esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
1. The four main controlling factors--falling rock shape, slope surface material, initial velocity, and slope angle--exhibit decreasing effects on \(R_{\text{n}}\). The analysis revealed a significant two-factor interaction: falling rock shape-slope surface material, falling rock shape-initial velocity, slope surface material-initial velocity, and falling rock shape-slope angle. Notably, a significant three-factor interaction was observed among falling rock shape, slope surface material, and initial velocity.
2. At high-level \(\eta\), the falling rock shape interacts with other main controlling factors, the line/face-face collision energy consumption increases. This effect on \(R_{\text{n}}\) is weaker than that of low-level \(\eta\) interacting with slope surface material and initial velocity, but stronger than its interaction with slope angle. At high-level \(A_{\text{slope}}\), the slope surface material interacts with falling rock shape and initial velocity is weakened due to a decrease in slope surface material strength and deformation recovery. The collision strength increases at high-level \(v\), which reduces the effect of falling rock shape and slope surface material on \(R_{\text{n}}\). The energy consumption discrepancy is mainly attributed to falling rock shape at high-level \(\theta\), while the effect of slope angle on \(R_{\text{n}}\) diminishes.
3. In three-factor interaction, the \(R_{\text{n}}\) sensitivity on \(A_{\text{slope}}\) becomes negligible as \(\eta\) and \(v\) increase. High-level \(\eta\) reduces the effect of slope surface material on \(R_{\text{n}}\) at high-level \(v\)* The maximum error between the estimated \(R_{\rm n}\) from the multi-factor interaction model and the field value is 0.014, corresponding to a 3.39% error. This indicates high accuracy compared to the results from the non-interaction model and other empirical formulas. The accuracy of \(R_{\rm n}\) can be improved by up to 68.52% compared to the estimated results by the non-interaction model and other empirical formulas.  
Conceptualization, R.B. and Z.H.; methodology, R.B.; software, Z.H.; validation, R.B. and Z.H.; formal analysis, R.B.; investigation, R.B.; resources, R.B.; data curation, Z.H.; writing--original draft preparation, R.B.; writing--review and editing, R.B. and Z.H.; visualization, Z.H.; supervision, R.B.; funding acquisition, R.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 11802230) and the Natural Science Basic Research Program of Shaanxi Province (No. 2018)Q5122).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors would like to thank anonymous referees for their careful reading of this article and valuable suggestions.  
The authors declare no conflicts of interest.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 2 The Principles of MALDI-TOF MS
### Time-of-Flight Mass Analysis  
Once ionized, the charged biomolecules are accelerated through an electric field into a field-free drift region (the flight tube). The ions are separated according to their mass-to-charge \(m/z\) ratios; lighter ions travel faster and reach the detector before heavier ones. The time each ion requires to reach the detector is recorded and converted into a mass spectrum.  
Figure 1: Schematic representation of the MALDI-TOF MS mechanism [40].  
The resulting spectrum typically displays of multiple peaks within the 2000-20,000 Dalton range, corresponding primarily to ribosomal and housekeeping proteins [50; 51; 52; 53; 54].
## 3 Results and Discussion
### Effects of SO\({}_{2}\) and H\({}_{2}\)O on NO Removal Activity
#### 3.3.4 FT-IR Analysis  
Further confirmation of the TG-DTA data was obtained by FT-IR analysis to investigate the sulfate and ammonium groups on the catalyst surface. Figure 9 shows the FT-IR spectra of fresh and used catalysts.  
The freshand used catalyst both exhibited bands at 910 and 1635 cm\({}^{-1}\) and featured different metal oxides. In comparison with the fresh Mn-Ce/Al\({}_{2}\)O\({}_{3}\), the used Mn-Ce/Al\({}_{2}\)O\({}_{3}\) revealed some new bands at 1025, 1142, 1422, and 3260 cm\({}^{-1}\). According to literature [44; 45; 46], the bands at 1025 and 1142 cm\({}^{-1}\) were assigned to the ant symmetric stretching vibration of the SO\({}_{4}\)\({}^{2-}\) group [47]. The peak at 1422 cm\({}^{-1}\) indicated the presence of NH\({}^{4+}\) species, adsorbed on the Bronsted acid sites [46; 48]. Additionally, the broad peak at 3260 cm\({}^{-1}\) was attributed to the N-H stretching vibration of the NH\({}^{4+}\) group [49]. Combining the TG-DTA result with the FT-IR result, the conclusion further confirms that the formation of sulfate species on the surface of the catalyst during the SCR reaction in the presence of SO\({}_{2}\) can lead to pore plugging, a decrease in the surface area, and occupation of the active sites, which explain the decline in the SCR activity of the catalyst [18].
