esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application Discussion
### An Ultra-Low-Noise Image Sensor
 \tag{24}\]  
In order to analyze the efficiency of the proposed method for problems with different dimensional uncertainty, three cases are considered: only \(\boldsymbol{P}\) is uncertain in Case 1; only \(\boldsymbol{X}\) is uncertain in Case 2; and both of them are uncertain in Case 3. The initial design option is selected as - \(\boldsymbol{\overset{0}{X}}=(0.10\,\text{mm},\,0.10\,\text{mm},\,0.10\,\text{mm})\), and \(\overset{0}{v}=2.92\,\text{\SIUnitSymbolMicro mis}\) obtained by \(PPV\left( \begin{array}{cc}\overset{-0}{X}&\overset{-}{P}\\ \boldsymbol{\overset{-}{X}}&\overset{-}{P}\end{array} \right)\). After giving \(Bel_{j}^{T}=(85\%,\,95\%,\,99.9\%)\), the original problem is converted into three sub-problems, as in Equation (9). They are solved by the proposed method and the double-loop method; all results are listed in Table 3. Firstly, the results of the proposed method and the reference solutions are almost identical for all cases, and thus the validity of the results is presented. Secondly, each of the cases converges into a stable optimal set after three or four iteration steps. For this problem, the convergence of the proposed method is little affected by the number of uncertain variables. Thirdly, the performance function evaluations \((N_{F})\) increase with the increasing dimensional number of uncertainties, while overall the efficiency of the proposed method is relatively high. Case 3 is taken as an example. Even if the FEM is called directly by EBRO, \(N_{F}=198\) means a computational time of only about 20 h. \begin{table}
\begin{tabular}{c|c c c c c} \hline \hline \multicolumn{1}{c}{\(\boldsymbol{Xi}_{i}\), \(\boldsymbol{i=1,2,3}\) (mm)} & \multicolumn{2}{c}{\(\boldsymbol{P_{1}\) (W)}} & \multicolumn{2}{c}{\(\boldsymbol{P_{2}\) (W)}} \\
**Subinterval** & **BPA** & **Subinterval** & **BPA** & **Subinterval** & **BPA** \\ \hline \(\overline{\mathbb{X}}_{i}-0.05,\overline{\mathbb{X}}_{i}-0.03\) & 6.7\% & \([0.95,1.1.05]\) & 37.5\% & \([0.55,0.65]\) & 6.5\% \\ \(\overline{\mathbb{X}}_{i}-0.03,\overline{\mathbb{X}}_{i}-0.01\) & 24.2\% & \([1.05,1.15]\) & 17.6\% & \([0.65,0.75]\) & 23.8\% \\ \(\overline{\mathbb{X}}_{i}-0.01,\overline{\mathbb{X}}_{i}+0.01\) & 38.3\% & \([1.15,1.25]\) & 5.4\% & \([0.75,0.85]\) & 32.2\% \\ \(\overline{\mathbb{X}}_{i}+0.01,\overline{\mathbb{X}}_{i}+0.03\) & 24.2\% & \([1.25,1.35]\) & 19.1\% & \([0.85,0.95]\) & 12.5\% \\ \(\overline{\mathbb{X}}_{i}+0.03,\overline{\mathbb{X}}_{i}+0.05\) & 6.7\% & \([1.35,1.45]\) & 20.4\% & \([0.95,1.05]\) & 24.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The marginal BPA of variables in the image sensor problem. Figure 7: The finite element model (FEM) of the image-sensor-mounted PCB. PCB: printed circuit board.
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 4 Biobased Monomers for Polyester and Polyamide Synthesis
### Biobased Aliphatic Diacids  
Succinic acid is a naturally occurring dicarboxylic acid, which is predominantly produced commercially through petrochemical routes by catalytic hydrogenation of maleic acid or anhydride, with a global production of 30-50 kilo tons per year [87; 88]. Succinic acid can be also produced by fermentation of carbohydrates or glycerol using engineered bacteria or yeast. The current bio-route for succinic acid is based on proprietary _E. coli_ or yeast strains [88]. To lower the cost, other microorganisms and yeast have been developed, like _Coryne_-type bacteria, which shows a significantly higher productivity compared to _E. coli_. [33] Currently, four companies have built up commercial facilities for the production of biobased succinic acid: Reverdia, Succinity, Bioamber and Myriant [29].  
