esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Regulation and Competition between SCBFs  
As SCBFs share structural similarity, it is not surprising that they bind RNAP in a similar way. For example, DksA and GreA/GreB compete and act antagonistically on some promoters [15,56]. This situation raises the problem of how the binding of several mutually interchangeable factors, which have different activities, is regulated. In _E. coli_ DksA is the most abundant SCBF in the cell and its levels do not seem to depend on growth rate [57]. GreA concentration is three times, and GreB 10 times, less than that of DkSA, with Rnk concentration close to that of GreB [18,57].  
One level of regulation of the SCBFs' activities may be bound to growth conditions. For example, DkSA, which acts synergistically with ppGpp, may fully manifest its function in stationary phase, when ppGpp is produced. In contrast, TraR, which does not require ppGpp, may perform its functions in the exponential phase of growth. In addition, levels of SCBF can change in response to specific conditions, for instance due to different types of stress [58, 59, 60, 61]. However, this control mechanism may not be enough, given that GreB, the least abundant of the studied _E. coli_ SCBFs, is present in the same quantity per cell as RNAP [57]. Consequently, at each moment in time, all RNAPs in the cell may be accompanied by one or the other SCBF. Furthermore, it has been observed that Gre factors may stay permanently bound to RNAP and the elongation complexes [28,62], that is, they may act as a subunit of RNAP, rather than its accessory factor (Figure 1C).  
There is evidence that different SCBFs act on specific conformational or functional states of the RNAP [63], which may explain their controlled interchange. For example, _Thermus aquaticus_ GreA has been shown to enter the secondary channel and substitute for the TL during misincorporation and backtracking, but not in active elongation complexes [37]. After the cleavage reaction is complete, Gre apparently withdraws from the secondary channel to allow continuation of RNA synthesis. This functional switch of Gre may involve conformational changes of the factor (Figure 1C), analogous to those recently proposed for the eukaryotic analog of GreA, TFIIS [64]. Indeed, at least two alternative conformations have been observed for GreA and its homologue Gfh1 [33]. DkSA was proposed to recognise some paused elongation complexes [65] and, possibly, complexes that are about to undergo misincorporation ([29], see below). Thus, subtle conformation changes in the elongating RNAP in response to various signals may control the interchange of SCBFs in its active center. For example, bacterial species that encode DksA also possess large insertions in the TL (SI3) [63]. Alternative conformations of SI3 may provide discrimination between recruitment of DksA and the other SCBFs [63].
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 5 Experimental Results and Analysis
### Comparative Experiment
This indicates that the proposed spatial attention mechanisms and dual-view fusion strategy effectively enhance pose estimation accuracy along these axes. In contrast, error variability in the depth dimension is notably higher, especially around central body joints such as the spine, hips, and shoulders (e.g., keypoints 9-15). This can be attributed to the limited radial resolution of mmWave FMCW radar, as well as interference from multipath propagation and self-occlusion. Despite these challenges, the average localization error across all joints remains consistently low, demonstrating the model's robustness for full-body 3D pose reconstruction. These results validate the mmHSE framework's accuracy in anatomically relevant spaces and further emphasize the importance of improving depth modeling as a critical direction for future radar-based human pose estimation research. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Data Representation**} & \multirow{2}{*}{**Training Set**} & \multirow{2}{*}{**Test Set**} & \multicolumn{4}{c}{**MAE (cm)**} \\ \cline{5-6}  & & & & **Horiz.** & **Vert.** & **Depth** & **Avg.** \\ \hline RF-Pose3D [21] & Heatmap & 127,080 & 423,360 & 4.9 & 4.0 & 4.2 & 4.37 \\ RPM [22] & Heatmap & 278,480 & 69,620 & – & – & – & 5.71 \\ mmPose [20] & RGB Image & 32,000 & 1700 & 7.5 & 2.7 & 3.2 & 4.47 \\ HuPR [23] & VRADE Heatmap & 115,800 & 12,600 & – & – & – & 6.82 \\ mmHSE & Heatmap & 27,000 & 3000 & 2.24 & 2.75 & 3.36 & 2.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of related work based on MAE evaluation index.
