esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Foundations
### Non-Uniform Dual-Rate Extended Kalman Filter (NUDREKF)
#### 3.2.1 Nonuniform, Multirate, Sampled-Data System Modeling
\begin{bmatrix}A_{SR}&B_{SR}(T)\cdot\big{(}I-(\Delta^{u})_{k}^{ T}\big{)}&0&B_{SR}(T)\cdot(\Delta^{u})_{k}^{T}\\ 0&I-(\Delta^{u})_{k}^{T}&0&(\Delta^{u})_{k}^{T}\\ (\Delta^{y})_{k}^{T}\cdot C_{SR}&0&I-(\Delta^{u})_{k}^{T}&(\Delta^{y})_{k}^{T} \cdot D_{SR}^{T}\cdot(\Delta^{u})_{k}^{T}\\ 0&0&I\\ (\Delta^{y})_{k}^{T}\cdot C_{SR}&(\Delta^{y})_{k}^{T}\cdot D_{SR}\cdot\big{(} I-(\Delta^{u})_{k}^{T}\big{)}&I-(\Delta^{u})_{k}^{T}&(\Delta^{y})_{k}^{T} \cdot D_{SR}\cdot(\Delta^{u})_{k}^{T}\end{bmatrix}\begin{bmatrix}x\\ v^{u}\\ v^{u}\\ u\end{bmatrix} _{k}^{T} \tag{9}\] \[\text{where, now,}\]\[ \begin{split}(A_{MR})_{k}^{T}&=\begin{bmatrix}A_{SR}&B_{SR}(T) \cdot\left(I-(\Delta^{u})_{k}^{T}\right)&0\\ 0&I-(\Delta^{u})_{k}^{T}&0\\ (\Delta^{y})_{k}^{T}\cdot C_{SR}&0&I-(\Delta^{y})_{k}^{T}\end{bmatrix}\\ (B_{MR})_{k}^{T}&=\begin{bmatrix}B_{SR}(T)\cdot\left(\Delta^{u} \right)_{k}^{T}\\ (\Delta^{u})_{k}^{T}\end{bmatrix}\\ (\Delta^{y})_{k}^{T}\cdot D_{SR}\end{bmatrix}\\ (C_{MR})_{k}^{T}&=\begin{bmatrix}(\Delta^{y})_{k}^{T}\cdot C_{SR}&(\Delta^{y} )_{k}^{T}\cdot D_{SR}\cdot\left(I-(\Delta^{u})_{k}^{T}\right)&I-(\Delta^{y})_{k }^{T}\end{bmatrix}\\ (D_{MR})_{k}^{T}&=\begin{bmatrix}\Delta^{y}_{k}\cdot D_{SR}\cdot \Delta^{u}_{k}\end{bmatrix}\end{split}  \tag{10}\]  
The proposed NUDREKF-based control structure considers nonuniform, dual-rate, sampled-data system outputs and fast-rate inputs to the plant. Then, from the previous formulation, we will have  
\[ \begin{bmatrix}x\\ \
u^{y}\end{bmatrix} _{k+1}^{T}= \begin{bmatrix}A_{SR}&0\\ (\Delta^{y})_{k}^{T}\cdot C_{SR}&I-(\Delta^{y})_{k}^{T}\end{bmatrix}\begin{bmatrix} x\\ \
u^{y}\end{bmatrix} _{k}^{T}+ \begin{bmatrix}B_{SR}(T)\\ (\Delta^{y})_{k}^{T}\cdot D_{SR}\end{bmatrix} u_{k}^{T} \tag{11}\]  
\[y_{k}^{T}= \begin{bmatrix}(\Delta^{y})_{k}^{T}\cdot C_{SR}&I-(\Delta^{y})_{k}^{T }\end{bmatrix}\begin{bmatrix}x\\ \
u^{y}\end{bmatrix} _{k}^{T}+ \begin{bmatrix}(\Delta^{y})_{k}^{T}\cdot D_{SR} \end{bmatrix} u_{k}^{T} \tag{12}\]  
being  
\[ \begin{split}(A_{MR})_{k}^{T}&=\begin{bmatrix}A_{SR}&0\\ (\Delta^{y})_{k}^{T}\cdot C_{SR}&I-(\Delta^{y})_{k}^{T}\end{bmatrix}\\ (B_{MR})_{k}^{T}&=\begin{bmatrix}B_{SR}(T)\\ (\Delta^{y})_{k}^{T}\cdot D_{SR}\end{bmatrix}\\ (C_{MR})_{k}^{T}&=\begin{bmatrix}(\Delta^{y})_{k}^{T}\cdot C_{SR}&I-(\Delta^{y} )_{k}^{T}\end{bmatrix}\\ (D_{MR})_{k}^{T}&=\begin{bmatrix}(\Delta^{y})_{k}^{T}\cdot D_{SR} \end{bmatrix}\end{split}  \tag{13}\]
## 3 QSAR Today
### Data Matrix Setup: Preprocessing Data into a Uniform Matrix  
Before building a QSAR model, we must setup a data matrix. The QSAR data matrix consists of rows and columns, where each row represents a compound containing independent (X) variables--descriptors of the compound--and dependent (Y) variables--measured biological activities or properties. Most QSAR models have only one Y variable, but it is possible to model several Y variables simultaneously.  
