esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of Fixed-Bed Adsorption Process  
The experimental breakthrough curve (BTC) was used to calculate the breakthrough time (\(t_{b}\)) and the saturation time (\(t_{s}\)) when the concentration of the dye at the outlet stays approximately constant (i.e., steady state has been reached). The time of breakthrough was calculated as the time at which the dye concentration at the outlet (\(C_{t}\)) reaches 5% of the dye concentration at the inlet (\(C_{0}\)) [1]. Usually, the saturation time is the time at which the dye concentration at the outlet reaches that at the inlet (\(C_{t}/C_{0}=1\)). The adsorption capacity of the PSAC bed (\(q_{bcd}\), mg g\({}^{-1}\)) was determined with numerical integration of the concentration profile (\(C_{t}/C_{0}\)) of the breakthrough curve according to the trapezoidal rule (Equation (1)):  
\[q_{bcd}=\int_{t=0}^{t=t_{perp}}\bigg{(}\frac{C_{0}-C_{t}}{m_{bcd}}\bigg{)}Q\;dt \tag{1}\]  
where \(t_{perp}\) is the total operating time (min), \(m_{bcd}\) is the amount of activated carbon used in the fixed-bed column (g), and \(Q\) is the feed flow (L min\({}^{-1}\)). Other important parameters of the BTC were the total time of the adsorption process (\(\Delta t\), min), the volume of effluent (\(V_{\mathit{eff}}\), L), and the mass transfer zone (\(MTZ\), cm), which corresponds to the adsorbent bed space in which the greatest exchange between adsorbate and adsorbent molecules takes place with time and depends on the rate of adsorption and the inlet flow rate [6; 15; 16]. Furthermore, it allows for the verification of whether the adsorbent is being efficiently used for dye adsorption in a dynamic process, since \(MTZ\) represents the region of the bed where the adsorbate is most efficiently and quickly adsorbed [11]. The parameters were determined from the following equations:  
\[\Delta t=t_{s}-t_{b} \tag{2}\]  
\[MTZ=L\times\frac{(t_{s}-t_{b})}{t_{s}} \tag{3}\]  
\[V_{\mathit{eff}}=Q\times t_{\mathit{oper}} \tag{4}\]  
where \(L\) is the bed height (cm).  
The percentage of dye removal is given by Equation (5):  
\[\% Removal=\frac{q_{total}}{m_{total}}\times 100 \tag{5}\]  
where \(q_{total}\) (mg) is the total mass of dye adsorbed given by Equation (6), and \(m_{total}\) (mg) is the amount of adsorbate accumulated in the column provided by Equation (7) [9]:  
\[q_{total}=\frac{Q}{1000}\int_{t=0}^{t=t_{\mathit{oper}}}\left(C_{0}-C_{t} \right)\;dt \tag{6}\]  
\[m_{total}=\frac{C_{0}Qt_{\mathit{oper}}}{1000} \tag{7}\]  
where \(Q\) is the feed flow (L min\({}^{-1}\)), \(t_{\mathit{oper}}\) is the total operating time (min), and \(C_{0}\) and \(C_{t}\) represent the initial dye concentration and the dye concentration (mg L\({}^{-1}\)) at any time \(t\) (min), respectively.
## 7 Discussion and Conclusions
### Crack Propagation by XFEM/GFEM  
A conceptual representation of the steps involved in a typical crack propagation analysis with the XFEM/GFEM is provided in Figure 41. As should be obvious based on Section 3, enriched finite element methods are essentially discretisation schemes and, as such, require coupling to appropriate criteria in order to model crack propagation. In the present case these are provided by the LEFM framework. The flowchart of Figure 41 involves elastic solution steps followed by the evaluation of a crack propagation criterion. This is common for most LEFM schemes relying on discretizstion, such as for instance FEM or SBFEM. The coupling to further schemes for crack propagation, such as the cohesive zone model, is also possible, in which case the steps of Figure 41 would have to be modified.  
The enriched finite element schemes contained within the XFEM/GFEM family of numerical methods permit the treatment of discontinuities and singularities independently of the mesh, while preserving the convergence rates of the underlying FE method. Hence, conventional meshers are employed, yet enriched node and element sets need to be specified and their contributions to the equilibrium equations need to be assembled. This, apart from introducing additional DOFs associated with the enrichment functions (Equation (19)) and potential conditioning problems, requires the use of more involved numerical integration schemes leading to an increased computational toll. Nevertheless, these operations are only performed on a small part of the domain, thus minimizing this additional cost. As mentioned in Section 3, several techniques are available that allow performing the required tasks in a robust and automated manner.  
