esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Seeding Performance of Self-Propelled Industrial Hemp Seeder
## 3 Parameter Design and Analysis of Critical Components of Seed Metering Device
### Parameter Design of the Metering Wheel
#### 3.2.3 Speed of Seeding Wheel  
The filling effect of the seed metering device directly affects its seeding performance. Whether the seed falls into the hole smoothly depends on the relative motion between the seed and the metering wheel. The speed of the metering wheel will affect the filling effect. The seed filling process is a dynamic system consisting of the gravity of the seed itself, the inter-species force, the positive pressure between the seed and the metering wheel, the sliding friction force, and the rolling friction force. It is a complex process to separate the seed from the population. In order to obtain the theoretical value range of speed of the seed metering wheel, the expanded kinematic analysis of the seed filling process is shown in Figure 5.  
The Formulas (7) and (8) should be satisfied between the hole's limit relative velocity and the rotational speed when the seed is in the \(X\)-axis's transverse direction and the \(Y\)-axis's vertical direction [32].  
\[v_{\omega}=v_{r}\bigg{(}d-\frac{L_{\max}}{2}\bigg{)}\sqrt{\frac{g}{L_{\max}}} \tag{7}\]  
\[\omega=\frac{2\pi n}{60} \tag{8}\]  
Comprehensive Formulas (6)-(8) The value formula of the rotational speed of the metering wheel (9)  
\[n\leq\frac{30}{\pi R}\bigg{(}\bigg{(}d-\frac{L_{\max}}{2}\bigg{)}\sqrt{\frac{ g}{L_{\max}}}\bigg{)} \tag{9}\]  
where \(n\) is the rotational speed of the metering wheel, r/min; \(r\) is the radius of the metering wheel, mm.  
The diameter of the metering wheel was 60.0 mm, the diameter of the hole was 6.7 mm \(\leq d\leq 13.0\) mm, and the maximum length of the seed was 4.2 mm, which was substituted into Formula (9) to obtain 69.7 r/min \(\leq n\leq 155.1\) r/min.
## 3 Materials and Methods
### Determination of Anti-Inflammatory Properties  
#### 3.7.1 Assessment of Inhibition of Protein Denaturation  
The anti-inflammatory properties of the tested hydrogels with CBD and TER were assessed using the methodology described by [PERSON] et al. [86]. As part of this test, the possibility of inhibiting the denaturation of bovine serum albumin (BSA) by hydrogels in the concentration range of 0.1, 1.0, and 5.0% was assessed. Briefly, 1000 uL of individual concentrations of hydrogel extracts (H-CON, H-CBD, H-TER, and H-CBD-TER) was mixed with 450 uL of 5.0% aqueous BSA solution and 1400 uL of phosphate-buffered saline (PBS, pH 6.4). The mixtures prepared this way were incubated at 37.0 \({}^{\circ}\)C for 15 min. The samples were then heated at 70.0 \({}^{\circ}\)C for 5 min, followed by cooling in an ice bath to 25.0 \({}^{\circ}\)C. Then, the absorbance of the tested samples was measured at \(\lambda\) = 660 nm using the DR600 UV-Vis spectrophotometer ([PERSON], Wroclaw, Poland). A diclofenac concentration of 500 ug/mL was used as a positive control. Three independent experiments were performedas part of the analyses, and each sample was tested in triplicate. The inhibition of protein denaturation by the tested hydrogels was calculated from the following equation:  
\[\%\text{inhibition of denaturation}=\frac{1-\text{As}}{\text{Ac}}\times 100\% \tag{9}\]  
where the following definitions hold:  
As is the absorbance of the tested sample;  
Ac is the absorbance of the control sample.
# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 3 Proposed SDR-Based LTE-LAA Architecture and Its Resource Management Scheme
### SDR-Based Base Station Communication Model  
The BS transmission power is denoted as \(\mathcal{P}_{i}\), and the gain of the channel between a BS \(i\) and a user \(j\) is denoted as \(g_{ij}\). Meanwhile, \(\sigma^{2}\) denotes the noise power. Hence, the signal's SINR from a BS \(i\) toward a user \(j\) can be given by  
\[\alpha_{ij}=\frac{\mathcal{P}_{i}\cdot g_{ij}}{\sum_{v\in\mathcal{I},v\
eq i} \mathcal{P}_{v}\cdot g_{iv}+\sigma^{2}} \tag{1}\]  
In this study, we adjust the modulation and coding scheme (MCS) after evaluating the behavior of the channel based on \(\gamma\) (channel attenuation factor). This approach is known as the maximum throughput channel state [49]. We use this approach to achieve the maximum throughput by adapting the MCS in accordance with the SINR value. This is a suitable approach for streaming and conversational traffic [50].  
