esa-sceva/satcom-chunk-collection huggingface dataset:
# A Recursive Least-Squares with a Time-Varying Regularization Parameter
## 4 Recursive Least-Squares
### Recursive Least-Squares with Fixed Regularization
: _The approximation Equation (22) is based on the first-order Taylor's series expansion of matrices. Consequently, more accurate results can be obtained by expanding to higher orders._

The proposed method for RRLS is summarized in Algorithm 2. ```
1. Start with \(\hat{\mathbf{x}}_{0}=\mathbf{0}\) and \(\mathbf{P}_{0}=\dfrac{1}{\gamma_{0}}\mathbf{I}\). 2. For each new observed data, \(d_{m+1}\), and new row, \(\mathbf{u}_{m+1}^{T}\) do the following:  
\(\bullet\) Estimate the new regularization parameter, \(\gamma_{m+1}\) using a suitable regularization parameter selection method (e.g., Equation (7)). \(\bullet\) Calculate: \(\delta=\gamma_{m+1}-\gamma_{m}\). \(\bullet\) Calculate: \(\mathbf{P}_{m+1}\) using Equation (26). \(\bullet\) Calculate: \(\hat{\mathbf{x}}_{m+1}\) using Equation (27).
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## Materials and Methods
### Loss Functions  
Class imbalance is a major problem in most tumor segmentation problems, and it is even more severe in our task compared to the BraTS glioma dataset because of small target volumes. The imbalance would most likely lead the model to a trivial solution, which predicts all voxels as background. There are several ways to deal with this problem by modifying the loss function.
###### Abstract  
Crosslinkers are indispensable constituents for the preparation of SPE materials with ethylene glycol dimethacrylate (EGDMA) and divinylbenzene (DVB) among the most prominent representatives. A crosslinker that has not yet been used for the preparation of SPE sorbents is 3,3'- (hexane-1,6-diyl)bis(1-vinylimidazolium) bromide [C\({}_{6}\)-bis-VIM] [Br]. In this study, we synthesized differently crosslinked vinylimidazole polymers with EdDMA, DVB and [C\({}_{6}\)-bis-VIM] [Br] and evaluated their extraction efficiencies towards phenolic acids. Dispersive SPE experiments performed with the [C\({}_{6}\)-bis-VIM] [Br] crosslinked polymers exhibited significantly higher extraction recoveries for the majority of analytes. Due to these promising results, the [C\({}_{6}\)-bis-VIM] [Br] crosslinked polymer was optimized in terms of the monomer to crosslinker ratio and an efficient dispersive SPE protocol was developed, with maximum recoveries ranging from 84.1-92.5% and RSD values < 1%. The developed extraction procedure was also applied to cartridges resulting in recoveries between 97.2 and 98.5%, which were on average 5% higher than with the commercial anion exchange sorbent Oasis\({}^{\text{\textregistered}}\) MAX. Furthermore, the sorbent was regenerated showing a good reusability for the majority of analytes. In conclusion, this study clearly highlights the yet untapped potential of the crosslinker, [C\({}_{6}\)-bis-VIM] [Br], with respect to the synthesis of efficient anion exchange polymers for SPE.  
Keywords:crosslinker; phenolic acids; vinylimidazole; anion exchanger; co-polymer; solid-phase extraction; sustainable analytical sample preparation +
Footnote †: journal:  
Acticle
## 3 Results and Discussion
### Preprocessing and Feature Extraction Using the Proposed Algorithm  
Before analyzing SDS-PAGE images with the new methodology: \"Image Profile Based on Binarized Image Segmentation\" (IPBBIS), the images were adjusted to the size defined by the variables MAXWIDE = 600 (width in pixels), MAXHIGH = 400 (height in pixels).  
The SDS-PAGE protein gel image was converted to greyscale and then binarized. In addition, an erosion operation was performed to increase the spacing between samples and bands (see Figure 5). Next, the IPBBIS method was applied to perform feature extraction.  