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 3 Downlink Scheduling in LTE
### Proportional Fair (PF) Algorithm  
The PF technique allocates the users' existing available radio resources. It considers the general channel characteristics and previous data concerning throughput as the weighting factor for the predicted data rate. The objective of the PF technique is to maximize the overall throughput while also providing fair data flow, as in Equation (1) [22].  
\[M_{i,k}^{PF}=\frac{d_{i,k}(t)}{R_{i}(t)} \tag{1}\]  
This measure ascertains the fraction of the presently accessible data rate \(d_{i,k}(t)\), and the average of the previous data rate \(R_{i}(t)\), while \(i\) corresponds to the flow in the \(k\) sub-channel.
# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 5 Performance Evaluations
The path length cost is measured by the total length \(l_{at}\) divided by the number of CPs (\(\tau\)). The maximal length of path constraint is considered as the resource and the unused resource is measured by \(1~{}-~{}(l_{at}/l_{max})\). Equation (24) indicates that the efficiency index depends on the path length cost for each CP and the resource unused rate. \[\zeta_{efficiency}=1~{}-~{}\left[\left(\frac{l_{at}}{\tau}\right)~{}\times~{} \left(1~{}-~{}\frac{l_{at}}{l_{max}}\right)\right] \tag{24}\]  
where \(l_{at}\) denotes the actual length of mobile sink \(m_{i}\) travelling path in each subtree in each round and \(\tau\) denotes the number of CP in subtree. Figure 8 depicts the impact of path length on the efficiency  
Figure 7: The comparison of SD energy consumption of three algorithms. (**a**) BD-Scenario; (**b**) UD1-Scenario; (**c**) UD2-Scenario; (**d**) UD3-Scenario. index. Four scenarios were considered in the simulation. In general, the efficiency indices of the BRPA, HiCoDG and CDCA mechanisms increase with the number of mobile sinks, as shown in Figure 8a,b The proposed mechanism CDCA outperforms the other two approaches HiCoDA and BRPA. This occurred because that HiCoDA and BRPA mechanisms do not consider the constraints of number of mobile sinks and path length. As shown in Figure 8, the ERPA yielded the smallest efficiency index value among the three compared mechanisms because the node balance in different subtrees was not considered. In addition, the efficiency index value of HiCoDG scheme was smaller than that of the proposed CDCA mechanism. This occurs because that the HiCoDG scheme also did not consider the node balance issue in different clusters and the distance between current cluster-head and the next cluster-head is not taken into consideration. The proposed CDCA balances the node number in different subtrees and finds as more as possible CPs under the constraint of total path length in each subtree. Therefore, the proposed CDCA achieves higher resource utilization and smaller path cost, as compared with the other two mechanisms. Consequently, the proposed CDCA mechanism yielded the largest efficiency index value.
## 2 Model Establishment and Analysis  
### Finite Element Model Building  
Before performing the finite element analysis, a finite element model that contains solid and shell elements is required.  
The first step is to establish a geometric model using computer-aided design (CAD) software to establish a geometric model of the semi-trailer fifth wheel, including the geometry and dimensions of the saddle body and other components, and the use of rigid body elements (RBE2) to connect the various components. The geometric model was then imported into HyperMesh (2020) for subsequent meshing and modeling operations; the material of all components was High Strength Low Alloy Structural Steels Q345.  
Because the force transforming element is located in the center of the fifth wheel (as shown in part 11 of Figure 2), connecting the saddle body and other components that play a vital role, it is set as property of solid element (PSOLID) and meshed using hexahedral elements. The other ten components are set as property of shell elements (PSHELL), so that the thickness of the components can be set as a variable by using shell cells, which is convenient for the subsequent lightweight design, and meshing is conducted by using a mixture of hexahedral elements and tetrahedral elements. Finally, the initial thickness of each component was defined, as shown in Table 1, for the corresponding thickness of each component, and the obtained solid model of the fifth wheel is shown in Figure 2.  
