esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Conventional Channel Estimation Methods
### Pilot-Based Channel Estimation Method
#### 2.1.3 MMSE Channel Estimation  
The purpose of the MMSE algorithm is to minimize the mean square error between the actual value and the estimated value, and the cost function of the MMSE estimation result \(\hat{\mathbf{h}}_{\text{MMSE}}\) can be represented as:  
\[J\Big{(}\hat{\mathbf{h}}_{\text{MMSE}}\Big{)}=\text{E}\bigg{\{}\Big{(}\mathbf{ h}-\hat{\mathbf{h}}_{\text{MMSE}}\Big{)}\Big{(}\mathbf{h}-\hat{\mathbf{h}}_{ \text{MMSE}}\Big{)}^{\text{H}}\bigg{\}} \tag{6}\]  
in a similar vein, setting the partial derivative of \(J\Big{(}\hat{\mathbf{h}}_{\text{MMSE}}\Big{)}\) to zero, it can be concluded:  
\[\hat{\mathbf{h}}_{\text{MMSE}}=\mathbf{R}_{hy}\mathbf{R}_{yy}^{-1}\mathbf{y} \tag{7}\]  
where \(\mathbf{R}_{hy}\) represents the cross-correlation matrix between the actual channel response \(\mathbf{h}\) and the received signal \(\mathbf{y}\), and \(\mathbf{R}_{yy}\) is the autocorrelation matrix of \(\mathbf{y}\). \(\mathbf{R}_{hy}\) can be further expressed as:  
\[\mathbf{R}_{hy}=\text{E}\Big{\{}\mathbf{hy}^{\text{H}}\Big{\}}=\text{E} \Big{\{}\mathbf{h}(\mathbf{Xh}+\mathbf{w})^{\text{H}}\Big{\}}=\mathbf{R}_{hh} \mathbf{X}^{\text{H}} \tag{8}\]  
also, \(\mathbf{R}_{yy}\) can be further expressed as:  
\[\mathbf{R}_{yy}=\text{E}\Big{\{}\mathbf{y}\mathbf{y}^{\text{H}}\Big{\}}= \text{E}\Big{\{}(\mathbf{Xh}+\mathbf{w})(\mathbf{Xh}+\mathbf{w})^{\text{H}} \Big{\}}=\mathbf{X}\mathbf{R}_{hh}\mathbf{X}^{\text{H}}+\sigma^{2}\mathbf{I} \tag{9}\]  
where \(\mathbf{R}_{hh}\) is the autocorrelation matrix of the actual channel response and \(\sigma^{2}\) is the noise variance. Therefore, it can be concluded:  
\[\hat{\mathbf{h}}_{\text{MMSE}}=\mathbf{R}_{hh}\bigg{(}\mathbf{R}_{hh}+\sigma ^{2}\Big{(}\mathbf{X}\mathbf{X}^{\text{H}}\Big{)}^{-1}\bigg{)}^{-1}\hat{ \mathbf{h}}_{\text{LS}} \tag{10}\]  
Since the MMSE algorithm considers the noise impact and introduces the priori information \(\mathbf{R}_{hh}\) and \(\sigma^{2}\), its channel estimation accuracy is high. Under the same MSE accuracy condition, at least 10 dB of SNR gain can be obtained compared to the LS algorithm. However, the practical implementation of the MMSE algorithm is constrained due to challenges in acquiring prior knowledge and second-order statistical features in communication systems. Moreover, the need for multiple matrix inversions significantly amplifies the algorithm's complexity.