Figure 4: (**A**) SDS-PAGE gel containing GPN protein expressed at different concentrations. Lane 1, molecular weight control; lanes 2-11, GPN at the following concentrations: 2.0, 0.0, 14.5, 9.5, 27, 18, 30, 18.5, 26, and 14 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively. (**B**) The intensity profile of lane 1 of (**A**) (weight control). (**C**) The intensity profile of lane 4 for (**A**). (**D**) The intensity profile of the bands for the recombinant GPN protein region, highlighted in red from the gel in (**A**).  
The image from the preprocessing (Figures 5C and 6) was filtered with a binary mask consisting of a matrix of \(1\times 400\) pixels (Figure 6B). The IPBBIS method was applied, and different binarization techniques such as Niblack, Sauvola, and Otsu were used. There were no significant variations in the obtained results.  
By calculating the histogram in the lane region covered by the binary mask (MAXWIDE = 1 \(\times\) MAXHIGH = 400, for complete gel analysis or MAXWIDE = 1 \(\times\) MAXHIGH = 50, for band analysis), the pattern in Figure 6C was obtained, which shows the distribution of the pixels. On the other hand, by performing the binary segmentation of the same region (inside the mask), the histogram shown in the graph in Figure 6D was generated. As the image is binarized,  
Figure 5: Preprocessing of SDS-PAGE gel images. (**A**) Grayscale image. (**B**) Binarized image. (**C**) Eroded image.  
Figure 6: IPBBIS. (**A**) Binarized and eroded image. (**B**) Representation of the binary mask with a size of 1 \(\times\) 400 pixels placed at pixel 7. (**C**) A simple histogram of the region contained in the binary mask, (**D**) Histogram of the region after applying Otsu segmentation to (**C**).  
this pattern only shows the maximum intensity values for black (pixel 0) and white (pixel 255). The number of white pixels is stored in an array with all gel values selected by the binary mask.  
The array obtained by applying the binary mask in Figure 6A generated the new image intensity profile. Figure 7 shows the maxima representing the center of each analyzed band and the multiple minima separating them.
# Development and Application of Intelligent Temperature Control System for Large Aqueduct
## 3 Engineering Application of Intelligent Temperature Control System for Large Aqueduct
### Simulation Calculation Module
#### 3.2.2 Establishment of the Characteristic Point  
The aqueduct is a thin-walled structure cast with C50 concrete. Early cracks may arise on the surface of the side walls or the joint surface of the old and new concrete, which may affect the structure safety [17; 18]. Sidewall surface: C50 concrete exerts heat quickly in the early stage. The internal and surface of the aqueduct can produce a large temperature difference in two or three days after pouring, and cracks will occur when the tensile stress of the concrete surface exceeds the design value. A combined surface of old and new concrete: the concrete of the aqueduct is poured in layers with an interval of 15 days when the second layer of concrete is poured, which will be constrained by the first layer. The large tensile stresses will be generated in surface of old and new concrete due to the constraint and temperature, which will result cracks [19].  
In order to prevent the occurrence of cracks affecting structural safety and to characterize the temperature stress field of concrete for the aqueduct, the characteristics of seven locations (a-g) on the mid-span section of the aqueduct are taken as the characteristics, taking into account the symmetry of the aqueduct structure, the temperature stress and the comprehensive influence of concrete gravity, as shown in Figure 5.
## 3 Results
### Comparative Analysis of VOC Emissions at Different Flowering Stages
#### 3.2.2 Principal Component Analysis and Hierarchical Cluster Analysis  
Principal component analysis (PCA) showed that the first three principal components explained 29.92% (PC1), 18.19% (PC2), and 13.06% (PC3) of the variance, respectively, with a cumulative variance of 61.18% (Figure 4). The three replicates for each developmental stage were closely clustered, indicating reasonable reproducibility. Samples from different  
Figure 3: Comparative analysis of the relative total VOC content and the percentage of different VOC categories at four different flowering stages. (**A**) Comparative analysis of total VOC content. Different lowercase letters indicate significant differences at the \(p<0.05\) level. (**B–**E**) Relative content percentages of different VOC categories at BS, HS, FS, and SS stages, respectively.  