For the calculation of the SIFs, elements within the interaction integral domain are identified and their contributions are assembled. A suitable crack propagation criterion is applied in order to evaluate the propagation direction, and together with a user-specified crack propagation increment \(\Delta a\) determine the new crack tip location. Since implicit crack representation has become an almost integral part of enriched finite element methods, the next step would involve the update of this representation. This task might introduce additional challenges, however, significant work has been carried out in this direction, with several methods available for tackling this issue in a simplified manner.
# Jointly Production and Correlated Maintenance Optimization for Parallel Leased Machines
## 6 Conclusions
### Used Notations  
The following notations are used throughout the paper:  
\begin{tabular}{l l}
**Parameter** & **Explanation** \\ \(H\) & Number of production periods \\ \(\Delta k\) & Duration of each production period (months) \\ \(d_{k}\) & Random demand for period \(k\) (\(k=1,2,\ldots,H\)); \\ \(\mu_{k}^{d}\) & Mean of the Gaussian demand during period \(k\) (\(k=1,2,\ldots,H\)); \\ \(\sigma_{d}^{2}\) & Variance of the Gaussian demand \\ \(I_{k}\) & Inventory at the end of period \(k\) (\(k=1,2,\ldots,H\)) \\ \(\beta\) & Service level to be satisfied at each period \\ \(p_{k}\) & Quantity produced by machine \(i\) (\(i=1,\ldots,M_{t}\)) in period \(k\) (\(k=1,2,\ldots,H\)) \\ \(p_{k}\) & Total production quantity during period \(k\) (\(k=1,2,\ldots,H\)) \\ \(\lambda_{ik}(t)\) & Failure rate of a machine \(i\) during a production period k \\ \(\lambda_{n}(t)\) & Nominal failure rate for maximal production level \\ \(A_{n}\) & Average number of failures of the system \\ \(A_{i}\) & Average number of failures of a machine \(i\) \\ \(T_{r}\) & Repair time (preventive maintenance) (hours) \\
**Decision Variables** \\ \(M_{k}\) & Number of machines during period \(k\) (\(k=1,2,\ldots,H\)) \\ \(X_{ik}^{N}\) & Number of time units (hours) that machine \(i\) (\(i=1,\ldots,M_{k}\)) works during period \(k\) (as \\ \(X_{ik}^{S}\) & normal time) (\(k=1,2,\ldots,H\)) \\ \(X_{ik}^{S}\) & Number of time units (hours) that machine \(i\) (\(i=1,\ldots,M_{k}\)) works during period \(k\) (as \\ \(X_{ik}^{S}\) & overtime) (\(k=1,2,\ldots,H\)) \\ \(N_{i}\) & Number of individual preventive maintenance to be performed in a machine \(i\) \\ \(N\) & Number of group preventive maintenance actions during time horizon \\ \(T_{i}\) & Interval between two successive individual preventive maintenance \\ \(T_{n}\) & Interval between two successive group preventive maintenance \\
**Boundaries Variables** \\ \(M\) & Upper boundary of machines number \\ \(m\) & Lower boundary of machines number \\ \(u\) & Number of pieces that a machine can produce per time unit (hour); (the same for \\  & all machines) \\ \(X_{\max}^{N}\) & Maximum of time units (hours) that a machine could be operated during a period as \\ \(X_{\max}^{N}\) & regular time \\ \(X_{\max}^{S}\) & Maximum of time units (hours) that a machine could be operated during a period \\  & as overtime \\
**Production Costs** \\ \(c_{1}\) & Average cost of leasing a machine \\ \(c_{2}\) & Fixed costs of leasing (e.g.: administration costs) \\ \(c_{3}\) & Cost of installation/removal of a machine from period \(k-1\) to period \(k\) \\ \(c_{4}\) & Constant of asymmetry in costs \\ \(c_{\rm o}\) & Operating cost per overtime hour \\ \(c_{\rm n}\) & Operating cost per normal hour \\ \(c_{\rm h}\) & Cost to be paid to hold a produced unit in stock \\ \((a_{1}+a_{2}\times d_{k})\) & Optimal net stock \\
**Maintenance costs** \\ \(\Gamma(t)\) & Total maintenance cost \\ \(M_{p}\) & Unit preventive maintenance cost \\ \(M_{c}\) & Unit corrective maintenance cost \\ \end{tabular}
## 2 Materials and Methods
### Method Validation and Matrix Effect
The matrix effect (ME) was estimated by comparing the slopes of six points on the matrix-fitted calibration curves and the solvent-fitted calibration curves according to the following formula:  
\[\%ME=\left(\frac{slope_{matrix}}{slope_{solvent}}-1\right)\cdot 100 \tag{1}\]  
Depending on the percentage of ME, different matrix effects could be observed: negative--high signal suppression (\(\geq\)50%) and moderate signal suppression (20 to 50%); no matrix effect (\(-\)20 < ME < 20%); positive--moderate signal enhancement (20-50%) and high signal enhancement (\(\geq\)50%) [57; 58]. The measurement uncertainty was estimated basing on the data obtained in the validation study. The relative expanded uncertainty was calculated by using the coverage factor k = 2 at the confidence level of 95% [59].