## 2 System Model  
As illustrated in Figure 1, we consider a downlink IRS-assisted MU MISO photon-counting communication system. In this scenario, optical signals are absorbed or reflected by obstacles such as buildings, which weakens the signal strength or changes the signal transmission path, leading to signal interruption or the degradation of communication quality. Therefore, we introduced IRS to facilitate NLOS transmission, using the reflection and refraction principles of light to guide optical signals to the target receiver and thereby bypassing buildings. Optical IRS can be physically implemented based on the design of reflector arrays or metasurfaces, and through reasonable configuration with the transmitting LED and receiving PD, a stable and reliable signal transmission link can be formed. In our proposed system, the base station (BS) is equipped with \(M\) LEDs to transmit information to \(K\) users, each of whom has a single PD for signal reception.
## 4 Results
### Characteristics of Rare Earth Elements  
The analysis and test results of rare earth elements and relevant calculation parameters are shown in Table 2. It can be seen that the total amount of rare earth elements (\(\Sigma\)REE) of the three wells in the J\({}_{2}\)z\({}_{1}\) Formation in Wudun sag varies greatly: 144.4 \(\times\) 10\({}^{-6}\)-245.5 \(\times\) 10\({}^{-6}\), with an average of 192.9 \(\times\) 10\({}^{-6}\) for Well XC1, 165.5 \(\times\) 10\({}^{-6}\)-331.8 \(\times\) 10\({}^{-6}\), with an average of 234.2 \(\times\) 10\({}^{-6}\) for Well D2, and 69.7 \(\times\) 10\({}^{-6}\)-151.2 \(\times\) 10\({}^{-6}\), with an average of 103.2 \(\times\) 10\({}^{-6}\) for Well D1. The ratio of LREE/HREE is 6.9-19.4, of which Well XC1 is 8.6-10.6, with an average of 9.3, Well D2 is 6.9-11.5, with an average of 9.6, and Well D1 is 9.0-19.4, with an average of 13.0. Well XC1 and Well D2 show moderate differentiation characteristics of light and heavy rare earth elements, while Well D1 shows high differentiation characteristics of light and heavy rare earth elements.  
In the UCC\(-\)normalized rare earth element spidergrams, Well D2 and Well XC1 show relatively consistent distribution characteristics, showing obvious \"V\" right dip characteristics of light rare earth element enrichment and heavy rare earth element flatness, with Eu as a negative anomaly. Well D1 shows the characteristics of light rare earth element enrichment, heavy rare earth element loss and slight positive Eu anomaly. It reflects that Well D1 is different from Well D2 and Well XC1 (Figure 6), which is consistent with the analysis results of major and trace elements.  
Figure 5: UCC\(-\)normalized trace element spidergrams of the samples.
## 4 Performance Evaluation
### Results and Discussion
#### 4.3.1 ALCOA+ Value Regression  
Based on the available data we trained a regression model, as described in the previous section, and then we used this model to aid the training of the predictive model. The performance of this latter model, was subsequently compared against the following state-of-the-art time-series processing network types:  
* GRU [30]
* LSTM [31]  
The results obtained by this performance evaluation are shown in Table 4. For training and evaluation, k-fold cross-validation leave-one-out (k = 10) was used. The values reported are the average values obtained across all folds from all runs.  
The best results were achieved using the proposed model on both datasets across all ALCOA+ principles. In particular, while the GRU- and LSTM-based models achieved more or less similar performance, the ALCOAi model outperformed them with a safe margin in almost all of the cases. The lowest performance gap was observed in the regression result of the legible principle, while the largest one was observed for the contemporaneous principle.
# Development of a Virtual Reality-Based System for Simulating Welding Processes
## 2 Materials and Methods
### Systematic Procedure for Developing the Virtual Reality-Based System
#### Development of Module #1  
The central control circuit block MCU #1 was responsible for controlling the infrared radiation source and controlling the stepper motor to shorten the welding rod. The architecture of module #1 is shown in Figure 3. MCU #1 received the control signal from the computer via the RS485 port using the Modbus remote terminal unit (RTU) communication protocol block, from which it processed and outputted different radiation levels (infrared-emitting) and welding rod-shrinking speeds (moving electrode wire). The architecture of MCU #1 is shown in Figure 4.  
Figure 3: Architecture of module #1.  
