esa-sceva/satcom-chunk-collection huggingface dataset:
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
###### Abstract  
In this paper, an angle-of-arrival (AOA)-based algorithm is proposed for tracking the position of an anonymous target in three-dimensional (3D) space. Distributed sensors are deployed, which can measure both the azimuth and elevation angles of the AOAs. Assuming the target movement is non-linear, the extended Kalman filter (EKF) is applied, where the observation process is realized by a practical AOA-based position detector, to form a unified factor graph (FG) framework. Moreover, the variance of observation errors, which is needed by EKF, is estimated in real time by using both the AOA measurements and the predicted target state. Such a dynamic estimating approach exhibits higher performance robustness compared to the conventional method, especially when the sensing environment is unstable. Additionally, the predicted target state is also used as the a priori information of the system, in order to reduce the impacts of burst sensing errors. According to the simulations, the proposed system is shown to achieve less root mean squared errors (RMSE) in different evaluation scenarios, with fast convergence behavior.  
Keywords:3D tracking; AOA; factor graph; extended Kalman filter +
Footnote †: journal:
# Prediction of Transient Temperature Distributions for Laser Welding of Dissimilar Metals
## 4 Results
### Offsetting the Laser Beam toward Ti-6 Al-4V  
It is observed from the prediction that predicted temperatures are sufficient to melt both samples. Occasionally, it is suggested for dissimilar joining to focus the laser beam on the side with the higher melting point to melting it with a higher temperature. It helps to control the IMC formation and transfer the heat toward low melting point side [2; 56]. The interface faces a comparatively low temperature compared to a zero offset condition, but it should be sufficient to melt the sample with the lower melting point. In this study, it is observed that the interface can reach up to a temperature of 3200-3300 K. Offset of the laser focus by 0.3 and 0.6 mm toward Ti6 Al4V side reduces the temperature of the interface points, sufficient for AISI 316L to melt. Figure 16a,b indicates that the predicted temperature of interface point (10, 20, 0) for both offset distances is sufficient to melt the steel alloy; in addition, the temperature that can be achieved in the bottom layers are also sufficient to get the penetration. The reduction in interface temperature for that particular point on the interface is comparable with that shown in Figure 9a. The noticeable reduction of the melt area in the steel side and the wider melt area in the Ti6 Al4V side may cause less healthy convective flow in the molten zone around the keyhole, avoiding the formation of intermetallic phases in the weld because most heat input can be lost quickly on the Ti6 Al4V side before sufficient heat is transferred into the 316L side. Ti6 Al4V has a high value of thermal conductivity as well. When the laser offset is 0.35 mm toward the Ti alloy, it could be effectively connected with SS. The ratio of molten Ti alloy to SS was 95:5. A TiFe\({}_{2}\) layer was formed on the SS side with a width of 24 um and the tensile strength of the joint could range up to 182 MPa [57].  
Figure 15: Maximum temperature (transient) of edge probes x = 0 mm (green) and x = 20 mm (blue).
# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 2 Related Background  
### Compressed Sensing  
Classical compressed sensing collects all the information in high-dimensional data. It maps the high-dimensional data into low-dimensional data to achieve an efficient dimensionality reduction representation of the signal, and it can reconstruct high-dimensional data. By observation of compressed sensing, signals with sparsity can be reconstructed from less data than traditional Nyquist sampling. Therefore, various signal transformations can transform many non-sparse signals into sparse signals, such as discrete cosine transform (DCT), discrete wavelet transform (DWT), etc. From the mathematical view, the classical CS can be expressed as follows:  
\[\mathbf{x}=\mathbf{\
abla}\mathbf{s} \tag{1}\]  
\(\mathbf{x}\in\mathbb{R}^{\text{N}}\) is the signal that can be sparse. \(\mathbf{\
abla}\) is a set of orthonormal basis. \(\mathbf{s}\) is the signal in the sparse domain.  
\[\mathbf{y}=\mathbf{\Phi}\mathbf{x}=\mathbf{\Phi}\mathbf{\
abla}\mathbf{s} \tag{2}\]  
\(\mathbf{\Phi}\in\mathbb{R}^{\text{M}\times\text{N}}\) is an observation matrix which is uncorrelated with \(\mathbf{\
abla}\) and \(\text{M}\ll\text{N}\). \(\mathbf{y}\in\mathbb{R}^{\text{M}}\) is the observation vector by compressed sensing dimensionality reduction.  