In a Rayleigh channel, the error probability in a symbol can be calculated as follows [51]:  
\[P_{e}=\frac{4(\sqrt{m}-1)}{\sqrt{m}}\cdot Q\Bigg{(}\sqrt{\frac{3\cdot\alpha_{ ij}\cdot\gamma^{2}}{(m-1)}}\Bigg{)}-\ \left[\frac{2(\sqrt{m}-1)}{\sqrt{m}}\cdot Q\Bigg{(}\sqrt{\frac{3\cdot\alpha_{ ij}\cdot\gamma^{2}}{(m-1)}}\Bigg{)}\right]^{2} \tag{2}\]  
where \(m\) denotes the modulation order.  
The bit error probability \(P_{e}^{b}\) is a function of \(P_{e}\). Hence, it can be expressed as follows:  
\[P_{e}^{b}\cong\frac{P_{e}}{log_{2}(m)} \tag{3}\]  
By assuming that the fading is independent from one frame to another and utilizing the maximum likelihood algorithm, the probability of receiving an error-free block can be calculated as follows [51]:  
\[P_{ef}^{bll}=\sum_{q=0}^{Q}\binom{W}{q}P_{e}^{bq}(1-P_{e}^{b})^{W-q} \tag{4}\]  
where \(W\) denotes the codeword width and \(Q\) is the number of bits. Hence, the block error probability can be calculated as follows:  
\[P_{e}^{bll}=1-P_{ef}^{bll} \tag{5}\]  
Accordingly, the max data rate (in bps) between the BS \(i\) and user \(j\), based on the channel condition, can be derived as follows:  
\[D_{ij}^{max}=\frac{N_{i}^{c}\cdot N_{symb}\cdot R_{w}\cdot log_{2}(m)}{T_{fr}} \cdot\left[1-P_{e}^{bll}\right] \tag{6}\]where \(N_{c}^{i}\) is expressed as  
\[N_{i}^{c}=N_{l}^{i}+N_{u}^{i} \tag{7}\]  
where \(N_{i}^{c}\) presents the total number of assignable subcarriers at BS \(i\), \(N_{l}^{i}\) depicts the number of subcarriers in the licensed band, \(N_{u}^{i}\) represents the number of subcarriers in the unlicensed band, \(N_{symb}\) is the number of symbols, \(R_{w}\) is the code rate of the used MCS, and the frame time duration is denoted by \(T_{fr}\).
## 3 Results and Discussion
### Post-Correction Performance
Besides, compared to the SLR model, the AutoML model also showed a better performance than the MLR model, as exhibited in Figure 11. The post-correction improvement due to AutoML compared to SLR was \(-0.17\)\({}^{\circ}\)C and \(-1.54\%\) for T and RH RMSE values, respectively; this was more significant than the improvements of of 0.04 \({}^{\circ}\)C and \(-0.56\%\) caused by MLR. Figure 6: Forecast performance of raw forecast and post-correction results for: (**a**) temperature and (**b**) relative humidity. Figure 8: Difference between the RMSE values of post-correction forecasts for: (**a**) temperature and (**b**) relative humidity by AutoML model and random forest model. Figure 7: Difference between the RMSE values of post-correction for (**a**) temperature and (**b**) relative humidity by AutoML model fitted by previous month in the same year (2019) and historical data in last year (2018). Figure 10: Difference between the RMSE values of post-correction forecasts for: (**a**) temperature and (**b**) relative humidity by AutoML model and moving error post-correction method. Figure 9: Difference between the RMSE values of post-correction forecasts for: (**a**) temperature and (**b**) relative humidity by AutoML model and traditional linear MOS method. Results evaluated with the bootstrap method also indicated a better performance of the AutoML in most stations, as indicated by Figures 12 and 13. However, it should be noted that post-correction performance was evaluated with one month of data, which could have limitations in terms of representing long-term performance, especially when in relation to climate events such as the El Nino-Southern Oscillation.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 5 Simulation and Result
### Limitation  
In comparison to the K-SP strategy, our K-Bottleneck Minimize algorithm achieves a marked reduction in overall computational effort; however, it still incurs greater overhead than the K-DG approach. This limitation stems from the requirement that, whenever a  
Figure 8: Computation time cost of different strategy.  
