study of PickNPack food manufacturing, it can find applications typically in mechanical manufacturing, pharmaceutical production, daily chemical goods production, aviation industry, and so forth, where machine parts for manufacturing are computerised and interactive machine collaborations are essential to the system.
## 3 Methods
### Network and User Processing
#### 3.2.3 Temporal Constraints on Phase Biases  
As presented in the introduction, contradicting results have been published regarding the time stability of phase biases. With a Kalman filter implementation of the UDUC observations, different models assuming different time stability of the phase biases are easy to implement and compare. In this study, two different methods to estimate satellite and receiver phase biases both at the network and user levels are considered:  
1. Phase Biases are estimated as constant parameters (constant phase biases);
2. An epoch-wise reference phase clock is introduced. All other phase biases are assumed as constant parameters relative to it (reference phase clock).  
Note that, in all cases, the GPS L5 satellite phase biases are assumed to vary in time with a process noise of \(1\)\(\frac{\text{cycle}}{\text{h}}\), due to the variation reported in [11].  
To obtain one reference phase clock for each satellite and receiver, as opposed to one for each phase bias, a reparameterization is introduced. Let \(x=\begin{bmatrix}\delta_{1},\delta_{2},\dots,\delta_{n}\end{bmatrix}\) be the phase biases of one satellite/receiver and \(A=\begin{bmatrix}1&0\\ e_{n-1}&-\text{I}_{n-1}\end{bmatrix}\in\mathbb{R}^{n\times n}\), where \(e_{n-1}=\begin{bmatrix}1&1&\cdots&1\end{bmatrix}^{T}\in\mathbb{R}^{n-1}\) and \(\text{I}_{n-1}\) is the identity matrix of the specified dimension. With \(x^{*}=A\cdot x=\begin{bmatrix}\delta_{1},\delta_{1}-\delta_{2},\dots,\delta_{1 }-\delta_{n}\end{bmatrix}^{T}\), we obtain a reparameterization of \(x\) containing the reference phase bias \(\delta_{1}\) and all other phase biases differenced to it. The reference phase bias shall be estimated as an epoch-wise parameter, i.e., act as a phase clock, and the differenced phase biases as constant parameters. Then the process noise matrix of \(x^{*}\) is given with \(S_{x^{*}}=\text{diag}(\sigma_{E},0,0,\dots,0)\). When no reparameterization shall be performed, an equivalent process noise matrix of \(x\) can be found by application of error propagation with \(A\cdot S_{x^{*}}\cdot A^{T}=\sigma\cdot J\). Note that \(A^{-1}=A\) and \(J\) denotes an all-ones square matrix.  
Network and user results are generated by the application of both approaches. The results can then be compared in one consistent implementation to evaluate the performance of both approaches.
# On-Chip Reconstructive Spectrometer Based on Parallel Cascaded Micro-Ring Resonators
## 3 Simulation Results and Analysis
### Spectral Reconstruction Simulation Results
As the number of sampling states increases, the spectrometer can better capture the subtle differences between the peaks, allowing for clearer separation and resolution of the individual spectral features. The programmable nature of the silicon photonic filter is a key strength of our design, providing users with the flexibility to optimize the performance of the spectral chip based on their specific needs. By adjusting the number of sampling states, users can prioritize either detection speed or reconstruction accuracy, depending on the requirements of their application. This adaptability makes our on-chip spectrometer design adaptable to a wide range of spectroscopic applications, from rapid screening to high-precision measurements. Figure 8: (**a**) The calculated average relative error \(\varepsilon\) on test set and the resolution of reconstructed spectrum using the spectrometer designs with different sampling state numbers. Simulated reconstruction results with 729 sampling states for (**b**) narrow double-peak spectra separated by 0.1 nm and 0.2 nm, (**c**) previously selected narrow spectrum, and (**d**) the measured ASE spectrum.