However, if sparse signals are reconfigurable, the observation matrix needs to satisfy the restricted isometry property (RIP):  
\[(1-\delta_{K})\parallel\mathbf{x}\parallel_{2}^{2}\leqslant\parallel\mathbf{\Phi}\mathbf{ x}\parallel_{2}^{2}\leqslant(1+\delta_{K})\parallel\mathbf{x}\parallel_{2}^{2} \tag{3}\]  
\(\delta_{K}\in(0,\,1)\) is the restricted isometric constant of the matrix \(\mathbf{\Phi}\), and RIP ensures that any two sparse vectors still maintain their Euclidean distances under the projection of \(\mathbf{\Phi}\). The observation matrix satisfies the RIP so that the observation vector of any sparse vector can be reconstructed by minimizing the observation error, which is mathematically represented as:  
\[\hat{\mathbf{x}}=\underset{x}{\text{arg}min}\parallel\mathbf{y}-\mathbf{\Phi}\mathbf{x}\parallel_ {2}^{2} \tag{4}\]  
\(\hat{\mathbf{x}}\) is the reconstructed signal. The compression ratio of compressed sensing [26] is \(\text{r}=(\text{N}-\text{M})/\text{N}\), so \(\text{M}\ll\text{N}\) will have a higher compression ratio. Different levels of compression ratio can be achieved by adjusting the size of the observation matrix while satisfying the RIP. The observation vector y of compressed sensing can be reconstructed with high probability by algorithms based on the convex optimization algorithm, the greedy algorithm, the combinatorial reconstruction algorithm, and the Bayesian method. However, there will be problems such as unstable reconstruction when the number of observations is low and a long reconstruction time due to high computational complexity.
###### Abstract  
One of the most important applied technologies in water treatment is reverse osmosis (RO). However, membrane fouling and flux reduction pose significant challenges. The electric field, as an effective preventive measure, has received limited attention in RO applications. In this study, we added electric fields to finished rolled RO membranes to investigate their effect on membrane fouling and desalination performance. Experimental results indicated that higher electric fields were associated with higher concentrations of treated brine, resulting in a more significant effect. Permeate flux ratios increased with increasing voltage, with peaks of 1.02% (1000 mg/L, 25 V), 1.23% (2000 mg/L, 25 V), and 1.37% (3000 mg/L, 25 V), respectively. Additionally, the maximum reduction in the specific energy consumption (_SEC_) was 31% (2000 mg/L, 25 V) and 59% (3000 mg/L, 25 V), respectively. Notably, electric fields had a retarding effect on Ca\({}^{2+}\) and humic acid (HA) fouling, with a stronger effect on HA, and higher permeate flux was maintained even after 120 h of operation. While this study visually demonstrates the direct effect of electric fields on RO, further quantification of the economic benefits of this method and a comprehensive understanding of the mechanisms behind how the electric field enhances permeate flux and mitigates membrane fouling are needed.  
reverse osmosis; membrane fouling; desalination performance; permeate flux; electric field +
Footnote †: journal: Applied Sciences  
0000-0002-414-575[[https://doi.org/10.3390/app14020575](https://doi.org/10.3390/app14020575)]([https://doi.org/10.3390/app14020575](https://doi.org/10.3390/app14020575))
## 1 Introduction
### Related Works  
#### 1.2.1 Maritime Internet of Things (MIoT)  
With the increasing frequency of maritime activities, the development of maritime communication technology has an immense strategic importance [16]. However, due to the complex and variable maritime environment and inconsistent communication system standards, the development of maritime communication lags behind that of land-based communication significantly. The MIoT relies on reliable communication networks for the transmission and exchange of data [17]. The network can include SC, wireless sensor networks, cellular networks, and other technologies to cover vast ocean areas [18]. An efficient communication network can realize real-time DT and remote control between nodes and supports the needs of different application scenarios. In [19], the MIoT is mainly driven by marine resource development and management, maritime shipping safety, marine environment monitoring and protection, the cross-border cooperation and sharing economy, and scientific and technological progress and application needs [20]. With the continuous development of related technologies and the continuous expansion of application scenarios, the MIoT plays an increasingly important role in the marine industry and marine environmental protection [21].  