# A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper
## 2 Broadband Communication  
### Millimeter Waves and Massive MIMO  
In order to attain more bandwidth than traditional frequency bands, 5G and the following 6G mobile networks operate in high-frequency zones [12]. This allows for high-capacity wireless transmission of data at speeds up to several gigabits per second (Gbps) [13]. It is generally agreed that mmwave communication [14] is one of the most crucial technologies for reaching peak data rates of 10 Gbit/s [15]. This is due to the fact that increasing the bandwidth is an effective method for increasing the system's capacity [7] and that there is a lot of bandwidth available in the mmwave channels. Measurement and modeling of mmwave channels [16] and assessment of the band's potential for future wireless systems have received significant academic and industrial attention because of the spectrum's abundance [17]. However, due to negative propagation effects, particularly those brought on by obstructions in the line of sight (LoS), these high frequencies have been used only sparingly up to this point [18]. It is important to handle the greater propagation losses associated with mmwave frequencies, especially beyond 100 m and in non-line of sight (NLoS) environments [19]. On the one hand, signal transmission becomes more difficult in areas with dense foliage and higher propagation loss [20]. Nonetheless, the link could be forced into the low signal-to-noise ratio (SNR) zone due to the constrained send signal power dispersed over a broad bandwidth [21]. To address these problems, several transceiver topologies have been created that steer the received or broadcast beams in the appropriate direction. [22]. The two main technologies that will enable 5G mmwave communication are MIMO and beamforming [23]. Beamforming is a method for using MIMO/array elements to direct the main lobe radiation beam at the transmitters and receivers in the desired directions, eliminating the undesirable spatial selectivity [24]. The beamforming array is widely divided into the following categories: analog, digital, and hybrid beamforming array [25]. Figure 1 shows the block diagram of massive MIMO system.
# The Current Development Status of Agricultural Machinery
## 2 Power System of Agricultural Machinery Chassis
### Hydraulic Power System
[45] proposed a fully hydrostatic-driven intelligent spraying chassis with flexible control capabilities. Their research provides theoretical support for straight-line control of four-wheel independent drive systems and offers a reference for designing high-precision, reliable, and cost-efficient hydraulic synchronization control systems. In conclusion, China has achieved significant advancements in hydrostatic drive technology and intelligent control systems for hydraulic-driven chassis. Nevertheless, several critical challenges remain, including dependence on imported core components, the inherent trade-off between system efficiency and energy consumption, and insufficient electro-hydraulic-mechanical coordinated control. Particularly, the lack of comprehensive system energy equations that integrate hydraulic actuators and suspension systems hinders in-depth analysis of fluid-mechanical-terrain coupling mechanisms and vibration energy transmission and dissipation dynamics. These limitations, combined with inadequate intelligitization, constrain the development of next-generation agricultural hydraulic power systems. To address these challenges, three strategic breakthroughs are essential: (1) The development of high-power-density hydraulic pumps and motors, along with low-leakage valves, should be prioritized. System topology should be optimized through innovations in hydrostatic drive technology to reduce energy consumption while simultaneously advancing the domestic production of core components. (2) A multi-objective optimization framework integrating terrain characteristics, load conditions, and energy consumption should be established using model predictive control (MPC) and reinforcement learning (RL). This will enable coordinated anti-slip control and optimal power distribution. (3) Multi-sensory integration--combining LiDAR, inertial navigation systems (INSs), and tactile feedback sensors--should be implemented alongside digital twin platforms to minimize hydraulic system response latency. Nonlinear disturbance observers should also be employed to compensate for dynamic uncertainties, thereby achieving multi-modal perception and electro-hydraulic-mechanical synergy. Additionally, lightweight design strategies must be emphasized to meet the operational demands of hilly and mountainous terrains. With strong national policy support and continuous technological innovation, hydraulic drive technology has become a key enabler for the modernization of agricultural machinery. Through component-level breakthroughs, algorithm-driven optimization, and system-level integration and validation, advanced hydraulic power systems are expected to play a pivotal role in promoting efficient, intelligent, and environmentally sustainable agricultural mechanization.