Data preprocessing includes tasks such as smoothing, normalization, aggregation, data reduction, sampling when the dataset is too large, noise elimination, feature selection (see Section 3.3), data cleaning, data integration, and discretization. The result should be a uniform matrix, meaning a matrix with the same number of descriptors in each row, which will be used to build a QSAR model.
## 8 The Effect of Alloying Elements on Electrical Steel
### Effect of Silicon-Iron (Si Fe)  
The most-used materials in electrical machines are iron alloys with a certain silicon content. These materials are available in two states: a Grain-Oriented (GO) state, which exhibits anisotropic properties with different permeabilities in different directions, and a non-oriented state, wherein magnetic properties are isotropic in all directions [75]. The addition of silicon to iron makes mechanical processing more challenging and increases electrical resistance. Sadly, this comes at a cost, as there is a slight decrease in saturation magnetization along with reduced permeability [76]. Similarly, aluminium also increases electrical resistance and further reduces a material's permeability.  
A higher manganese content in an alloy results in larger grains and, consequently, increased permeability. However, this also leads to higher losses [77].  
Currently, the focus in relation to Si Fe steels is primarily on reducing iron losses, and this is driven by the need to address higher base frequencies due to increased machine speed and the presence of increased harmonics. These trends aim to improve the overall efficiency and performance of electrical machines.  
High-Silicon-Content Non-Oriented Si Fe  
Silicon-containing layers were developed to increase the electrical resistance of a material and thus reduce the eddy current. At present, the silicon content of these slats reaches 6.5%. However, in addition to reducing the maximum flux density and permeability of high-SiFe material, the silicon content makes alloys brittle and harder, resulting lead to increased manufacturing processes and costs and a need for more complex and expensive tools [78]
## 4 Evaluation of the Platform and Discussion
### Energy Augmented Reality System
#### 4.2.1 Image Targets Selection  
In any AR system, it is very important to obtain proper image targets that later have to be recognized, and on which virtual information related to them is going to be overlapped. One of the most influential parameters is light. If an image target is obtained under unfavorable lighting conditions, the target detection and tracking can be significantly affected. Thus, it is important to make sure that there is enough light in our room so that the scene details are well visible in the camera view. Figure 8 shows the proportional relation between the rating assigned to the microwave image target when it is taken under different light conditions. As can be observed, when the luminosity is about 40%, the system detects fewer features (only 1 star rating is assigned) than when the luminosity is about 90% (3 stars are assigned).  
It is important to highlight that two more attributes that must be taken into account to achieve a high rating are (1) the images have to be rich in details and (2) the fact that they should not contain repetitive patterns.
## 5 Conclusions  
This paper presented a new leak detection method primarily developed to monitor plastic service pipes in typical water distribution networks. The method is based on the computation of the kurtosis of the normalized autocorrelation of vibro-acoustic signals. The method was assayed in a test bed by measuring vibration and pressure signals induced by artificially generated leaks. The experimental results confirmed its effectiveness when monitoring the longitudinal vibrations of the pipe wall. The main potential benefits of the proposed method are summarized as follows:  
* The method exhibits a high sensitivity to leaks in the range of interest, hence appearing appropriate for early leak detection.