## 2 Reconstruction Algorithm
### The Strain Reconstruction Algorithm
The objective function \(f=\sum_{j=1}^{s}\left[R_{\alpha l,j}(\lambda_{j})-R_{large,j}(\lambda_{j}) \right]^{2}\) can be calculated in terms of the simulation spectrum \(R_{cal,j}(\lambda_{j})\) and the real-world experimental spectrum \(R_{large,j}(\lambda_{j})\), where \(\lambda_{j}\) represents the \(i_{th}\) Bragg wavelength and \(S\) presents the total number of segments. When the appropriate optimization value \(f\) is similar to the required error accuracy value \(\varepsilon_{0}\), a global optimal solution is obtained. The grey wolf optimizer algorithm can, therefore, serve as a global method for the optimization problem that involves an optimum estimation for objective error function in terms of the difference between the simulation spectrum and the experimental reflection spectrum response. For each iteration, it automatically updates the search path at different regions in order to find  
Figure 2: Flow chart of strain reconstruction algorithm of FBGs. the minimum error of \(\varepsilon_{0}\). The unknown strain distribution can then be obtained as the simulation strain distribution when determining the most optimal solution. Step 3: Experimental data verification  
Based on previously published research, and the specific optimization methods detailed above, a method of simulation verification can now be adopted [18]. In this study, the strain reconstruction algorithm obtained using the GWO method was verified by experimental strain data obtained by the DIC method. The simulation spectrum was calculated by the modified transfer-matrix algorithm based on the uniform strain of each grating segment, and the experimental spectrum was obtained using fiber Bragg grating sensor demodulation. Finally, the accuracy of the reconstructed strain of FBGs obtained by the algorithm was verified by the real experimental strain obtained by the DIC method.
###### Abstract  
Spatial analysis of hydrological data often requires the interpolation of a variable from point samples. Commonly used methods for solving this problem include Inverse Distance Weighting (IDW) and Kriging (KG). IDW is easily extensible, has a competitive computational cost with respect to KG, hence it is usually preferred for this task. This paper proposes the optimization of finding the IDW parameter using a nature-inspired metaheuristic, namely Particle Swarm Optimization (PSO). The performance of the improved algorithm is evaluated in a complex scenario and benchmarked against the KG algorithm for 51 precipitation series from the Dobrogea region (Romania). Apart from facilitating the process of applying IDW, the PSO implementation for Optimizing IDW (OIDW) is computationally lighter than the traditional IDW approach. Compared to Kriging, OIDW is straightforward to be implemented and does not require the difficult process of identification of the most appropriate variogram for the given data.  
inverse distance weighting; kriging; particle swarm optimization; prediction error; spatial interpolation 2020 10.254; doi:10.3390/app10062054
## 2 Mooring Area Detection of Single Anchored MASS
### Method of Traditional Ship Anchorage Area Detection  
In traditional methods, usually, the radar or electronic chart equipment is applied to select the anchor position, and a circle is selected with a certain radius without anchoring ship and water surface obstacles in areas with appropriate water depth and seabed sediment. The circle detected by this method meets the requirements, namely, the appropriate anchorage circle. The radius is the safety anchorage circle radius, and the center of the circle is the anchor position.  
Figure 2: Diagram of plane motion of the anchoring ship. (**a**) Motion diagram of single anchored ship. (**b**) Ship motion trajectory of single anchored ship within 12 h. (**c**) Ship motion trajectory of single anchored ship within 24 h. (**d**) Ship motion trajectory of single anchored ship within 5 days. (**e**) Ship motion trajectory of single anchored ship within 10 days. (**f**) Ship motion trajectory of single anchored ship within 15 days.  
Figure 4 provides the diagram of ship anchor position selection, in which the blue dot represents the obstacle. In Figure 4a,b, the anchorage circle 1 has no interference from other obstacles within the anchorage circle radius, and the point \(O\) can be used as the dropping anchor position of the anchoring ship; the anchorage circle 2 is impacted by obstacles within the anchorage circle radius, which is not suitable for the anchorage circle of anchoring ship. In Figure 4b, there is an influence of wind flow, and the center point \(O\) of anchorage circle 1 can be used as a suitable dropping anchor position. However, due to the influence of wind flow in the process of anchoring, there will be a process of moving to the direction of force, which leads to a new anchor position \(O^{\prime}\). There are two obstacles in the new anchorage circle, which present potential risk in anchoring. Therefore, the influence of wind flow should be fully considered when selecting the anchor position. In navigation practice, there are few scientific intelligent detection methods of anchorage area; therefore, ships usually choose a larger anchorage circle radius to ensure the anchoring safety, which objectively causes the waste of the anchorage area resource. In addition, some spaces in the anchorage area could be regarded as the anchorage area if the detection capacity is qualified for it.