## 3 Methods
### Coherence of Differential Interferogram Analysis  
For a series of radar echoes, if there is similarity between their phase and amplitude, in the radar interference image, this similarity is manifested as interference fringes. Coherence is an index by which to measure the similarity between radar echoes, and the coherence coefficient is a measure of coherence. The coherence between two radar echo signals is \(\gamma\) and can be defined as follows [58]:  
\[\gamma=\frac{\mathrm{E}\Big{\{}\mathrm{Y}_{\mathrm{m}}\mathrm{Y}_{\mathrm{s}}^ {\ast}\Big{\}}}{\sqrt{\mathrm{E}\Big{\{}\mathrm{Y}_{\mathrm{m}}\big{|}^{2} \Big{\}}\mathrm{E}\Big{\{}\mathrm{|Y}_{\mathrm{s}}|^{2}\Big{\}}}} \tag{7}\]  
Based on this definition, the coherence of each pixel in the interferogram can be calculated. Coherence can usually be used as a measure of the effect of short-wavelength  
Figure 4: Schematic diagram of the geometric relationship between satellite incidence angle and observations of slope deformation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Image Features**} & \multicolumn{1}{c}{**Deformation Projected Down the Slope**} \\  & & **to the Line of Sight** \\ \hline \multirow{2}{*}{\(\alpha>0\): slope facing the satellite} & \(0<\alpha<\theta\): foreshortening & close to the satellite \\  & \(\alpha=\theta:\) foreshortening & cannot be measured \\  & \(\alpha>\theta:\)layer & away from the satellite \\ \hline \multirow{2}{*}{\(\alpha<0\): slope facing away from the satellite} & \(|\alpha|>(90-\theta):\)shadow (no signal) & cannot be measured \\  & \(|\alpha|=(90-\theta):\)ground range resolution & away from the satellite \\ \hline \hline \end{tabular}
\end{table}
Table 3: Categories of geometric relationship between satellite incidence angle and observations of slope deformation.  
band errors on measurement accuracy; the higher the coherence, the smaller the standard deviation of the interferometric phase and the lower the dispersion of the interferometric phase for the same number of views. When the coherence is lower and the standard deviation of the interferometric phase is larger, the uncertainty of the interferometric phase is greater. Therefore, coherence is an important metric for InSAR data, and higher coherence often means more reliable phase information. During repeated orbit observations, interferometric phase decoherence can be caused by changes in the antenna phase center during satellite flights and changes in the dielectric coefficient of surface objects [59]. Considering the coherence performance of the data, the multi-look factors of 10:2 and 4:3 are used to reduce the decoherence noise of Sentinel\(-\)1A and ALOS/PALSAR\(-\)2, respectively. In this paper, the average coherence of all interference pairs of two kinds of data is used to evaluate their accuracy.
# Vibration Control of Multi-Modular VLFS in Random Sea Based on Stiffness-Adjustable Connectors
## 3 Vibration Control Method Based on the Stiffness-Adjustable Connector
### Mathematical Model of the Floating System
#### 3.1.1 Hydrodynamic Model
\begin{array}{l}\mathbf{x}_{i}=\widetilde{\mathbf{x}}_{i}e^{-j\omega t}\\ \dot{\mathbf{x}}_{i}=-j\omega\widetilde{\mathbf{x}}_{i}e^{-j\omega t}\\ \ddot{\mathbf{x}}_{i}=-\omega^{2}\widetilde{\mathbf{x}}_{i}e^{-j\omega t} \end{array}  \tag{9}\]If we substitute Equation (9) into Equation (8), we arrive at  
\[\mathbf{f}_{i,w}=\overline{\mathbf{f}}_{i,w}e^{-j\omega t}-\sum_{q=1}^{N}\big{(} \mathbf{\Delta M}_{i,q}\widetilde{\mathbf{x}}_{q}+\mathbf{\Delta C}_{i,q} \dot{\mathbf{x}}_{q}\big{)} \tag{10}\]  
where  
\[ \begin{array}{l}\overline{\mathbf{f}}_{i,w}=-j\omega\rho\iint_{S_{i}}\left( \psi_{I}+\psi_{D}\right)\mathbf{n}_{i}\;\mathrm{d}s\\ \Delta\mathbf{M}_{i,q}=\rho\iint_{S_{i}}\mathrm{Re}\left(\psi_{q}\right) \mathbf{n}_{i}^{T}\;\mathrm{d}s\\ \Delta\mathbf{C}_{i,q}=\rho\omega\iint_{S_{i}}\mathrm{Im}\left(\psi_{q}\right) \mathbf{n}_{i}^{T}\;\mathrm{d}s\end{array}  \tag{11}\]  
In Equation (11), the first term is the incident wave force, the second term is the added mass, and the third one is the added damping.
# Demosaicing by Differentiable Deep Restoration
## 3 The Proposed Method
### Differentiable Deep Restoration  
Previous deep learning based methods for demosaicing often address it by direct regression. In comparison, we represent the result color image \(I\) as a linear combination of basis maps within a local window centered at every pixel, which can be expressed as  
\[I^{(i)}=B^{(i)\top}V^{(i)}. \tag{8}\]  
Here, \({}^{(i)}\) standards for the local window centered at the \(i\)-th pixel. The basis maps \(B^{(i)}\) are produced by a neural network, while the combination coefficients \(V^{(i)}\) are online optimized by minimizing the cost function in Equation (1). Thus, the demosaicing process works in a hybrid architecture. The network generates basis maps according to image context to regularize the solution, and the optimization solves the inverse problem to enforce the physically-based degradation model.  