For controlling the infrared radiation source, the signal from channel #9 (PWM_M) was used, and for controlling the stepper motor, the signals from channels #6 and #7 were used. Channel #9 was connected to the infrared transmitter attached on the welding torch.  
MCU#1 controlled the amount of IR by changing the voltage for the infrared 850 nm LED. This LED, considered as the IR emitter, operated between 840 and 850 nm and worked well for generic IR systems with emitting angles of 30 degrees and utilizing a power source of DC 1.4-1.6 V and 20 mA.  
The dimensions of the LED infrared 850 nm are shown in Appendix A. The function for controlling the IR emitter is detailed in Appendix B.  
The principle for MCU#1 controlling the IR levels as shown in Figure 5 is as follows:  
The MCU#1 provided variable PWM, through the resistor-capacitor (RC) filter, which is an electric circuit composed of resistors and capacitors to create a DC voltage for operational amplifier 2 (OPAM_2). The output voltage was fed back through the operational amplifier 1 (OPAM_1), which was then fed to the inverting input of OPAM_2. OPAM_2 performed voltage integration. If the output voltage dropped, resulting in a noninverting input with a voltage lower than the input pulse width modulation (PWM) voltage, then the OPAM_2 output decreased, causing the Vref pin reference voltage of the linear monolithic-2596 (LM2596) to decrease and the linear monolithic (LM2576) to increase in pulse width to compensate. Thus, the output voltage of LM2596 was always kept stable corresponding to the fixed PWM from the output of MCU#1.  
Channels #20 and #21 of MCU#1 were used for connecting to computer via RS485 port. Circuit diagram of the communication block for connecting between MCU #1 and computer is shown in Figure 6 where channels RS485_OUTB and RS485_OUTA were used for connecting RS485 port with computer; channels RX_485, DK_RS485, and TX_485 were used for connecting RS485 port with MCU #1. The function for communicating the computer via the RS485 port is detailed in Appendix C.  
Figure 4: Circuit diagram of MCU #1.
## 3 Results and Discussion
### Adsorption Studies
#### 3.2.6 FTIR Analysis  
To identify functional groups responsible for the adsorption activity, FTIR spectra of the two plants and their waste products were analyzed before and after Cu sorption (Figures 11 and 12). The spectra of LA and MO are similar, showing broad IR bands due to overlapping a huge number of bands associated with the vibrations of the various structural units building the rich multicomponent composition of the plants. Despite this similarity, differences in band positions and intensities reflect compositional variation. Our previous work on _Anethum gravelens_ L. provides detailed insights into such spectra, closely resembling those of MO and LA [20]. For this study, the focus is on the functional groups involved in Cu sorption.  
The IR spectra confirm the presence of oxygen-containing functional groups such as (i) Hydroxyl groups from different phenolic and aliphatic structures (~3400 cm\({}^{-1}\)); (ii) Carboxylic groups, esters, and ketones (e.g., bands at 1733 cm\({}^{-1}\), 1630 cm\({}^{-1}\), and 1520 cm\({}^{-1}\)); and (iii) Functional groups associated with aromatic and aliphatic esters and ethers, as well as phenolic OH groups and alcoholic C-OH groups--bands between 1320 and 1020 cm\({}^{-1}\). Notably, essential oil extraction causes minimal spectral changes  
Figure 11: FTIR spectra of MO and waste material M and after Cu(II) sorption: MOS and MS, respectively. In the spectra of M, MOS, and MS. Only the bands with frequency changes in comparison with M are explicitly indicated with the corresponding numbers. The dash-dot lines note the bands that undergo a change in both frequency and intensity.  
in waste materials (MS and LS), leaving functional groups intact for Cu ion interaction (Figures 11 and 12). This can be expected considering the small amount of the essential oils present.  
Some spectral changes occur in marked regions (R1-R4) after Cu sorption (Figures 11 and 12). Bands around 1628 cm\({}^{-1}\) (R1) and 1260 cm\({}^{-1}\) (R3) shift slightly (between 4 and 10 cm\({}^{-1}\)), while intensity changes are observed around 1075 cm\({}^{-1}\) (R4). Moreover, only in the case of MOS, two additional spectral features can be observed: A decrease in intensity for the bands at 1410 cm\({}^{-1}\) (R2) and 1048 cm\({}^{-1}\) in the R4 range (Figure 11). This suggests that in MO, more functional groups, including alcoholic, ether, and ester groups, interact with Cu(II) ions. These interactions likely explain the higher adsorption activity of MO compared to LA (e.g., 100% for MO vs. 68% for LA).  