Itaconic acid is an attractive unsaturated monomer that has already been produced industrially by sugar fermentation using _Aspergillus terreus_ early in the 1960s [89; 90]. The current production of itaconic acid is around 80 kilo tons per year, mainly in USA, China, Japan and France [91]. To reduce the cost and increase the sustainability, current studies mainly focus on strain improvement of microorganisms by mutagenesis, development of more cost-effective process methodologies, and the use of alternative cheap substrates such as cellulolytic biomass [91].  
Adipic acid is one the most important commodity chemicals, which is mainly used for the production of nylon 6,6 [33; 92]. The current global market for adipic acid is around 4 million tons per year [31]. At present, over 90% of adipic acid is manufactured industrially by oxidation of cyclohexanol or KA-oil (a mixture of cyclohexanol and cyclohexanone) using concentrated nitric acid [92; 93; 94; 95]. In recent years, two prospective biosynthetic pathways to biobased adipic acid have been developedand are under commercialization evaluation at the moment [31; 33]: (1) chemo-catalytic conversion of biologically derived precursors such as _cis,cis_-muconic acid or D-glucaric acid; and (2) direct biological conversion of vegetable oils and sugars using yeast.  
In addition, suberic acid, sebacic acid and dodecanediolic acid are also (potentially) biobased monomers which can be converted from plant oils [96; 97; 31; 98].
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 2 Materials and Methods
### Image Reconstruction and Analysis
#### 3.1.2 Imaging Results  
The estimates from both methods were used to produce SSA images over a simulated 50 mm transducer sweep of the 19.2 mm array. Figure 7 shows a reference image produced by synthetic aperture imaging (coherent plane wave compounding) from a single transducer position. The full width at half maximum (FWHM) point target resolution at 100 mm depth was measured to be 2.20 mm.  
Very similar performance was observed for the two estimation methods compared to image formation using the true applied motion profile, improving the FWHM to 0.70 mm in both speckle tracking and channel correlation, matching the ideal case. The maximum errors in displacement estimation for speckle tracking and channel correlation were 12 and 83 \(\upmu\)m, respectively. These results are compared to a fully sampled large array with a 70 mm receive aperture and 50 mm transmit aperture that is roughly equivalent to the effective array formed by SSA and also achieved a FWHM of 0.70 mm.  
The observation of some differences due to the spatial frequency content differences between a fully sampled array and SSA effective aperture reflected in a trade-off between resolution and side lobe performance was expected. gCNR was also measured for the anechoic lesion in the images and found similar improvements across the cases with SSA improving gCNR and the estimated motion cases performing close to the ideal cases (slightly lower than with the equivalent large array).
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Log-Periodic Antenna  
A log-periodic antenna (also known as a log-periodic aerial or array) is a narrow-beam, directional, and multi-element antenna with a broad bandwidth. This type of antenna has the capability to operate over a wide range of frequencies. The log-periodic antenna supports a wide band of frequencies and, as a result of that, it is usually used in television antenna that specifically work in the VHF band. The log-periodic antenna is structurally similar to the yagi antenna, but with a distinct design. The log-periodic antenna comes in various formats as discussed herein.
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 2 Related Work
There have been many studies combining dynamic embedding vectors with deep learning. The DARTS model [18] proposed by [PERSON] et al. uses a soft selection embedding vector dimension search technique. The AutoEmb model [19] proposed by [PERSON] et al. is improved on the basis of DARTS. The final embedding dimension is determined by the weighted sum of six fixed-size embedded vectors, and the weight is determined by the frequency of user-item interactions. The ESAPN model [4] uses the hard selection method to realize the embedding vector dimension search technology, which is a further improvement of the above two models. However, these models focus on dynamic embedding, ignoring the capture of linear relationships, and do not employ additional linear models to compensate for the insufficiency of deep learning in low-frequency data regions. Therefore, it is necessary to construct a new model to strengthen the capture of linear relationships while capturing nonlinear relationships, which introduces dynamic embedding technology to improve the overall accuracy of the recommender system at the same time. In this paper, we propose the TMMNN model based on the ESPAN model. In order to solve the problem of high-dimensional embedding of low-frequency data in deep learning, a method of dynamically adjusting the dimensions of the embedding vectors is used in the model. In order to strengthen the capture of linear relationships between users and items, the matrix factorization method is used in the model, which has the advantagesof feeding back implicit information, reacting time effects, etc. [10]. To capture the nonlinear relationships, a multi-layer perceptron is used in the model for in-depth learning.