# PnV: An Efficient Parallel Consensus Protocol Integrating Proof and Voting
## 3 Node and Data Model  
PnV is a consensus protocol specifically designed for consortium blockchains. This protocol effectively classifies network nodes into five distinct roles, thereby segregating bookkeeping rights from voting rights in an efficient manner. More precisely, designated bookkeepers possess exclusive privileges to generate blocks while ensuring that each block undergoes meticulous verification by voters before its ultimate commitment in the blockchain.
# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
###### Abstract
In this paper, a 16-element mmWave front end at both the transmitter and receiver is presented to assess the real-time beam-based AoA performance with downlink 5G PRS transmissions. To the best of the authors' knowledge, this is the first work on a real-time 5G mmWave PRS testbed with reduced antenna arrays assessing downlink AoA performance outdoors.
## 2 Simulation of Plasma Sheath Fluid Model  
The study of low-frequency communication issues in hypersonic vehicle essentially involves a qualitative analysis of the characteristics of the plasma sheath. Therefore, the thickness, the concentration of the plasma sheath (related to the flight altitude of the vehicle), and the location of the plasma sheath on the vehicle should be the key factors to consider. Due to the restrictions of the shock layer, the vehicle does not generate a very thick plasma sheath, so thickness is not the key factor we need to consider. However, the flight  
Figure 1: Flow chart of EM characteristic analysis of the hypersonic vehicle’s plasma sheath. (**a**) Geometric sketch of the RAM-C II model and its high-speed fluid analysis. (**b**) The RAM-C II generates plasma sheath and is irradiated by the HPM. (**c**) Analysis of transmission and reflection characteristics of the plasma sheath.  
altitude of the vehicle has a significant impact on the plasma concentration [26]. When the hypersonic vehicle is flying above 70 km altitude, the air density reduces the collision frequency between the gas elements and increases the motion time of the air molecules. These processes cause thermodynamic energy change processes such as advection, rotation, vibration, and electron excitation of the gas molecules to lag behind their respective equilibrium states; therefore, they must undergo a thermodynamic non-equilibrium process. As the altitude decreases, e.g., below 70 km, the collision frequency between the gases increases because of the increased density of the air; the motion time of the air molecules decreases, and all the internal energy modes of the gas elements are in equilibrium with the advection modes of the heavy particles, which are in a state of thermodynamic equilibrium [27; 28; 29]. The EM properties of plasma sheath in thermodynamic equilibrium and chemical non-equilibrium have been studied and discussed in ref. [20]. As a consequence, when the plasma sheath is in thermodynamic and chemical non-equilibrium, this work focuses on the EM characteristics after the contact between the plasma sheath and the HPM.  
Regarding the consideration of plasma concentration at different positions of the vehicle, according to the principle of plasma sheath formation, the plasma concentration is highest in the head region of the vehicle and lowest in the tail region. This article mainly focuses on studying the area with the highest plasma concentration on the vehicle. If low-frequency communication is significantly improved in the area with the highest plasma concentration, it will also have a good effect in other areas of the vehicle.
## 3 Results and Discussion  
### Characterization of Microcapsules  
The SEM images of the DVB-wax microcapsules were shown in Figure 3a. The synthesized microcapsule had a good degree of sphericity and the sizes were evenly distributed. The average particle size was determined to be ~150 \(\upmu\)m. To further verify the morphology of the DVB-wax microcapsules and surface property, an area was randomly chosen and viewed in higher resolution, as shown in Figure 3b. It is obvious that the microcapsule is a sphere with a smooth surface and no damage or defect. According to the solvent-resistivity and temperature-resistivity tests of DVB-wax microcapsules, no breakage or leakage of wax were observed under lab microscope. It suggests that the microcapsules are solvent-resistive and temperature-resistive. These all provide a solid basis for the further use of DVB-wax microcapsules as synthetic materials in the reaction afterwards.  
The DSC tests were conducted for synthesized microcapsules with a different DVB to wax ratio. Microcapsules with the DVB/wax ratio of 2.5:1 could be synthesized with maximum wax encapsulation and no wax leakage. The heat of fusion for pure wax was 147.6 J/g and the counterpart for microcapsules with a DVB/wax ratio of 2.5:1 is 33.8 J/g. The actual value was lower than the theoretical one (42.2 J/g), because some waxes were flushed away together with the washing of PVP and PVA. Another reason was due to the incomplete encapsulation of wax by the microcapsules. It is also found that the phase change temperature of microcapsules was lower than pure wax. It could be attributed to the high latent heat or low heat transfer rate of wax in microcapsules.  