It can be seen that carboxylate, phenolic, alcoholic, ether, and ester groups contribute to Cu\({}^{2+}\) biosorption on MO and LA and their wastes. MOs higher adsorption correlates with its broader spectral changes, while waste materials retain sufficient functional groups for effective sorption (80% for MS and 70% for LS).
## 5 Field Boundary Detection  
The boundary of work area is calculated from the collected data. The GPS coordinates from machinery usage may include all the points showing agriculture machinery movement. To get the field shape, we need to identify the boundary points only. The work area field could be regular or irregular in shape. The boundary detection for the regular shaped field is easy to calculate, while the irregularly shaped field is relatively difficult. After studying several methods, including Delaunay Triangulation and concave hull, we were able to find the best algorithm for boundary detection. An overview of each algorithm is presented here. The detailed comparison of algorithms is presented in evaluation (Section 7) of the paper.
## 3 Results and Discussion
### Fracture Mechanism  
To demonstrate the fracture mechanism of CF-failed specimens, Figure 10 displays the fractographies of the welded and BM specimens with short and long holding times, respectively. Fatigue striations were observed in all specimens, as shown in Figure 10a-d. Compared with the welded specimens, more pronounced fatigue striations were produced in the fracture surface of the BM specimens, indicating that the more severe fatigue damage was induced in the BM specimens during the main-fatigue-crack propagation. This deduction can be proved by the shorter cyclic lifetimes at the third stage (high propagation rate) for the BM specimens (Figure 5a).  
Figure 10c,d show the fractographic images of the BM and welded specimens under creep-fatigue loading with holding times. The creep voids and dimples were clearly seen in the welded specimens with long holding times, as well as the BM specimens. It should be noted that more creep voids were observed in the fracture surfaces of the welded specimens. It is widely accepted that the creep voids are formed at the grain boundary with  
Figure 10: Fractographies of the BM specimens under creep-fatigue loading with (**a**); short and (**c**) long holding times. Fractographies of the welded specimens under creep-fatigue loading with (**b**) short and (**d**) long holding times.  
high stress concentration during the holding periods [5,6]. The higher values of \(\sigma_{\text{th}}\) could result in higher stress levels in the welded specimens during the holding period (900 s), so as to produce more severe creep damage at the grain boundaries. It should be noted that although the stress levels of the welded specimens with holding times of \(120,300\) and \(600\) s were higher than the BM specimens, their stress levels were still less than the threshold value of creep stress (90 MPa [20]). As the stress was lower than this threshold value, the effect of creep deformation on stress relaxation gradually diminished [20]. Thus, the cyclic life of those welded specimens were slightly higher than the BM specimens. However, the stress level of the welded specimen with a holding time of 900 s was 110 MPa, which is higher than the threshold value of creep stress (90 MPa [20]). The more severe creep damage inside the welded specimens with a holding time of 900 s can significantly reduce their cyclic life (548), which is shorter than the cyclic life (948) of the BM specimens. The observation of the fracture surface also proves that the fracture mechanisms of both kinds of specimens shifted from fatigue-dominated to creep-fatigue-interactive damage with increasing holding time. Since numerous creep voids were formed inside the P92 steel and welded joint, the period of the main-crack propagation was reduced, which led to the absence of a third cyclic softening period. Therefore, the fracture mechanism could be deduced to have shifted from fatigue-dominated damage to creep-fatigue interaction with increasing holding time (Figures 5 and 10).