## 2 Materials and Methods
### Occupant Identification Sensor Station  
This study developed an occupancy detection sensor station based on frequency-modulated continuous wave (FMCW) radar to accurately detect the presence or absence of occupants in a specific space and infer their behavior based on their location. Figure 3 shows a schematic diagram of the developed sensor station. The FMCW radar transmits a continuous wave with a linearly varying frequency and receives reflected signals from targets to extract distance and velocity information. The frequency bandwidth of the chirp signal affects the range resolution, while the number of chirps affects the velocity resolution. High-velocity resolution is crucial for accurately distinguishing between stationary clutter and moving humans [27]. Table 2 presents the key specifications of the developed sensor station.  
The sensor estimates the occupant's location by using measured distance and angle data. The system was adjusted and calibrated to determine the presence/absence and movement state, as well as distance and angle, by comparing the x-y coordinates of the predefined spatial data. Occupancy status and location data from the FMCW sensor were transmitted to an ESP32 microcontroller. Due to its cost-effectiveness and performance, the ESP32 has become a widely adopted platform for various IoT devices, aligning with previous studies [28; 29]. Equipped with both Wi-Fi and Bluetooth capabilities, the ESP32 offers flexible connectivity options, simplifying system design and reducing the need for additional modules or components [30]. Data collected from the sensor was transmitted to a data analysis platform and stored at a rate of one data point per second.  
For the data analysis platform, Home Assistant 2023, a highly extensible open-source home automation system, was selected. Home Assistant supports integration with over 1500 different devices and services and undergoes continuous development to incorporate new features and protocols [31]. Its flexible configuration is primarily based on YAML (Yet Another Markup Language) configuration files, while also providing an intuitive user interface. In this study, data collected from the ESP32 was integrated into Home Assistant for visualization and storage, laying the groundwork for a dynamic temperature control system through integration with heating control valves. Ultimately, the goal is to create a customized indoor environment based on occupancy and activity patterns.  
Figure 3: Configuration of sensor station: (**a**) Schematic diagram of data flow; (**b**) install condition of FCMW sensor, data acquisition device, and home automation platform.
## 5 Prototypical Validation and Case Study  
To validate the technical feasibility of the method presented in Section 3 and the concept of Variable Deployment Models, we implemented a prototype based on the _Topology and Orchestration Specification for Cloud Applications (TOSCA)_[4]. TOSCA is an official OASIS standard for automating the deployment and management of cloud applications in a technology-independent and vendor-neutral manner. For this purpose, we (i) first introduce _Variability4 TOSCA_, which is an extension of the _TOSCA Simple Profile in YAML Version 1.3_[4] that supports VDMM introduced in Section 3. Afterward, we (ii) present the architecture, prototypical implementation, and benchmark of _OpenTOSCA Vintner_[12], which is a Variability4 TOSCA Deployment System that enables automating Steps and of our method. Thus, OpenTOSCA Vintner enables automatically transforming Variability4 TOSCA models into standard-compliant TOSCA models that can be executed by standard-compliant TOSCA orchestrators. We demonstrate this by employing the open-source orchestrators xOpera [13] and Unfurl [14] for executing the generated TOSCA models. Finally, we (iii) conduct a case study and apply our method to our motivating scenario.
# Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method
## 4 Simulation of the Positioning Errors
### First-Order Contraction Error of the Ball Screw and Linear Grating  
When the first-order contraction of the ball screw and linear grating exist in the machine tool, the measured radius of the machine tool in a circular motion can be written as:  
\[R=x_{0}=L\cos\theta_{1}+L\cos(\theta_{1}+\theta_{2}) \tag{17}\]  
Substituting Equation (16) into Equation (17), we can obtain:  
\[R=50\cos\frac{\pi}{6}+50\cos\frac{\pi}{3} \tag{18}\]  
The design trajectory of the machine tool in a circular motion can be written as:  
\[r=R\cos\theta_{3}+i(1+R\sin\theta_{3}) \tag{19}\]  
The real trajectory of the machine tool in circular motion is:  
\[r_{1}=R\cos\theta_{3}+\frac{aR}{2}\cos^{3}\theta_{3}+i(1+R\sin\theta_{3}) \tag{20}\]  
where \(a\) = 0.1.  