candidate path violates the prescribed fiber-length bound, the algorithm must excise its single longest, high-risk edge and then re-invoke a full shortest-path computation under the risk metric. Although the maximum path length \(O(N)\) is strictly bounded, each prune-and-recompute cycle carries a penalty. When such cycles are performed repeatedly, their aggregate contribution to total runtime becomes non-negligible.
# A Dual-Polarized and Broadband Multiple-Antenna System for 5G Cellular Communications
## 4 Prototyping and Measurements of the Suggested Design
A low ECC value indicates that the signals received or transmitted by the different elements exhibit minimal correlation. This is desirable in MIMO systems as it reduces interference and enhances the antenna array's ability to provide independent and diverse communication paths. Similarly, a low TARC value implies that the antenna array efficiently radiates the input power, resulting in little to no reflection back to the input ports. The presented ECC and TARC demonstrate that the antenna array is highly compatible with MIMO functionality, as it maintains low correlation and optimal radiation efficiency across the target band, providing reliable and effective performance for multi-antenna scenarios. Figure 15: _Cont._
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
## 1 Introduction  
Oil production faces a decline in many oil fields in different parts of the world because of the withdrawal of large amounts of high-flowing oil. There is a necessity, therefore, to apply various methods to enhance oil recovery from existing oilfields worldwide. The extraction of heavy crude oil needs higher energy input due to its resistance to flow, high density, and low American Petroleum Institute (API) gravity of less than 20 [1].  
Viscosity is a measure of a fluid's resistance to flow. It is one of the most important properties of a fluid and plays a very important role in the petroleum industry. The viscosity of a crude oil therefore affects the ability to pump it out of the ground. Small changes in viscosity can have a dramatic impact on the properties of petroleum fluids. Oil mobility can be improved by a reduction of oil viscosity; therefore, lowering the viscosity of crude oil is essential in the oil extraction industry. Thus, the discovery of new technologies that reduce the viscosity of crude oil with minimum requirements to raise its temperature is highly valued.  
Microbial enhanced oil recovery (MEOR) has been proposed for many years as a cheap and effective alternative technique. MEOR is a good alternative in improving the recovery of crude oil from reservoir rocks by using microorganisms and their metabolic byproducts [2]. Microbial enhanced oil recovery (MEOR) is used in the third phase of oil recovery from a well, which is the tertiary oil recovery stage [3]. Many investigations of the MEOR technique have injected microorganisms or their products into oil reservoirs to improve the efficiency of oil recovery [4; 5; 6]. Up to 50% of the residual oil can be extracted by this exceptionally low operating cost technology [7]. Bacteria can ferment readily available organic compounds to produce gases such as CH\({}_{4}\), CO\({}_{2}\), and H\({}_{2}\). These gases, which are produced in situ, can increase the pressure of the reservoir and enhance oil recovery.  
In addition, they mobilize the crude oil through reducing its viscosity which enhances its recovery [8].  
This study aimed to demonstrate the potential of using bacterial isolates _Bacillus_ species, _Pseudomonas putida_, and _Nocardiic cyriacigeorgica_ in the biotransformation of heavy crude oil (26\({}^{\circ}\) API) and to evaluate the viscosity reductions of the heavy crude oil to enhance its flow properties and increase its extraction efficiency from the reservoir.
## 3 Methodology
### Temporal Correlation Prediction Method  
#### 3.2.1 LSTM Model  
The LSTM network [45] is a deep learning method designed for sequential data. It can deeply mine crucial hidden information in time-series data and effectively capture long-term dependencies, making it highly sensitive to temporal changes. Therefore, we used the LSTM model to extract temporal features from landslide displacement monitoring data.  
LSTM introduces a memory cell, which can store and access information, and controls the flow of information through a gating mechanism. The key components of LSTM include the input, forget, and output gates (Figure 5).  