The MIoT is widely used in marine resource exploration and development, ship safety management, marine environment monitoring, fishery management, marine disaster early warning, and other fields. For example, the MIoT enables real-time monitoring of the marine meteorological and environmental conditions, providing decision support to fisheries managers; ships can be tracked and monitored in real time to improve the safety and efficiency of ship operations; the exploitation of marine resources can be monitored to support sustainable development [22; 23; 24]. MIoT technology and applications are used, through the combination of sensors, communication networks, and data processing platforms, to achieve the monitoring of the marine environment, resource management, and security [25]. MIoT technology has broad application prospects and can provide strong support for the development of the marine industry and environmental protection [26].
## 4 Evaluation Methodology
### Experiment Result of Feature Selection with Permutation Decoding  
In the experiment, performances were compared with detection models that use all 41 features from the KDD Cup '99 dataset, and simple detection rate (detection precision)-based wrapper, PCA, and the proposed feature-selection methods were tested for feature selection performance comparison. For feature-selection methods, 4, 6, and 8 feature selections were performed by selecting 10%, 15%, and 20% of the total features, and they were applied to each of the 10 models for average performance comparison. An NB classifier-based detection model was used.
## 3 Mathematical Modeling of PV Models  
The first step in PV modeling is determining the model's parameters, which may be achieved by analyzing experimental data. Following model development, the model may be used to examine how the temperature, irradiance, and load affect the PV system's efficiency. The models can also be used to maximize the PV systems' efficiency. This is figuring out how to best align and arrange solar cells to produce the most energy possible.  
PV cells, also referred to as solar cells, are electronic devices specifically engineered to transform solar radiation into electrical energy. These devices are constructed using semiconductor components, such as silicon. These materials have the capacity to absorb photons released by sunshine, therefore releasing electrons and enabling the movement of electric current. Solar panels are frequently joined in series and parallel combinations to increase their voltage and electrical output. Solar panels have a wide range of uses, including generating energy for household and business reasons, providing power in remote locations, and enabling the operation of portable electronic equipment such as calculators and mobile phones. Solar cells are continuously improving in terms of efficiency and cost-effectiveness, making them a more appealing choice for capturing clean and sustainable energy. Equivalent circuit models are used to depict every aspect of the _I-V_ spectrum of a cell or component. These models consist of a set of sequential functions that are calculated based on established operating restrictions.  
A mathematical model may accurately depict the output properties of the PV model. This will demonstrate the biological processes that occur within the cell of the module. The SDiM and DDiM models are widely utilized. The characteristics of the PV cell are retrieved by assuming that the cells have equivalent characteristics and operate under similar circumstances. This criterion is used to formulate the objective functions for the model's description.
## 2 Establishment of the Analysis Model  
### Pavement Structure and Materials  
The structure and material of the three-dimensional finite element analysis model were consistent with those of the Lanzhou BRT asphalt pavement structure, as shown in Figure 1. The following assumptions about the BRT asphalt pavement structure model were made [41; 42]. (1) The surface of the asphalt pavement was made of viscoelastic materials, and the remaining layers were made of linear elastic materials. (2) The surface of the pavement was considered to be a horizontal plane, regardless of its roughness and the influence of the horizontal and vertical slopes. (3) Structural layers were of uniform thickness, and were isotropic and continuous, without considering the influence of gravity. (4) The structural layers were completely continuous, and the displacement was continuous in the vertical direction.  