Therefore, the coefficient for a pixel \(i\) is  
\[V^{(i)}=\operatorname*{arg\,min}_{X}\sum_{j\in\mathbb{N}(i)}\|H(j)B^{(i)\top}(j)X -M(j)\|^{2}+\lambda\|X\|^{2}, \tag{9}\]  
where \(\mathbb{N}(i)\) is the set of pixels in the \(n\times n\) local window centered at \(i\), and the coefficient \(\lambda\) is the factor of the regularization term. This minimization problem is efficiently solved by the Cholesky decomposition with closed-form solution. This closed-form solver is differentiable and can be embedded into the network without any difficulty. The regularization term here is to avoid instable results caused by perturbations in bases. It also prevents the numerical instability of matrix inverse in the optimization. In order to find appropriate settings of \(n\) and \(\lambda\), we fix basis number \(m\) as \(4\), and change \(n\) from \(\{3,5,7\}\) and \(\lambda\) from \(\{0.001,0.01,0.1\}\). Finally, we set \(n=5\) and \(\lambda=0.01\) empirically for our method.  
An example of some learned bases and corresponding coefficients is visualized in Figure 2. The bases for each local window is cropped from the full resolution basis maps generated by our basis generation network. The whole image can then be solved by processing these overlapping patches one by one and each patch determines the result of its center pixel.
# The Quirky Rot Fungi: Underexploited Potential for Soil Remediation and Rehabilitation
## 1 Soil Ecology, Health, and Erosion Control
### Slag Stabilization and Mining-Affected Soil
For example, fungal cell wallscontain compounds such as chitin and glucans, which provide cohesion and flexibility to the hyphae. Chitin contains negatively charged N-acetylglucosamineimine residues, which have a high affinity for numerous metal cations [43]. The production of siderophores, low-molecular-weight molecules with affinity for iron, has also been documented for rot fungi and may also play a role in the binding of potentially toxic elements. Arsenic (As) may co-precipitate or form complexes with iron in siderophores, thus reducing As mobility in soils and sediments and its leaching into aquatic matrices [44; 45]. In addition, white-rot and brown-rot fungi may also display the ability to transform metals into less toxic forms through biochemical processes, such as precipitation or crystallization through oxalate secretion. This seems to be an efficient process for dealing with divalent cations such as copper (Cu), cadmium (Cd), and lead (Pb) [45; 46; 47]. For example, [PERSON] et al. [46] found that Pb removal from aqueous suspensions by the white-rot fungus _P. ostreatus_ reached a rate of approximately 90%. The authors concluded that this was mainly achieved through fungal cell functional group (carboxyl, hydroxyl, and amine groups) interactions with the cations, and the fungal cells were able to actively transport the cations to their interior and store them in vacuoles [46]. It is also worth highlighting that the resilience of decomposer fungi in these environments may be due to the fact that some metals are considered essential to their metabolism. For example, Cu is used as a cofactor in laccase enzymes [47], just as zinc (Zn) can enter the secondary metabolic pathways of the fungus, such as the structural stabilization and catalytic activity of enzymes with lower expression, such as dehydrogenases [46]. Saprophytic fungi can enrich mining waste, improve soil with nutrients, break down recalcitrant materials, and contribute to their structure by extending the hyphal network (Figure 2). The lack of studies on the stabilization or remediation of slag or waste is practically nonexistent; however, the limited evidence collected in other contexts allows us to hypothesize that the use of rot fungi in this context warrants further research. Studies are crucial to assess the long-term sustainability and economic viability of these approaches, because the effectiveness of fungal-based remediation strategies depends on several factors, including substrate characteristics, the selection of fungal species, environmental conditions, and microbial interactions, which urgently need to be addressed. Careful consideration of these factors, along with comprehensive monitoring and assessment, is essential to optimize remediation strategies and achieve the successful restoration of mining sites.