The first step in LSTM is to determine which information to forget from the cell state, which is determined by a sigmoid layer known as the \"forget gate\". The forget gate takes the previous hidden state \(h_{t-1}\) and the current input \(x_{t}\) as inputs and outputs a value between 0 and 1. A value of 1 means \"completely retain this value\", while a value of 0 means \"completely forget this value\".  
The computation of the forget gate was given by Equation (6):  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]\Big{)}+b_{f}. \tag{6}\]  
2. Input Gate  
The second step determined which information to store in the cell state and consisted of two parts. First, a sigmoid layer, known as the \"input gate\", decided which data must be updated. Then, a \(\tan h\) layer created a vector \(\widetilde{C}_{t}\) of new candidate values that were added to the cell state. These two parts were then combined to update the cell state.  
The computation of the input gate was given by Equations (7) and (8):  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}])+b_{i}, \tag{7}\]  
\[\widetilde{C}_{t}=\tan h(W_{c}\cdot[h_{t-1},x_{t}])+b_{c}, \tag{8}\]  
The combination of the forget and input gates was given by Equation (9):  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t}. \tag{9}\]  
Figure 5: LSTM model.  
Output Gate  
Finally, output information was determined, which was based on a filtered cell state.  
The computation of the output gate was given by Equations (10) and (11):  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}])+b_{o}, \tag{10}\]  
\[h_{t}=o_{t}*tanh(C_{t}), \tag{11}\]  
where \(h_{t-1}\) is the output from the previous layer; \(x_{t}\) is the input vector; \(\sigma\) is the sigmoid function; \(W_{f}\) is the coefficient matrix for \(f_{t}\) and \(i_{t}\); \(b_{f}\) is the bias for \(f_{t}\); \(b_{i}\) is the bias for \(i_{t}\); \(W_{c}\) is the coefficient matrix for \(\widetilde{C}_{t}\); \(b_{c}\) is the bias for \(\widetilde{C}_{t}\); \(C_{t-1}\) is the memory from the previous block; and \(C_{t}\) is the memory from the current block.
## 5 Test-Result Analysis
### Influence of Key Design Parameters on the Force-Transmission Mechanism
#### 6.1.1 Plate-Thickness-Design Effect  
The sensitivity of the force characteristics of the anchor box to the designed thickness of the plates was investigated by varying the thickness of the support plates N1 and N2 and the pressure plate N7, while keeping the other design parameters constant. The designed thickness of each plate was determined by considering the range of possible values for the actual structural design, as shown in Table 5.  
The results of the stress-inhomogeneity coefficient and the force-transmission ratio of the weld for the different design thicknesses of the support plates N1 and N2 and the pressure plate N7 are shown in Figure 17. The figure uses the left and right vertical axes to represent the shear-stress-inhomogeneity factor and the force-transfer ratio, with the hollow icon line indicating the welds' shear-stress-inhomogeneity-factor results, referring to the left vertical axis, and the solid icon line indicating the force-transfer-ratio results, referring to the right vertical axis.  
The results of the study showed the following: 1 the shear-stress-inhomogeneity coefficient of the transmission weld decreased with the increase in the thickness of the pressure plate N7, and the force-transmission ratio of the weld did not change significantly with the increase in the thickness of the pressure plate N7; 2 the shear-stress-inhomogeneity coefficient of the transmission weld decreased with the increase in the thickness of the support plates N1 and N2, and the force-transmission ratio of the weld increased with the increase in the thickness of the support plates N1 and N2; 3 the inner-web weld had a larger force transfer than the outer-web weld, but the shear-stress-inhomogeneity coefficient was smaller; 4 the variation in the thicknesses of the support plates N1 and N2 had a greater effect on the force characteristics than the thickness variation in the pressure plate N7.
# Analysis of Bullet Impact Locations in the 10 m Air Pistol Men's Competition Based on Covariance
## 3 Methods
### Extraction of Bullet Impact Point Location Information
#### 3.1.1 Extraction of Image x and y Coordinates  
The x and y positions of the bullet impact locations are detected from the target images using template matching. Examples of the target, the bullet impact template, and the detected results are shown in Figure 1. The bullet impact template searches from the top-left of the target image, performing a horizontal scan, and calculates the correlation with the image pixels that match the template. The top 10 positions with the highest correlation values are determined to be the bullet impact locations. If bullet impact locations overlap completely, the top 10 positions might not be detected accurately, so the detected positions are manually adjusted as needed.