## 3 Results and Discussion
### Scattering Parameters  
Scattering parameters, or S-parameters, for each of the MIMO configuration mounted as integrated design were simulated and measured for the understanding and reporting of the practical and simulation characteristics of the design. The Vector Network Analyzer (VNA) used for the measurement of the S-parameters was an Anritsu Shock Line MS46122B. The measurement setup for the S-parameters is reported in Figure 11.  
The measured and simulated S-parameters for both MIMO antenna configurations, including reflection and transmission coefficients, are reported in the form of plots in Figure 12.  
It can be noted, in Figure 12a, that, for the low-band MIMO configuration, the measured reflection coefficients of both antennas correlate with the simulated results; therefore, the measured operational bandwidth of the design is also the same as that of simulation. The slight change in the characteristics is due to the non-ideal fabrication and mounting of the connectors. Similarly, the reflection coefficients for the mmWave antennas are also reported in Figure 12b. Here, it may also be noted that the measured results correlate with the simulated results. The slight change in the measured traces is due to the non-ideal fabrication and other real time impairments. Similarly, the transmission characteristics for the mmWave are reported in Figure 12c,d.  
Here, it may be noted that the minimum isolation achieved is 29.03 dB, which is still high and is indicating good practical isolation characteristics. From the measured results, it may be noted that the effective bandwidth of the antenna, in the practical case, is still the same as that of the claimed simulated bandwidth.
## 3 Analysis of Factors Influencing the Results
### Effect of Contrast and SNR of Slanted-Edge Images on the Slanted-Edge Method  
In this section, we examine the impact of image contrast and SNR on MTF measurements using a slanted-edge angle of \(7^{\circ}\). Low edge contrast can hinder the visual distinction of edges, thereby affecting the stability of MTF results. We analyze images with contrasts of 0.95, 0.85, 0.75, 0.66, 0.55, 0.45, 0.35, 0.25, 0.15, and 0.05. Additionally, various noise levels are introduced to these images to achieve slanted-edge images with SNRs of 100, 90, 80, 70, 60, 50, 40, 30, 20, and 10. Using the algorithms proposed in this paper, we explore how image contrast and SNR influence the slanted-edge method. The simulation results are presented in Figure 15.  
Figure 14: Nyquist MTF versus sampling step curve.  
Figure 15: Nyquist MTF at different contrast and SNR.  
The presence of noise in the edge image inevitably leads to fluctuations in the MTF curve. As illustrated in Figure 15, random noise significantly impacts MTF calculations. A decrease in the image's SNR results in an increase in the RMSE, leading to a rapid decline in calculation accuracy. Specifically, when the SNR falls below 30 dB, the calculated RMSE of the MTF exceeds 0.2, markedly increasing the likelihood of MTF aliasing. Consequently, the reliability of the measured MTF under these conditions is considerably diminished. In the absence of noise interference, the contrast of the images on either side of the edge marginally affects the accuracy of the MTF calculations. This is evident from Figure 15, which demonstrates that even when the contrast of the gray values on both sides of the edge is reduced to 0.3, the MTF calculation still retains high accuracy. However, as random noise increases, images with higher contrast demonstrate a superior ability to resist noise interference, thereby maintaining higher calculation accuracy.  
Consequently, for optimal measurement results, it is essential to maintain an image contrast of at least 0.3 and ensure that the image's SNR exceeds 30.
## 5 Axial Piston Pump Simulation Test
### Comparing the Detection Performance of Pressure and Vibration Signals  
To further validate the relative stability of the pressure signals compared to the vibration signals, a comparison was made with the vibration acceleration signals collected using a vibration accelerometer on the end cap of the axial piston pump. For the three channels of vibration signals collected, it was observed that using the z-direction vibration data yielded better results in the analysis of the signals under abnormal conditions. Figure 16 shows the time domain waveforms of the original vibration signals obtained from the axial piston pump under four distinct working conditions.  
Figure 16: Time domain waveform plot of raw vibration signals: (**a**) normal; (**b**) swashplate wear; (**c**) sliding shoe wear; (**d**) single-plunger loose shoe.  
Figure 15: Effect of parameters.  