## 1 Introduction
For less massive radiant systems some works have been recently published looking at their behaviour in dynamic conditions, but the analyses mainly looked at the radiant system, without taking into account the room. In particular, the influence of geometrical and construction parameters on the thermal performance of floor heating systems has been widely investigated, focusing mainly on wet systems, in which the pipes are embedded in a concrete layer. The pipe material, diameter and spacing, along with the thickness and material of the covering layer were studied, finding that the pipe material has negligible effect on the amount of useful heat [14], while the conductivity and thickness of the finishing layer have a high influence [14; 15]. In the case of wood flooring, and also the installation method (floating or adhesive covering) was investigated [16]. While the pipe spacing and the mean value of the watertemperature have large impacts on the surface temperature and heat transfer of the radiant floor, the thickness of the screed was found to have almost no influence [17]. Dry floor systems have been investigated less than wet systems. Experimental and numerical analysis of a lightweight system with aluminum foil covering a non-profiled insulation board were carried out by [PERSON] et al. [18], while the dynamic performance of a dry system with pipes laid in a profiled insulation board was investigated by [PERSON] et al. [19]. [PERSON] and [PERSON] [20] compared wet and dry floor systems based on numerical simulations, finding higher mean values, but lower uniformity, of the surface temperature of the dry system. [PERSON] et al. [21] developed a numerical model for the study of the steady-state and dynamic operation of a new light floor heating construction made of wood planks and aluminum diffuser, focusing also in this case only on the thermal behaviour of the emitter itself and stressing the importance of developing a model including also the building to fully investigate the performance of the radiant system and perform an energy comparison with traditional heating floor systems and other emission devices. The present work hence looks at the overall balance of a room taking into account the thermal inertia of the radiant system as well as the water inside the pipes, as better described in detail hereafter. Usually for solving this problem dynamic simulations have to be run in order to properly take into account the dynamic behaviour of building structures, as well as transient operation of water in the embedded pipes. The heat conduction through the radiant systems can be modelled in different ways. The most widely used methods are the simplified RC-model [22] and the response factors technique [23]. Both models have been demonstrated to be accurate against measurements.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 2 Smart Ships and Navigation Technology
### Navigation Technology
#### 2.2.1 Perception Technology
(2023) proposed an integrated low-visibility enhancement network (called AiOENet) that uses a unified encoder-decoder network architecture to flexibly recover low-visibility images, reducing the impact of harsh conditions on navigational safety [24]. [PERSON] et al. (2023) constructed a foggy navigation database using a differentiated deep learning architecture and an EfficientNet neural network and a new method for foggy images and perceived visibility, which by replacing the SE module and combining the convergent block-attention module and the focal point loss function identified the best enhancement algorithm depending on the fog [25]. [PERSON] et al. (2024) proposed an inshorse ship real-time detection transformer (ISRt-detr) that combines multiple coordinated attention and contrast learning to enhance the ability to perceive ships at night [26]. Detection of small targets at sea is an important area of ship navigation perception. AIS and radar systems may have the problem of missed detection; therefore, [PERSON] et al. (2024) introduced an SSIM-based region of interest search method and an ellipse-weighted fusion method, proposed the ship panoramic vision stitching algorithm SSIM-EW, and proposed a perceptual model (YOLOv8-SGW) for sea-surface target detection, which improves the intelligence level of ships [16]. [PERSON] et al. (2022) established relevant datasets of different complex sea states and introduced the improved Faster R-CNN for target detection in small sample datasets, which solved the problem of multiple complex sea-state data being difficult to collect simultaneously [27]. Traditional multi-feature detectors can only handle three or fewer features. [PERSON] et al. (2020) proposed a multi-feature detector based on the Isolated Forest (iForest) algorithm in order to detect small floating targets in sea clutter, which breaks through the limitation of the number of features and reduces the cost of computation [28]. The flow of ship perception is shown in Figure 4:
## 2 Materials and Methods
### The Experimental Set-up for Dielectric Constant Measurements
Figure 10: The dielectric sample was prepared from a bulk sample thin slice (**a**), and silver paint was applied to each face to serve as electrodes (**b**). Figure 9: Connection configuration when using an LCR meter as a capacitance bridge to test the capacitance of a device under test (DUT). With expected capacitance values for the LiNbO\({}_{3}\) samples in hand, measurements can be made using the LCR meter. Electrical connections are established with the samples by inserting them between conductive plates that are wired directly to the LCR meter terminals. Measuring the capacitance with the LiNbO\({}_{3}\) sample between the plates establishes the material capacitance, \(C_{m}\). To measure the open-cell capacitance, \(C_{0}\), the LiNbO\({}_{3}\) sample is removed, and the plates are held apart by the thickness of the sample with the same area of overlap. These two measurements cannot be immediately used for calculating the dielectric constant. They must first be corrected for the open circuit capacitance of the LCR meter. Separating the two conductive plates by a sufficient distance results in a small capacitance measurement that acts as the relative zero of the system. To correct the material and open-cell measurements, this open circuit value simply needs to be subtracted from both measurements. Following these steps, the necessary capacitance values for both LiNbO\({}_{3}\) samples were recorded and the dielectric constants calculated as shown in Table 4.  