stages were distinct, indicating significant differences in overall VOC characteristics. In PC1, \(\beta\)-pinene (loading coefficiente = 0.21) and \(\beta\)-ocimene (loading coefficiente = 0.20) have higher loading coefficients (Table S4). In PC2, ylangene, (Z,E)-\(\alpha\)-farnesene, curcuminene, calamenene, benzyl acetate, phenylethyl alcohol, and benzaldehyde have higher loading coefficients (Table S4). These VOCs with significant loading coefficients underscore their importance in distinguishing different stages. Hierarchical clustering heatmap analysis further validated the PCA results, with replicates clustering together and samples from different stages being distinct (Figure 5). The 67 VOCs were clearly divided into two major classes: Class I (35 VOCs) with higher emissions at HS and FS, and Class II (32 VOCs) with higher emissions at SS and BS (Figure 5).
## 3 Results  
### Model Validation and Parameter Optimization  
Figure 2(a) shows the comparison of monthly GPP estimated by the EC-LUE model with the corresponding tower-based values of 72 months at the rubber ecosystem site. The coefficient of correlations (R) and root-mean-square error (RMSE) of the estimated monthly GPP were 0.70 and 2.00, respectively, indicating the ability of the EC-LUE model  
Figure 2: Study flowchart. RMSE: root mean square error; MAPE: mean absolute percentage error; MAE: mean absolute error. SRHI: standard relative humidity index; \(\epsilon_{\max}\): the maximum LUE; VPD\({}_{0}\): the empirical coefficient of the water demand constraint equation; NDVI: normalized difference vegetation index.  
to capture variations in monthly GPP across different months. However, this model tended to underestimate high tower-based GPP to some extent during 2012 and 2013 (Figure 3b).  
According to [53], we extended the range of two free parameters, \(\mathrm{VPD_{0}}\) and \(\mathrm{\epsilon_{max}}\), 10% downward and upward, respectively, from the base range covered by all PFTs (Plant functional Types). EC-LUE was conducted by inputting all possible combinations of free parameters, with \(\mathrm{VPD_{0}}\) taking a step of 0.01 kPa, and \(\mathrm{\epsilon_{max}}\) having a 0.01 gC MJ\({}^{-1}\) step. The estimated outputs were compared with the tower-based GPP using MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE; the optimal values were \(\mathrm{VPD_{0}}\) = 0.34 kPa and \(\mathrm{\epsilon_{max}}\) = 2.75 gC MJ\({}^{-1}\) (see Figure S2 for detailed information).
# Deformation Law and Control Limit of CRTSIII Slab Track under Subgrade Frost Heave
###### Abstract
[28] revealed the deformation characteristics of unit slab track under subgrade settlement and also analyzed the influence of structural thickness and bonding mode on track irregularity. [PERSON] et al. [29] proposed a double-strain criterion of slab track for frost heave crack propagation and investigated factors involved in frost heave crack propagation. [PERSON] et al. [30] analyzed the effects of differential subgrade settlement on the damage  
Figure 1: Distribution of high-speed railway in frozen soil regions. distribution and mechanical properties of the slab track by establishing a three-dimensional damage model. [PERSON] et al. [31] considered the deformation of the subgrade in transition zones and built a numerical analysis model to identify the dynamic response of the train. From the literature review, it is noted that most research has focused on the influence of subgrade settlement on the mechanical characteristics of slab track. However, so far, few studies have been undertaken on the effects of frost heave displacement on track structure [6; 16; 32], and no control limits referring to the subgrade frost heave have not been proposed. Meanwhile, subgrade frost heave causes track damage, leading to changes in track material parameters. The material parameters of the slab track have a significant effect on the dynamic response of the vehicle [33] and the impact load has a huge influence on the track [34; 35; 36]. When the cooperative deformation of the rail surface caused by the uneven displacement of subgrade frost heave is not controlled, track irregularity will amplify the dynamic response of the train-track system, resulting in an increase in the track's deterioration rate, passenger discomfort and derailment risk [37; 38; 39; 40]. In this study, combined with the concrete plastic damage constitutive theory, a nonlinear damage analysis model of CRTSIII slab track subgrade frost heave was established using the finite element method. Then, the mapping relationship between different frost heave displacements and track irregularities was analyzed. Moreover, according to the interlayer seam relationship of the track structure, a control limit for subgrade frost heave displacement at the millimeter level was proposed. Compared to other existing linear elastic models, the established model takes into account the influence of the concrete plastic material, as well as the pre-stressed and common reinforcements of track structure. This reduces the deviations of simulated results from the real full-scale track structure, i.e., it increases the accuracy of the results. It is expected that this study may provide theoretical guidance for the maintenance of CRTSIII slab tracks in cold areas.