## 2 Materials and Methods
### Grating-Based PCI  
Typical laboratory grating-based PCI setup feature typically a high-power, macro-focus laboratory X-ray source and a G0 source gratings to increase the spatial coherence of the radiation and the usual G1 and G2 gratings forming the Talbot-Lau interferometer [10; 12; 13; 33]. To obtain the attenuation, the differential phase-contrast and dark-field images simultaneously, mostly a phase-stepping approach, usually moving the G1 grating cross-wise in small, equidistant steps is used on such Talbot-Lau setup. The Fourier decomposition of the phase stepping curve approximates it up to the second term as:  
\[I(\mathbf{r},\chi)=a_{0}+a_{1}sin(\frac{2\pi\chi}{p}+\phi) \tag{7}\]  
where \(\chi\) is the phase step position and \(p\) is the grating period of the grating being stepped. This is a sound assumption for rectangular gratings of a reasonable quality. It requires that the phase stepping corresponds to one or several full periods of the grating that is analysed. The mean and amplitude and phase factors in Equation (7) are obtained by pixel-wise fast Fourier transform (FFT) of the stepping images. Then three image modalities are defined as:  
\[A(\mathbf{r})=\frac{a_{0,s}}{a_{0,ref}} \tag{8}\]  
\[DPC(\mathbf{r})=\phi_{ref}-\phi_{s} \tag{9}\]  
\[DFI(\mathbf{r})=\frac{a_{1,s}a_{0,ref}}{a_{1,ref}a_{0,s}} \tag{10}\]  
where \(A\), \(DPC\) and \(DFI\) are the attenuation, differential phase contrast and dark-field projection images, respectively and the subscripts \(ref\) and \(s\) are for empty beam reference and for sample images. To obtain the CT images of \(\delta\) of the sample from the DPC images, instead of the usual apodization filters, such as Ram-Lak and Shepp-Logan filters [25] for attenuation CT, a Hilbert-filter is used [10]. Note that for reconstructing both the the phase-contrast and dark-field CT, the option of taking no logarithm of normalised images is built in the package.  
Besides the Fourier decomposition, we have also implemented a direct least-squares fit to determine the Fourier components in Equation (7) [33]. In contrast to the Fourier method this method does not need equidistant sampling of the phase stepping curve in Equation (7) in \([0,2\pi]\). For this, Equation (7) is linearized as  
\[I(\mathbf{r},\chi)=a_{0}+b_{1}sin(\frac{2\pi\chi}{p})+b_{2}cos(\frac{2\pi\chi}{ p}) \tag{11}\]  
where  
\[a_{1}=\sqrt{b_{1}^{2}+b_{2}^{2}} \tag{12}\]  
and  
\[\phi=\frac{b_{2}}{b_{1}} \tag{13}\]  
The three parameter \(a_{0,i},b_{1,i},b_{2,i}\) for pixel \(i\) are efficiently determined by least-square matrix inversion using the Penrose pseudo inverse, e.g., in MATLAB. Both methods deliver very similar results in general as is shown in [PERSON] et al. [33].
# Frost Resistance Prediction of Concrete Based on Dynamic Multi-Stage Optimisation Algorithm
## 2 Method  
### Ensemble Learning  
Ensemble learning is a method of combining multiple basic models to create robust and accurate models [20], mainly by building and combining multiple learners to solve a single prediction problem. The core concept of the ensemble method is that by combining the predictions of multiple models, it can improve the accuracy and robustness of the prediction, reduce overfitting, and improve the generalisation ability of the model. Ensemble learning is primarily divided into two methods, namely, bagging and boosting. Figures 1 and 2 illustrate its schematic diagrams.  
Figure 1: Schematic diagram of Bagging.  
Figure 2: Schematic diagram of boosting.  
Bagging is a parallel integration method that solves the scattering problem of prediction models by including additional training data [21]. It is primarily used to reduce the variance of model prediction, thereby improving its stability and accuracy. It adds robustness to the final decision by creating multiple models' integration'; a typical example is the random forest algorithm [22]. In boosting, which trains multiple weak learners in series, each model attempts to correct the errors of the previous model. The key idea of boosting is to combine multiple models with weaker performance into a powerful overall model. Its typical representatives are XGBoost, AdaBoost, and CatBoost.  
Bagging emphasizes training multiple models in parallel and averaging their prediction results, which can effectively reduce the volatility of high-variance models. This method is particularly suitable for engineering scenarios such as concrete frost resistance prediction, where issues like measurement errors, data disturbances, or sample imbalance exist, and it can enhance the stability and robustness of the model. Boosting, however, adopts an iterative approach to gradually optimise the residual errors of the previous round of models, making it more applicable to regression tasks with uneven distribution of feature importance and high levels of noise. In the modelling of nonlinear frost resistance indicators involved in this study, boosting can more effectively explore the marginal contributions of weak variables, thereby significantly improving the overall prediction accuracy and model performance.