As shown above, in Table 4, the process of calculating the dielectric constant from the measured capacitances is accurate but not precise. The LCR meter used is expected to provide measurements with \(\pm\)0.3% accuracy, and thus it is likely that the discrepancy is a result of human error. Precision in calculating the dielectric constant is dependent on precisely measuring the open-cell capacitance, which is strongly proportional to the separation of the conductive plates. A small difference between open-cell separation and the DUT thickness can significantly affect the final dielectric constant calculation. With this limitation in mind, it stands to reason that the calculated dielectric constant values still serve to describe the relative magnitude and not precise measurements. For dielectric characterization of the PMN-PT sample, the previous experimental setup is modified such that the measurement cell is contained within a Peltier heating stage. Recording the capacitance values at various temperatures allows for an observation of the effect of temperature on the dielectric constant of the material. The Peltier heat stage used for this experimentation is capable of maintaining temperatures between 0 \({}^{\circ}\)C and 120 \({}^{\circ}\)C without needing chilled water so that capacitance values will be measured over that range in 5 \({}^{\circ}\)C increments.
## 1 Introduction
For blushless AC machine where sinusoidal back-electromotive force (back-EMF) is desired, stator is required to generate as high main harmonics as possible and as low sub-harmonics as possible in MMF distribution regardless of rotor type. It is clearly summarized in form of winding factor [16; 17; 18]. On the other hand, rotor design determines the performances such as d, q-axis inductances and no-load flux linkage corresponding to shape of cavities, PM volume, and configuration of PM. Especially, raw material cost is concentrated on rotor in which PM is contained. Thus, design of rotor has more effect on improvements in torque ripple, cost and efficiency. Literatures [19; 20; 21] investigated the trends of performances with respect to various rotor types at given stators: distributed winding and concentrated winding each. This paper investigates the characteristic trends when gradually changing rotor design of V-shaped IPMSM, which has the moderate performances in copper loss, torque ripple, cost, and efficiency [20]. Above all, it is envisaged in basis of lumped parameter model (LPM). [PERSON] suggests the analysis of synchronous reluctance machine (SynRM) using d and q-axis magnetic equivalent circuit (MEC) as LPM [22]. Furthermore, [PERSON] suggests the optimization process of three-phase IPMSM with two layered ferrite PM using LPM and differential evolutionary strategy (DES) in his dissertation [23]. In 2014, [PERSON] designed five-phase PM assisted SynRM (PMaSynRM) with rare earth PM using the optimization process [24; 25]. Afterwards, [PERSON] designed and compared five-phase PMaSynRM whose external rotor is constructed with single layered cavity to three-phase PMaSynRM [26; 27]. The optimization with LPM and DES has been implemented and used many times for PMaSynRM and IPMSM with ferrite PM, however, not for V-shaped IPMSM especially with large amount of rare earth PM. Since [PERSON]'s LPMs on d, q-axis MEC are considered for multi-layered SynRM without any magnet, it does not consider the existence of rare earth PM strong enough to saturate the cores at initial state (no-load state) [22; 28]. This initial saturation would lower the accuracy of LPM. Therefore, the deductions based on LPM must be validated in comparison with the results from finite element analysis (FEA) and performance test on a fabricated motor. Therefore, this paper investigates the characteristic trends according to rotor design in the following process. First, the cavities in rotor are V-shaped because V-shaped cavity is superior to flat or spoke type in torque ripple, copper loss and cost according to the literature [19; 21]. Single layered IPMSM will be designed with five different shapes of cavity which gradually grow the included angle in V-shape. Moreover, Section 2 introduces d, q-axis MEC constructed in LPM and envisages how parameters will be affected with respect to each case of the shapes. In Section 3, FEA results will be represented to see the agreement between LPM and FEA. Finally, experimental results will be present.