# Performance Comparisons of Broadband Power Line Communication Technologies
## 2 Structures of PLC
### Hs Plc  
In this section, a brief description for the physical layer of HS PLC [6] is given. In Figure 8, a block diagram of the HS PLC physical layer is shown. The binary input is provided to the FEC block. For data frames, different FEC schemes are used according to three operating modes: NORMAL, diversity (DV), and extended diversity (EDV) mode. For NORMAL mode, a concatenated code with Reed-Solomon (RS) and convolutional code is used. The RS code uses a shortened code of (255, 239). The rate of the convolutional code is 1/2 or 3/4 and the constraint length is 7. For signal transmission in HS PLC, discrete multi-tone is used, which is essentially the same as OFDM. Thus, in this paper, it is called OFDM instead of DMT. A symbol block (SB) consists of 16 OFDM symbols. Encoding is performed by each SB unit. Then, the encoded bits of an SB are interleaved with an interleaving matrix of size \(N_{BPS}\times 16\), where \(N_{BPS}\) represents the number of bits in an OFDM symbol. \(N_{BPS}\) depends on the modulation method, which is determined by the channel conditions. In NORMAL mode, encoded bits bypass the diversity mapper. For modulation, differential phase shift keying (DBPSK), DQPSK, or differential 8 phase shift keying (D8 PSK) is used for each subcarrier of OFDM. The selection among the three modulations is made based on the channel condition. Information on the modulations selected for all the subcarriers is called a tone map (TM) and is transmitted to the communicating receivers.  
In DV mode, (20, 12) RS code is used. An input block of 96 bits is encoded, and a 160-bit output block is generated. These output bits go to the diversity mapper directly, and are transmitted by 16  
Figure 8: Block diagram of HS PLC.  
Figure 7: Block diagram of HPGP.  
OFDM symbols. In an OFDM symbol, 124 subcarriers with DBPSK are used and 10 encoded bits are transmitted. Thus, each encoded bit is copied at least 12 times. DV mode is the most reliable communication mode in HS PLC. In the case of EDV mode, (56, 40) RS code is used. This encoder generates 448 bits with a 320-bit input block. These output bits go to the diversity mapper directly, and are transmitted by 16 OFDM symbols as in DV mode. In an OFDM symbol, 152 subcarriers with DBPSK are used, and 28 encoded bits are transmitted, resulting in five copies for each encoded bit.  
The number of IFFT points for OFDM is 512. In HS PLC, CP is also used as in the other PLC technologies and 128 samples are assigned. At both ends of symbols, there are RIs of 16 samples. For the pulse shaping, raised-cosine windowing is used as in the other PLC technologies. The sampling frequency \(f_{s}\) is 50 MHz. The OFDM symbol period is 624 samples long, which corresponds to 12.48 \(\upmu\)s.
## 5 Analytical Study and Model Calibration
### Limitations of the Fiber-Based Finite Element Model
This overestimation can have significant ramifications when the model is used to assess the performance of retrofitting strategies, such as the TnT BRB system examined in this study. In light of these considerations, it is evident that while the fiber-based finite element model provides valuable insights into the global seismic behavior of RC structures, its limitations in capturing the intricate, localized phenomena must be transparently acknowledged. Future research endeavors should aim to integrate hybrid modeling strategies that combine the computational efficiency of the fiber-based approach with more refined localized damage mechanics models. For example, a multi-scale modeling framework could be developed wherein the global response is captured by the fiber model, while critical regions identified as prone to localized damage are modeled using detailed finite element methods that incorporate sophisticated constitutive models and high-resolution discretization. Such an approach would enable the capture of both global structural response and the critical local phenomena that govern failure mechanisms.