* Reasonably, it should be possible to define a universal threshold associated with the onset of leaks, hence simplifying the initial setup as no calibration would be required.
* The method should be sufficiently insensitive to variations in the amplitude of the measured signals, thus the method is unaffected by the local boundary conditions of pipelines.
* The method appears suitable for the implementation of an automated leak detection algorithm.
* Further improvements of the method's sensitivity seem achievable through proper optimization of the acquisition length.  
In light of the expected advantages, the implementation of a leak detection system based on the proposed method appears promising.  
Further experiments will be conducted by performing measurements of real leaks occurring in the service pipes of a water distribution network, to pursue two main goals. The first goal is to further assess the robustness of the method in real operating conditions. The second goal is to define an optimal filter to possibly enhance the reliability of the method with radial vibration measurements, hence overcoming its current main limitation.  
**Author Contributions:** Conceptualization, A.M., M.T. and A.R.; methodology, A.M.; validation, A.M. and M.T.; formal analysis, A.M.; investigation, A.M. and M.T.; resources, A.R.; data curation, A.M.; writing--original draft preparation, A.M.; writing--review and editing, M.T. and A.R.; supervision, A.R.; project administration, M.T.; funding acquisition, A.R.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
## 1 Introduction  
At present, root canal therapy is the most effective treatment for pulpal and periapical diseases. The keys to the process include thoroughly cleaning the root canal system, removing the source of infection, and reducing the damage to the root tip and periapical tissue [1, 2]. Thus, root canal therapy requires accurate measurement of the length of root canals [3]. Three approaches exist for measurement of root canal length: the hand feeling method, radiographic determination [4], and the use of an electronic apex locator (EAL). The EAL, which is most commonly used for measuring root canal length in clinical settings, has been developed over a long period using different methods [5].  
Root canal measurement was initially based on a resistance model, according to the phenomenon that the direct current resistance between the apical foramen and oral mucosa is almost constant, although the ages, tooth types, and root canal shapes of patients differ widely. The performance of this model has generally been considered unsatisfactory, as the measurement does not reflect a simple resistance model but, rather, a complex model with capacitor characteristics [6, 7]. Then, root canal measurement was carried out by a voltage gradient method, which was more accurate than before but still unstable [8, 9]. A method based on dual-frequency impedance ratios was proposed in the 1990s. This method uses the relative quantity instead of absolute quantity to make the results universal and reduce the effect of themeasuring environment on the results [10]. Several EALs using the dual-frequency impedance ratios method have demonstrated exceptional clinical performance [11; 12]. The multifrequency impedance method has been employed, based on the success of dual-frequency methods [13]; however, the EALs found on the market using the multifrequency impedance method cannot measure the length of root canal very precisely [14; 15]. The reason for such low accuracy is that the measurement environment in the root canal is extremely complex with infective substances and infected biological tissue. Deep learning methods have superior ability to cope with challenging environmental noises, so it was considered appropriate to apply a neural network for canal root length measurement. Data augmentation is also employed, as the data are limited by the scarce signals at confined frequencies, which prevents the ability to train an excellent neural network.  
Given the inadequacy of the current methodologies, this paper proposes a method for precisely locating the position of the apical foramen using a multifrequency impedance method based on a neural network, which is trained (after data augmentation) by distinct combinations of multifrequency signals generated from the designed circuit system. The method is experimentally verified, demonstrating that the proposed approach can promote accuracy and stability when measuring the length of root canals.