In regard to the actual engineering of the Lanzhou BRT asphalt pavement, the surface materials of the pavement are AC-13, AC-20 and ATB-25. The asphalt used in those asphalt mixtures is 90\(\mu\), with a penetration requirement at 25 \({}^{\circ}\)C of 80-100 (0.1 mm). Due to the remarkable viscoelastic properties of the asphalt mixture, its material properties are greatly affected by temperature. Considering the influence of the temperature field on the mechanical properties of BRT pavement structures, a creep model was adopted for the surface asphalt mixture. In this model, the surface deformation (\(\varepsilon_{\text{cr}}\)) is related to the temperature (\(T\)), stress (\(q\)), and time (\(t\)), and can be obtained with Equation (1):  
\[\varepsilon_{\text{cr}}=f(T,q,t) \tag{1}\]  
In this study, the strain hardening law was applied to simulate the creep characteristics of the asphalt mixture under loading conditions. In the analysis process, the stress state was varied. As a result, the above equation can be rewritten as Equation (2):  
\[\varepsilon_{\text{cr}}=\left(Aq^{n}[(m+1)\bar{\varepsilon}_{\text{cr}}]^{m} \right)^{\frac{1}{m+1}} \tag{2}\]  
where \(A\), \(n\) and \(m\) are parameters of the strain hardening creep model related to the temperature [43], and \(\bar{\varepsilon}_{\text{cr}}\) denotes the uniaxial equivalent creep strain.  
The thickness and material parameters of each structural layer of the pavement model are listed in Table 1, which shows the elastic parameters and creep parameters of AC-13, AC-20 and ATB -25 at different temperature [43]. The material parameters at 20 \({}^{\circ}\)C retrieved from the literature [26] were adopted for the base, subbase and subgrade.  
Figure 1: Lanzhou BRT asphalt pavement structure.
## 2 Materials and Methods
### Statistical Analysis  
The clinical data were analyzed using the IBM SPSS Statistics 24 software (Statistical Package for the Social Sciences, NY, USA). The normal distribution of the data was checked using the Shapiro-Wilk test. When data were normality distributed, it was reported as mean \(\pm\) Standard Deviation (S.D) and a paired t-Student test was used to measure inter-day variation of variables. When data were not normally distributed, a Wilcoxon test was used to establish the differences of variables between the visits and median\(\pm\) interquartile range (IQR) was provided.  
The statistics program used for the tear fluid metabolic protein analysis was R: a language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria). Some proteins' concentrations were detected as \"Out of Range\" (OOR, meaning that the value was less than the MinDC), or were extrapolated beyond the standard range (meaning that the values are outside the standard curve range). Robust regression on order statistics (ROS) [43], as used for the imputation of LOW values in molecules with % of detection <100, assuming log-normal quantiles, after checking that the data follows a log-normal distribution. To accomplish this, the non-detects and data analysis (NADA) R package [44] was used. However, to avoid biased results, statistical analysis was restricted to molecules with % detection values higher than 50%, i.e., with <50% of sample falling below the OOR. Molecules detected in less than 50% of the samples were not statistically analyzed any further. Metabolic protein data was transformed using the logarithmic base 2 scale.  
The within- and between-subject variance components were used to calculate the intra-subject coefficient of variation (CV) in tear fluid metabolic protein concentrations. Metabolite level variation and intersession reliability were assessed with the intra-class correlation coefficient (ICC). Linear random-effects models were also used to calculate ICCs as a measure of concordance of two samples. ICC values were interpreted as follows: 0-0.25, poor agreement; 0.27-0.49, low agreement; 0.5-0.69, moderate agreement; 0.7-0.89, strong agreement; and >0.8, very strong agreement [45]. Bland-Altman plots and limitsof agreement (LoA) were used to assess agreement between metabolites levels in both visits. The Pearson correlation test was calculated to determine the correlation between the variables. A correlation coefficient (r) was interpreted as follows: 0-0.20, poor; 0.21-0.50, fair; 0.51-0.7, moderate; 0.71-0.9, very strong, and \(>\)0.9, almost perfect correlation [46]. Statistical significance set at _p_-value of \(\leq\)0.05.