Figure 2: (**a**) Experimental scheme of volatile organic compound (VOC) adsorption with a temperature determination system; (**b**) Image of self-designed adsorption column inserted with thermocouples.
## 5 Conclusions  
The studies in the literature examining seismic potential with the finite element method have not yet revealed the effect of element types used in the model's discretization on the model and fault surface slip deficit. In order to fill this gap in the literature, the Main Marmara Fault, located in the Marmara region of the North Anatolian Fault, where an earthquake of magnitude seven or higher is expected, was modeled. Three-dimensional geometry was used in the modeling study. To obtain the 3D geometry, the area with left corner coordinates latitude: 26\({}^{\circ}\).10, longitude: 39\({}^{\circ}\).20, and upper right coordinates latitude: 30\({}^{\circ}\).90, longitude: 41\({}^{\circ}\).75 was created. The fault geometry used in the [PERSON] [39] study within this area was used. Three-dimensional geometry is considered as three vertical layers: upper, middle, and lower crust, and topography is added. Material parameters were obtained from the Litho 1.0 model, and the friction coefficient along the fault surfaces was entered as 0.05. The annual velocities of the 18 GNSS stations obtained in the [PERSON] [42] study were manipulated as displacements in the software. Supports were applied to the surfaces of the parts north of the Main Marmara Fault and from the bottom of the model. The created model was solved using different mesh methods in ANSYS software. According to the RMSE values calculated through comparing the GNSS test stations with the values obtained from the model, the _tetrahedron_ method obtained the best results with 1.53 and 1.09, and the _multizone-prism_ method obtained the worst results with 1.65 and 1.12 east and west RMSE components, respectively.  
As a result of the comparison between the best and worst models on the fault surfaces, the slip deficits at the GF and IF-CiS intersection, which have large slip deficit values, were compared. As a result of this comparison, GF slip deficit values are the same, but the IF-CiS intersection has a 0.5 mm difference. For the _multizone-prism_ mesh method, the annual slip deficit has been calculated to be less than the _tetrahedron_ method.  
Conceptualization, M.F.K.; methodology, M.F.K.; software, M.F.K.; validation, M.F.K.; formal analysis, M.F.K.; investigation, M.F.K.; resources, M.F.K.; data curation, M.F.K.; writing--original draft preparation, M.F.K.; writing--review and editing, M.F.K. and V.E.G.; visualization, M.F.K.; supervision, V.E.G.; project administration, M.F.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Gain and Pain in Graph Partitioning: Finding Accurate Communities in Complex Networks +
## 3 Evaluation of Accuracy
### Normalized Mutual Information (NMI)  
Whereas Normalized Mutual Information (NMI) (We have additionally utilized the _Adjusted Rand Index_ (ARI) [53], a recognized metric for community evaluation. However, as the results were consistent with those obtained using NMI, we have opted not to include them in order to maintain conciseness in the paper.), as defined in [54], stands as a widely acknowledged metric for assessing the similarity between clusters, it is particularly effective in measuring the agreement between optimal communities and those discovered by an algorithm. Consider a network \(G\) with \(n\) nodes, where \(\mathcal{C}(\mathcal{A})=C_{1},\ldots,C_{k}\) represents the communities obtained by algorithm \(\mathcal{A}\), and \(\mathcal{C}^{\prime}=C^{\prime}_{1},\ldots,C^{\prime}_{k^{\prime}}\) denotes the ground truth communities. The NMI value corresponding to algorithm \(\mathcal{A}\) can be computed as follows:  
\[NMI(\mathcal{A})=\frac{-2\sum\limits_{x=1}^{|\mathcal{C}|}\sum\limits_{y=1}^{| \mathcal{C}^{\prime}|}|\frac{|C_{x}\cap C^{\prime}_{y}|}{n}log(\frac{n|C_{x} \cap C^{\prime}_{y}|}{|C_{x}||C^{\prime}_{y}|})}{\sum\limits_{x=1}^{|\mathcal{ C}|}\frac{|C_{x}|}{n}log(\frac{|C_{x}|}{n})+\sum\limits_{y=1}^{|\mathcal{C}^{ \prime}|}\frac{|C^{\prime}_{y}|}{n}log(\frac{|C^{\prime}_{y}|}{n})}. \tag{13}\]  
When the identified communities precisely match the ground truth, NMI achieves its maximum value of one. Conversely, if there is no resemblance between the two sets, the NMI score drops to zero. Generally, a larger NMI value signifies a more precise and effective unveiling of community structures.