# Estimating the Thermal Conductivity of Unsaturated Sand
## 1 Introduction
The PTC model was used for the estimation of thermal conductivity of soil in the numerical analysis. [PERSON] et al. [28] evaluated the application of a novel ground heat exchanger through numerical modeling to solve transient heat transfer, and the thermal conductivity of the mixed backfill materials was obtained by the PTC model. [PERSON] et al. [29] proposed a numerical model of a vertical ground heat exchanger with the finite-volume method to evaluate the effects of thermal conductivity, volumetric heat capacity, temperature, and soil porosity, where the thermal conductivity of backfill materials was expressed as the PTC model. A mathematical model was developed to analyze the influence of unsaturated soil properties and groundwater flow on the performance of ground source heat pump systems by [PERSON] et al. [30], and the thermal conductivity of ground can be represented by the PTC model which was expressed as the sum of the thermal conductivity of each phase according to their volume fractions. According to the above studies, the PTC model is still widely used in numerical analysis because it can reasonably describe the physical and heat conduction properties of soil. However, the prediction of the PTC model is not the most accurate. The reason for this was that the value calculated by the PTC model is based on the assumption that the components are superposed to form a multiphase mixture, but the form of contact between solid particles is not taken into consideration which has a significant effect on soil thermal conductivity [11; 25; 26]. Thus, the values of the calculation by the PTC model which presented the upper bound of the thermal conductivity of the soil generally overestimated the data [15]. It is necessary to develop a more accurate and comprehensive model of soil thermal conductivity based on the PTC model. The aim of this study was to develop a modified model based on the PTC model to calculate the thermal conductivity \(\lambda\) of sand over a wide range of moisture contents from dry to saturated. The calculation steps of the modified model are presented. Based on the results, the performance of the modified model was validated by comparison with published datasets for a wide range of sand types. Finally, the modified model was used in the numerical analysis of the temperature field simulation, and the simulated and measured values were in good agreement.
# A Discriminative-Based Geometric Deep Learning Model for Cross Domain Recommender Systems
## 2 Review of Related Works
### Data Representation in the Non-Euclidean Domain  
Functions or problems defined based on the non-Euclidean domain can be further subdivided into sub-classes [11; 25; 26]. Firstly, we can consider situations consisting of multiple domains, such as identifying similarities and correspondences among items/user recommendations with computer vision and graphics applications. Functions defined in these settings are similar to CNNs based on the spatial domain and are more appropriate. Secondly, situations with fixed domains could also considered. An example could be the identification and prediction of global positioning of persons belonging to a social network based on their previous behaviors. In this example, the domain, which is the social network, is considered to be fixed. Functions defined in the domain are similar to CNNs based on spectral domains.  
The focus of this study is to examine functions based on the non-Euclidean domain, especially those that attempt to generalize or extend their solutions by exploiting their implementations with CNN techniques. Graphs and manifolds are examples of data in the non-Euclidean domain, and several studies [27; 28; 29] have attempted to generalize the application of NNs to graphs by a combination of deep learning models. The foremost application of a CNN to graphs in the spectral domain was proposed by [PERSON] [30]. However, the approach faced computational difficulties, which were resolved in [29; 31; 32], resulting in some state-of-the-art outcomes. The first application of a CNN on a mesh surface in the spatial domain centered on local intrinsic patches was proposed in [33]. These applications were found to have resulted in an up-to-the-minute performance on deformable recommender systems, whereas other approaches were based on intrinsic patches on point clouds and general graphs [11].  
There has been an explosive increase in the application of DL to manifolds and graphs in recent times. This can be seen in the numerous such techniques that have attempted to address issues pertaining to recommender systems and biochemistry.  
By way summary, the main objective of the current DL techniques, is to extend the application of the technique in the non-Euclidean domain. The non-Euclidean domain is represented by two (2) structures considered prototypically as graphs and manifolds. Graphs such as social networks consist of structured network data made up of edges and nodes. Manifolds on the other hand describe geometric-like applications of recommender systems like spatial coordinates based on the surface of an entity brought forth by a scan(LiDAR). These structures mathematically represent graph theory and differential geometry, respectively, and share numerous known characteristics.
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 2 Fiber-Wireless Broadband Access Network
### Challenges in Realization of the FiWi Networks  
Although the RoF network exploits the advantages of both the optical distribution network and wireless mobile network, the main challenge in the realization of the RoF concept is ensuring appropriate MAC protocol functionality. The merging of wireless and optical network segments has an impact on RoF network functionality, which is primarily reflected in the occurrence of additional propagation delays among different network segments. This can cause the expiration of certain timeouts of wireless MAC protocols that can significantly reduce network performance [14]. For example, when using the IEEE 802.11 standard, a distributed coordination function (DCF) of the MAC technique, the additional propagation delay has a large impact on the performance of the FiWi networks. There are different ways to solve this problem within the RoF concept; however, all of them are solutions that balance the the length of the optical cable and the network bandwidth [14].  