## 3 The Application of a Digital Twin to Find Robotic Motion Trajectory  
The digital twin framework utilizes a dynamic simulation model which is created using Tecnomatix Process Simulate software and consists of three-dimensional computer-aided design (3D CAD) objects imported from a Siemens Support Center online library. The model is built in four layers--geometry, physics, behavior, and rule--as suggested by [PERSON] [14]. Any additional components required at the workstation are created in NX as 3D objects and imported into the Tecnomatix environment. The Process Simulate Human package is used to create a digital model of the human worker for ergonomic evaluations, using realistic male and female human figures with deformable mesh technology to accurately represent body shapes [15]. The selected human model has a height of 160 cm, BMI of less than 25, and waist-to-hip ratio (for females).  
This study explores the use of a Kinect sensor to monitor human positions and presence in a workspace, with the goal of using decision trees (DT) in human-robot cooperation (HRC) to track and analyze interference volumes between humans and robots, as well as the frequency of interferences. This information is then used to periodically optimize robot trajectories in order to account for areas where humans frequently enter. By using historical data of human positions, the simulation is able to self-learn and generate robot trajectories that are free from possible human intervention.  
Figure 3 illustrates the images of the real human and robot and their digital counterparts in the digital twin. The system's digital model was built in Tecnomatix software. The red rectangle frame in the image indicates the positions where collision may occur between the human and robot while sharing the workspace.  
Figure 2: A sequence of tasks performed by humans and robots.  
The DT method involves saving key positions for the robot in the simulation model. Once the tasks are identified and robotic tasks are assigned, they are simulated in a virtual environment to generate robot trajectories. Intermediate positions are generated automatically, and the robot avoids possible collisions with equipment. These paths are then saved in an SRC file, which can be loaded to the robot. In cases of dynamic changes, robot instructions are sent in the form of a message that overrides the original robot program [16].
###### Abstract  
The uncertainty of two recently proposed methods, \"new fluid factor\" and \"delta K\", is analyzed under different water saturation and noise conditions through Monte Carlo modelling. The new fluid factor performs reliably (all metric parameters are above 0.9) when the water saturation is up to 95%. The delta K has better performance (all metric parameters are close to 1) such that it is able to distinguish hydrocarbon from brine without the interference of high water saturation. The results prove the performances of the two methods are stable in a high water-saturation scenario. The analysis of noise indicates the methods are sensitive to noise in the input data in that the performance is excellent when the noise is relatively low (\(-\)20 dB) and decreases with increasing noise energy. The new fluid factor, which is in the interface domain, is more sensitive than delta K in the impedance domain. The metric parameters of the new fluid factor and delta K are in the range of 0.5 to 0.8 when the noise is high (\(-\)7 dB). High-quality input data and integration with other geophysical methods can effectively reduce these risks. In addition, two widely used traditional methods (fluid factor and Lambda-Rho) are analyzed as comparisons. It turns out the new fluid factor and delta K have better performance than traditional methods in both high water saturation and noise conditions.  
Keywords:fluid indicator; hydrocarbon prediction; high water saturation; noise; seismic attribute; uncertainty analysis +
Footnote †: journal: Applied Physics Letters
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 4 Reliability Assessment Hierarchy
### Payload  
The payload is not contained in its own conditioned bay inside the fuselage, but is placed outside in a mobile turret inserted directly in the aerodynamic flow. The turret itself contains several electro-optical sensors like a thermal imaging camera, a Low Light Level Television, a laser tracer etc. From a mechanical point of view, the turret is gimballed, allows \(\pm\) 90\({}^{\circ}\) elevation, and 360\({}^{\circ}\) of continuous azimuth rotation; the system also contains the ancillary electronics of the sensors and the movement actuators.  
The turret is thermostatically controlled to ensure optimal operation of the electronics and to prevent freezing of the kinematic devices in high-altitude flight conditions. For these applications, the electronics will be chosen with consideration of their intrinsic reliability and highest temperature operative range. The geometry of the cases of the components must be chosen carefully, as the aircraft is subjected to frequent and abrupt changes in altitude, and therefore, pressure, that could stresssome components. It's important to note that to put all the equipment into a sealed and pressurized container would be too burdensome from the point of view of weight.