Each sample contained 259 data points obtained using the DTW algorithm for data partitioning. The partitioning of the samples under normal operating conditions resulted in 192 samples with a training-to-testing dataset ratio of 3:1, yielding 144 samples for training and 48 samples for testing. After completing data partitioning for the three abnormal operating states, 48 random samples were selected as the target abnormal samples. The results are presented in Table 6. Anomaly detection was performed using the DTW-RCK-IF composite method and the results were compared with those obtained from the pressure signals, as listed in Table 7.  
The results indicate that when vibration signals were used for anomaly detection under four different operating conditions, the average precision was 95.59%, the average recall was 98.44%, and the average F1 score was 96.92%. In comparison, when pressure signals were used for anomaly detection, the average precision increased by 2.63%, the average recall increased by 0.95%, and the average F1 score increased by 1.87%. This suggests that pressure signals are more stable and less susceptible to external factors in many situations. Therefore, the pressure signals yielded superior results for the axial piston pumps when employing the DTW-RCK-IF composite method for anomaly detection on the raw data.
## 4 Definition of the Theoretical Process Parameters
### Combustion and Reduction Temperature  
Temperatures of the processes to obtain syngas from biomass have an impact on the composition of the produced syngas because there is a direct relation between temperature, components' concentration, and the conversion efficiency. During experiments, measurements of temperatures in the reduction and combustion zones of the gasifier were recorded (Figure 4), demonstrating that these two parameters are directly correlated. Conversely,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**TEST 1 (150 min)**} & \multicolumn{3}{c}{**TEST 2 (117 min)**} \\ \cline{2-7}  & **Initial** & **Final** & **Difference** & **Initial** & **Final** & **Difference** \\ \hline Pellets (kg) & 39.60 & 19.50 & 20.10 & 42.05 & 29.05 & 13.00 \\ Char pellets (kg) & 3.15 & 4.35 & \(-\)1.20 & 2.40 & 3.60 & \(-\)1.20 \\ Total Biomass (kg) & 42.75 & 23.85 & 18.90 & 44.45 & 32.65 & 11.80 \\ \hline  & Average & Min & Max & Average & Min & Max \\ \cline{2-7} Air flow (Nm\({}^{3}\)/h) & 11.17 & 8.00 & 16.07 & 10.50 & 8.00 & 15.06 \\ Biomass consumption (kg/h) & 7.56 & & & 6.05 & & \\ Air/ Biomass (Nm\({}^{3}\)/kg) & 1.48 & & & 1.74 & & \\ Air intro/Stoich. Rate (\%) & 31.80 & & & 37.3 & & \\ \hline \end{tabular}
\end{table}
Table 3: Experiment conditions for tests 1 and 2.  
the combustion and the reduction temperatures were analyzed as a function of the inlet air flow.  
Previous studies [38; 39] demonstrated that, for the proper gasification of lignocellulosic biomass in a downdraft gasifier, the combustion and reduction temperatures should be in the range of 800-1000 \({}^{\circ}\)C and 500-700 \({}^{\circ}\)C, respectively (see Figure 5). During tests, these values were validated.
_Applied_  
_sciences_  
_Article_  
Structural Behaviour of Axially Loaded Concrete-Filled Steel  
Tube Columns during the Top-Down Construction Method  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
1 Faculty of Civil Engineering, The University of Belgrade, Bulevar Karaja Aleksandra 73, 11000 Belgrade, Serbia; [EMAIL_ADDRESS] (J.Z.N.); [EMAIL_ADDRESS] (S.M.K.)  
2 Boeoexpert Design Ltd., Ruzvelitova 23, 11000 Belgrade, Serbia; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS]  
**Abstract:** The paper discusses the structural behaviour of concrete-filled steel tube columns (CFT) when applied to the top-down construction method as temporary internal supports for ceilings. Their ultimate capacity to take into account the actual boundary conditions of the column is essential for successful application in construction. The paper presents the full-scale in situ testing of four slender specimens with variable D/t ratios under concentric axial loading. The CFT columns were supported on the previously jacked concrete piles. In addition, detailed finite element numerical models in ABAQUS and PLAXIS computer programs were developed. The models include the nonlinear behaviour of materials and the nonlinear behaviour of soil. The soil-pile-column interaction and impact of the CFT column-pile connection stiffness on global column stability were considered. The numerical model was validated by comparison with the experimental results. In conclusion, the coefficient for the effective buckling length of the studied columns is proposed. Finally, the experimental results of the critical buckling forces were compared with widely used international design codes Eurocode 4-EC4, American standard-ACI and the Australian standard-AS.  
**Keywords:** concrete-filled steel tube; top-down construction method; experimental investigation; FEM modelling; buckling force; effective buckling length