One of the possible solutions related to the elimination of the MAC coordination problem is the implementation of R&F technology (Figure 2). While, in the RoF approach, the optical cable is utilized as a medium for transmitting analog signals and management and access control to optical and wireless media is centralized in the central office (CO), in R&F technology, access control and management of optical and wireless media are separated and two different MAC protocols for each used network domain [14]. Therefore, in R&F networks, the optical and wireless networks are combined to create a single, integrated network. In general, R&F networks use distinct MAC protocols in each part of the network and therefore client access control is handled separately [5]. This means that traffic generated solely from wireless communication does not need to be transmitted through the optical network, as in the case of the RoF system. Distributed MAC protocols, such as IEEE 802.11, can avoid the additional propagation delays caused by fiber optic cables, which can negatively impact their performance. This feature allows for greater flexibility in the length of the deployed fiber optic cables and also increases the system's resilience, since local wireless traffic can still be served even if connectivity with the optical segment is lost [19].
## 2 SISO Impedance Measurement and Oscillation Analysis of FSC-VSPSU  
The SISO impedance measurement approach is more readily applicable in practical engineering due to its streamlined measurement process and reduced requirement for data acquisition sensors. To elucidate the SISO impedance measurement and oscillation analysis methods for the grid-connected FSC-VSPSU, the subsequent sections are structured as follows. Section 2.1 delineates the structure of the FSC-VSPSU, encompassing its control systems. Subsequently, Section 2.2 delineates the fundamental SISO impedance measurement technique. Section 2.3 will investigate the oscillation analysis method based on the measured SISO impedances.
## 4 Results
### SDEMs and TDEMs Reconstruction  
A total of 11 DEMs were generated from the winter and summer fieldwork campaigns. The total error, point cloud densities, output resolution, and other mapping parameters for each DEM are summarized in Table 3.  
Overall, with ad hoc image acquisition and image pre-processing, the SfM methodology was able to reconstruct different snow surfaces for both high and low light days and with different UAS/sensor systems. In all cases, centimeter-scale mapping was achieved (Table 3). For the Greenland sites, land-based surveyed TDEM depicts a lower quality than the UAS-based winter SDEM due to the limited points of view from which the survey could be performed. Nonetheless, it still achieved decimeter resolution (Table 3).  
Both light availability and snow surface type were found to play a role in the reconstruction quality. For example, without pre-processing, the minimal set-up was unable to map smooth areas and produced relatively rough point clouds compared to the smooth nature of that snow type (Appendix C). A decrease in point cloud densities was also observed for areas mapped with the advanced set-up as light availability decreased (Table 3). Lower achieved resolutions and reconstructions were observed for the SDEMs derived from the minimal set-up compared to the advanced set-up (Table 3). This is attributed to the less favorable conditions of smooth snow/overcast sky (Table 1) and the inferior quality of the sensor (extra noise), which required an inferior image quality reconstruction setting.  
The total error for the land-based SfM was higher compared to the UAS mapped areas due to the limited number of available camera positions (shown in Figure 2), which affected the geometry reconstruction and the GCP identification in the images. Accordingly, the point cloud densities were also affected by the inferior quality of these surveys.
## 5 Research Trends in Ge Photodetectors
### Zero-Bias PIN Photodiode  
As discussed in Section 3.4, high dark current leads to high stand-by power consumption in addition to the degraded SNR. Moreover, it is desirable for the detector and the receiver circuit to operate on a single power supply which often restricts the bias voltage for photodetector to be less than 1.5 V [74]. As a result, there has been an increasing research interest in the development of low-bias or even zero bias PIN photodiodes.  
In terms of responsivity, [PERSON] _et al_. [33] measured their Ge PIN detector's responsivity over the wide spectrum of 650 nm-1,650 nm. The reported responsivity at 0 V bias is more than 98% of the saturated value at 2 V reverse bias, which was attributed to high carrier collection efficiency resulting from the high built-in electric field in the diode's depletion region. High speed operation at zero-bias was demonstrated by [PERSON] _et al._[14]. From PIN Ge detectors with diameter of 10 \(\upmu\)m, a record high zero-bias 3 dB bandwidth of 25.1 GHz was obtained (see Figure 7).