According to Equations (18)-(20), the first-order contraction error of the ball screw and linear grating can be obtained. The measurement radii of J-DBB vary with both the length of the bars and angles of the two bars. In the comparison diagram shown in Figure 4, the solid black lines represent the designed trajectory of the circular motion of the machine tool, and the dotted red lines represent the real trajectory of the circular motion of the machine tool. Figure 4a depicts the variations of the trajectories with different lengths of the bars. Figure 4b depicts the variations of the trajectories with the angles of the two bars. The results suggest that when the angle of the machine tool spindle is 0\({}^{\circ}\)and 180\({}^{\circ}\), the errors are larger. On the side, the angle of the machine tool spindle is 90\({}^{\circ}\)and 270\({}^{\circ}\), and the errors are smaller. Figure 4 shows that the equipment error increases with the increase of length \(L\) and is unchangeable with the variation of angle.
# Research on a 3D Seismic Horizon Automatic-Tracking Method Based on Corrugated Global Diffusion
###### Abstract
Although existing methods have, to some extent, solved the core challenges of horizon-position tracking and achieved certain results, there are still the following problems: (1) most of the current research focuses on single-horizon tracking rather than full-horizon tracking and building horizon-position connections is more challenging for full-horizon tracking because the interference of waveforms in the time direction will intensify, while single-horizon tracking only needs to consider the matching of waveforms in adjacent traces; (2) waveform similarity measures only establish connections between local horizon position points, without considering the entire spatial diffusion problem. Moreover, similarity measures are directional, and local similarity measures cannot achieve optimal results globally; (3) RGT and inclination are not sufficiently precise, and there are often modeling errors in the mapping process, resulting in the inability to accurately indicate horizon extension direction. Given the limitations of the existing methods and the severe challenges in tracking horizons using 3D seismic data, this paper proposes a novel automatic horizon-tracking method for 3D seismic data based on the DTW (dynamic time warping) algorithm [14; 15; 16; 17]. The method is mainly divided into two steps: (1) the establishment of a local connection relationship by utilizing the correlation between seismic traces based on DTW; adjacent traces are connected to form a trace set, as shown in Figure 1; (2) the establishment of a global diffusion model based on the relationship between seismic samples, where a global model is established, and the center trace set is expanded outward to obtain a clear optimal model, finally realizing automatic horizon tracking by using 3D seismic data.
## 4 Data and Examples
### Analysis of Multivariate Traffic Parameter Characteristics
#### 4.2.2 Time-Varying Characteristics of Vehicle Speed  
In this section, we show the vehicle speed distribution in the three detection sections, with the vehicles entering from lane 1 as the main analysis object. The speed of these vehicles passing through the downstream two sections was identified, and the distribution of vehicle speed scatter points was plotted as shown in Figure 6, where the y axis is the speed and the x axis is the speed series number of records; the speed limit is 60 km/h shown as the red line in Figure 6.  
Regarding the speed sequences detected at the three sections, some of the sections have speed values that exceed or approach the speed limit, as shown in Figure 6. This is due to the low vehicle density and good traffic conditions during the corresponding period, where several vehicles accelerate and leave at higher speeds, resulting in higher speed data. Additionally, several outliers exist that may be caused by equipment malfunctions during data collection. For the remaining data, the headway is mainly distributed between 10 and 60 km/h, and the speed significantly fluctuates, exhibiting complex variation characteristics.