# Cable Force Identification of Two Bending Cable Networks with Arbitrary Boundary Conditions
###### Abstract  
The determination of existing cable forces is essential for evaluating the performance of bridges. The vibration technique is preferred among available practical methods because it is simple, fast, and economical. To apply this method, many researchers introduced analytical and empirical formulas considering bending rigidity, cable length, and boundary conditions. Nevertheless, most existing procedures to determine cable forces are for single cables, which are unsuitable for cable networks. To measure the cable forces of the cable networks, engineers should remove any cross-ties before conducting field tests. However, the removal of cross-ties separates the cable network into two independent cables, which does not reflect the actual behavior of the cable network. Hence, to save time and improve accuracy, this paper introduces a method to directly identify the cable tensions of a two-cable network without removing any cross-ties. This technique can precisely estimate the cable forces of the two-bending cable networks with arbitrary boundary conditions with an error of less than 1.0 percent.  
cable-network; bending effect; effective vibration length; cross-tie axial stiffness estimation +
Footnote †: journal: Applied Sciences
# Fuzzy Case-Based Reasoning System
## 3 Fuzzy Case-Based Reasoning System
### Introduction  
We may notice that fuzzy case-based reasoning system is a kind of fuzzy expert system, but differs from them significantly. Previous fuzzy expert systems make use of \"fuzzy inference\", formulating the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & \(X_{1}\) & \(X_{2}\) & \(X_{3}\) & \(X_{4}\) & \(X_{5}\) & \(X_{i}\) & \(X_{n}\) \\ \hline \(c_{1}\) & \(c_{1}(X_{1})\) & \(c_{1}(X_{2})\) & \(c_{1}(X_{3})\) & \(c_{1}(X_{4})\) & \(c_{1}(X_{5})\) & \(c_{1}(X_{i})mapping from a given crisp input to a crisp output using fuzzy logic. The process of fuzzy inference involves all of the pieces that are described in Membership Functions, Logical Operations, and If-Then Rules. This section describes the steps of a fuzzy expert system and uses the example of the two-input, one-output, three-rule problem. The basic structure of this example is shown in the following diagram, Figure 2a. From Figure 2a, we see that the inputs of a fuzzy expert system are crisp and the outputs are also crisp; in intermediate layers, fuzzy logic is used for predicting the outcomes given an input set based on the background knowledge--fuzzy rules and the membership functions. Our fuzzy case-based reasoning system is shown in Figure 2b. From Figure 2b, we first transfer the crisp inputs into fuzzy ones. Assume input 1 (Atmosphere Pressure) is \"Lower\" and input 2 (Wind Force) is \"Breeze\", according to the case base created in advance, then the output (Precipitation) is \"Heavy\", which is a fuzzy output.  
Figure 2: (**a**) Fuzzy expert system. (**b**) Fuzzy case-based reasoning system.  
The most important reason for changing crisp cases into fuzzy ones is that the prediction results may get better with the fuzzication process. It is well known that the need for accurate prediction is apparent when considering the benefits that it has. However, experiments show that the accurate crisp patterns make some accurate prediction results useless and meaningless. We also add the \"vague\" concept into the pattern reduction problem. It is somewhat vague inputs that make the outcomes of prediction more meaningful and accurate.  
Some would say that this better prediction accuracy might be achieved by sacrificing the output precision, and sometimes that is the case. The disadvantage of this system is that it can increase the prediction accuracy, but must sacrifice the precision.  
Sometimes, however, this is not the case. Experiments and the proof show that with the output precision unchanged (only change the inputs fuzzification), the prediction accuracy can still be increased. These phenomena can be intuitively understood because fuzzy ones may solve a problem better than a more accurate one. Sometimes, several elements together take effect, and if we divide them then we will not be able to get desired results.
## 5 Experimental Results
### Evaluation Metrics
#### 5.1.2 Normalized Correlation Coefficient  
NCC is the measurement utilized to discover the relationship coefficient between the original watermark and the extracted watermark. When NCC is close to 1, the extracted watermark equals the original watermark. The NCC is defined as follows:  
\[NCC_{qr,qr}=\frac{\sum_{x=1}^{m}\sum_{y=1}^{n}qr(x,y)\times Eqr(x,y)}{\left( \sqrt{\sum_{x=1}^{m}qr(x,y)^{2}}\right)\left(\sqrt{\sum_{x=1}^{m}Eqr(x,y)^{2 }}\right)} \tag{2}\]  
Figure 7: Sample watermarked images and extracted watermarks.