## 5 Discussion
### Applicability of PCA-AHP-QFD Integration in Different Study Industries
Potential of PCA-AHP-QFD: Integrating PCA with AHP-QFD in the healthcare sector can significantly streamline the feature prioritization process by reducing the complexity of the AHP-QFD matrices. Although Dataset X2 showed a more even distribution of variance across principal components, indicating a less dominant first component, PCA still aids in dimensionality reduction. This is crucial for managing the complexity of medical device design. By simplifying the dimensionality, PCA facilitates the identification of key features that meet both patient and regulatory requirements. However, the gradual decline in explained variance suggests that PCA may need to be supplemented with additional analytical methods to fully address the specific needs of this sector.
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
###### Abstract  
Precast concrete structures have become increasingly popular in the construction industry due to their ability to enhance efficiency, structural soundness, quality, and sustainability. Among these, modular construction has emerged as a transformative approach that fully leverages precast technology by manufacturing 3D modules off-site and assembling them on-site using inter-module connections. This study reviewed current literature trends on precast concrete structures and modular construction, analysing how modular construction distinguishes itself from other precast systems. This review further emphasises the role of composite connections--grounded, bolted, and hybrid systems--critical in ensuring structural integrity, efficiency in load transfer, and seismic resilience in modular construction. Advancements in composite connections have demonstrated significant promise, particularly in seismic performance, with reported energy dissipation improvements of up to 30% in hybrid connection systems. Yet limitations still exist, necessitating improvements in load transfer efficiency, ductility, and reliability under dynamic loads. Additionally, design considerations for modular construction, such as modular configurations, handling stresses, and transportation challenges, are explored to highlight their influence on system performance. This review underscores the feasibility and potential of modular construction in fostering sustainable and resilient infrastructure, as studies indicate that modular construction can reduce project timelines by up to 50% while minimising material waste by approximately 30%. The role of Non-Destructive Evaluation (NDE) techniques and intelligent monitoring systems in assessing and enhancing the lifecycle performance of composite connections is also emphasised. This review further advocates for continued research to refine composite connections and support the broader adoption of modular construction in modern building practices.  
modular construction; precast concrete; composite connections; seismic performance 2025 15 5256
## 5 Discussion
### Limitations and Future Directions
#### 5.6.2 Expansion of the ARRBD  
Currently, the ARRBD dataset remains limited in both scale and diversity, directly impacting the MS-RRBR's recognition performance for low-frequency categories. Subsequent research should prioritize broadening the dataset's scope and depth, incorporating additional types of RRBs while integrating multimodal data such as speech and sensor inputs to enhance its robustness. Additionally, participant diversity must be improved. Future studies should strive to recruit a more diverse participant pool that includes children from various ethnic backgrounds, age groups, and genders. This will help mitigate potential biases and improve the model's applicability across broader populations. Lastly, as the current dataset lacks detailed contextual information, it is unable to evaluate the model's performance in different environments. We aim to expand the dataset's metadata to assess the model's generalizability across various real-world and clinical settings.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 3 Results
### Observation Results
#### 3.1.3 Doppler Shift  
The Doppler shift is a crucial parameter in receiver design because it influences the search range during acquisition and determines the cold start time. It is directly related  
Figure 10: Received signal \(C/N_{0}\) using a quad GNSS system with a reception threshold of 10 dB-Hz.  
Figure 9: Received signal power using a quad GNSS system with a reception threshold of 10 dB-Hz.  
to the relative dynamics between the receiver and the satellite in the line of sight (LOS) direction, as well as the signal frequency. Figure 11 illustrates the results of the Doppler shift simulation. The Doppler shift values of most of the main lobe signals are concentrated between \(-10\) and \(+10\) KHz, while most the side lobe signals range between \(-20\) and \(+20\) KHz. The maximum Doppler shift reaches 26.23 KHz, which is much larger than the Doppler shift value of \(\pm 10\) KHz for dynamic receivers on Earth. To successfully acquire these highly dynamic and low power side lobe signals requires a subtle design of the receiver's acquisition algorithm.