# Effect of Cu, Cr, S Doped TiO\({}_{2}\) for Transparent Plastic Bar Reinforced Concrete
## 3 Experimental Results
### Results of Photocatalytic NO Removal Removal Test  
A photocatalytic NO removal test was conducted with each doped TiO\({}_{2}\) concrete specimen of 50 mm \(\times\) 100 mm \(\times\) 10 mm, as shown in Figure 15b. It was found that 15.8% of the total amount of NO was reduced by Cu-doped TiO\({}_{2}\) specimen during 5 h of 455 nm blue LED visible light irradiation by removing 6.4 umol out of 40.2 umol supplied. For Cr-doped TiO\({}_{2}\) and S-doped TiO\({}_{2}\) specimens, 1% of the total amount of NO was reduced by removing 0.4-0.5 umol. It was confirmed that the OPC (non-incorporated TiO\({}_{2}\)) specimen could hardly remove NO. This shows the Cu-doped TiO\({}_{2}\) specimen has some apparent photocatalytic NO removal performance compared to that of Cr- and S-doped  
Figure 14: Determination of energy band gap E\({}_{\text{g}}\) for TiO\({}_{2}\) specimens: (**a**) Cu-doped TiO\({}_{2}\); (**b**) Cr-doped TiO\({}_{2}\); (**c**) S-doped TiO\({}_{2}\); (**d**) TiO\({}_{2}\).  
Figure 12: XRD (X-ray diffraction) patterns of doped TiO\({}_{2}\)s.  
Figure 13: UV-visible absorption spectrum of specimens: (**a**) Cu-doped TiO\({}_{2}\); (**b**) Cr-doped TiO\({}_{2}\); (**c**) S-doped TiO\({}_{2}\); (**d**) TiO\({}_{2}\).  
TiO\({}_{2}\) specimens. The Cu-doped TiO\({}_{2}\) test result is judged to be an active photocatalytic reaction from homogeneity of microstructure and well dispersed nanoparticles in the reaction surface (Figure 10) showing a well composed anatase phase and Cu peak (Figure 12). In Figures 13 and 14, doping of Cu, Cr, and S contributed to reducing the difference between the conduction and valence bands of titanium dioxide. In particular, as the band gap energy of the Cu sample showed a large decrease, it was judged that it showed excellent NO removal performance in visible light irradiation.
# Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data
## 2 River Flow Velocity Inversion Model
### Signal Open-Loop Tracking Method
It has generally been much smaller than the integrator's bandwidth, \(\Delta t\)_ <\(1/T_{coh}\), so I and Q components can be approximated as follows:  
\[\begin{split} I_{n}&=\overline{A_{n}}\cdot\cos(2\pi \overline{\Delta f_{n}}\cdot T_{coh}+\Delta  
Vector amplitude \(A_{n}\) and raw residual phase \(\varphi_{out}\) can be defined as follows:  
\[\overline{A_{n}}=\sqrt{I_{n}^{2}+Q_{n}^{2}} \tag{6}\] \[\varphi_{out}^{n}=\arctan 2(\frac{Q_{n}}{I_{n}})=2\pi\overline{A_{n} }\cdot T_{coh}+\Delta\phi\]  
The raw residual phase \(\varphi_{out}\) is extracted from Equation (6) using the four-quadrant arctangent function. Figure 3a shows the wrapped phase of the GPS PRN 9 and Figure 3b shows the wrapped phase BDS IGSO 10 satellite in 2 min on 22 April 2021, 10:40 a.m.-10:42 a.m. (LT). The raw residual phase may exceed the phase interval [-\(\pi\),\(\pi\)], in which case the phase wrapping phenomenon occurs. \[\varphi_{residual}=Unwarp[\varphi_{out}]+2k\pi \tag{7}\]  
where Unwarp [] represents the phase unwrapping operation, and \(\varphi_{residual}\) represents the result after the raw residual phase unwrapping, with \(k\) as the integer ambiguity. Figure 4 shows the residual phase after phase unwrapping of GPS PRN 9 and BDS IGSO 10 for 2 min on 22 April 2021, 10:40 a.m.-10:42 a.m. (LT). The first 100 ms unwrapped phase details are shown with red dashed boxes. The incoherence time in this paper is 1 ms, and the wrapped carrier phase in Figure 3 will be noisy in detail, but as long as the unwrapped carrier phase meets the relatively obvious linear trend, it can meet the requirements of inversion results every two min, as in this paper. Figure 4: Unwrapped residual phase of GPS PRN 9 and BDS IGSO 10 in 2 min (10:40 a.m.–10:42 a.m. local time) (**left panel**); unwrapped phase details of GPS PRN 9 and BDS IGSO 10 in 100 ms (**right panel**). Figure 3: An example of wrapped phase of GPS PRN 9 (**a**) and BDS IGSO 10 (**b**) in 2 min (10:40 a.m.–10:42 a.m. local time).