Figure 2: Solid model of the fifth wheel.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 2 Methods for Estimating the Location of Radio Transmission Sources
### Estimation Method Using the MUSIC Method
\[\mu_{1}\geq\mu_{2}\geq\cdots\mu_{L}>\mu_{L}+1=\cdots=\mu_{K}=0 \tag{23}\]3  
\[\mu_{1}\geq\mu_{2}\geq\cdots\mu_{L}>\mu_{L}+1=\cdots=\mu_{K}=0 \tag{23}\]4  
\[\mu_{1}\geq\mu_{2}\geq\cdots\mu_{L}>\mu_{L}+1=\cdots=\mu_{K}=0 \tag{23}\]5  
The denominator part of Equation (32) shows a minimum value at \(\theta\), which is the direction vector (direction vector of the arriving wave) orthogonal to the eigenvector corresponding to the noise, as mentioned earlier. The MUSIC method, on the other hand, uses the noise portion (the portion where the power is small) to achieve high resolution estimation. However, when multiple radio waves are incident from a single source due to reflected waves, etc., there is a correlation between the incoming waves. In this case, the rank of the signal correlation matrix expressed in Equation (21) is not equal to the number of arriving waves, and the direction of arrival cannot be accurately estimated. It is known that all of the three location estimation methods using direction of arrival estimation introduced in this section have the problem that the estimation accuracy is degraded by many reflected waves when a ground antenna is used.
## 2 Materials and Methods
### Selection of 3D Printing Materials  
The selection of a suitable printing material depends on three considerations: the mechanical properties, radiological properties, and transparency. To represent the mechanical properties of the human aorta, the material of the model must have similar flexibility and elasticity. The tensile strength in elderly patients with cardiovascular disease should be around 0.39 MPa and have a modulus of elasticity of 0.628 [23]. The elongation at breaking point of the aorta is around 82% to 140% and the hardness is between Shore A12 and A18 throughout the ascending, descending, and abdominal aorta [24].  
Based on our previous study, we chose four different 3D printing materials that had mechanical properties close to that of patients with aortic dissection [22]. In terms of transparency, a completely transparent model would enhance stent graft deployment without the need for imaging and contrast. This would enable good vision of intravascular structures and be useful in academic applications. However, a transparent elastomer printing material was not available from commercial companies, therefore, a translucent material was the next best option. After scanning samples of several available materials with the current clinical thoracic CTA protocol, one of the materials, Visijet CE-NT (3D systems Inc., Wilsonville, OR, USA), was selected as it had the same radiological properties to in vivo aorta [22]. The reason for choosing Visijet CE-NT is due to the following reasons: first, it is a rubber-like material suitable for printing aorta-like models. Second, it has a light yellow and translucent appearance allowing for visualization of the deployed stent grafts inside the aorta. Third, the Visijet CE-NT material has two different hardnesses, A30 and A70, with A30 having a tensile strength between 0.2 and 0.4, which is close to elderly cardiovascular tissue properties. Finally, our recent study confirms that Visijet CE-NT A30 is the most appropriate material for printing aorta models because its CT attenuation is similar to that of the contrast-enhanced CT images of aortic dissection [22].
# Tolerance Modeling and Analysis Considering Form Defects for Spaceborne Array Antenna
## 2 Tolerance Analysis Considering Form Errors
### Tolerance Analysis Based on Samples  
Based on the sample generation method in Sections 2.2-2.4, the tolerance analysis can be achieved efficiently. All the samples and corresponding performance can be illustrated by the parallel coordinates plot [15] as shown in Figure 7. All the tolerances and corresponding performance are normalized according to Equation (30) and every observation is highlighted as a connecting line; the failed observation (cannot meet the design specifications) are marked as red and the others are marked as blue.  
\[T_{norm}=\frac{T-T_{min}}{T_{max}-T_{min}} \tag{30}\]  
where the original tolerance design interval is defined as \([T_{min},T_{max}]\), \(T\) represents a set of the sample, and \(T_{norm}\) represents the normalized tolerance variable.  
Designers can intuitively analyze the impact of different tolerance value combinations on product performance.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 4 Results and Discussion
### Distances Calculation
#### 4.1.2 Maneuver AC  
Maneuver AC is performed when the CubeSat changes its orientation from the initial position A to the final position C. This maneuver represents two different scenarios: for 3U CubSat from 3-unit drag face to 1-unit drag face and vice versa, or for 6U CubSat from 6-unit drag face to 2-unit drag face and vice versa. Figure 7 shows the results of HDD and VDD for the three different orbits under study.  
The three CubeSats show similar evolutions during the two days regarding HDD and VDD, but with different effects. SAT-L, the one with the lowest orbit, shows the highest distance in each instant, and SAT-H, the one with the highest orbit, shows the lowest distance in each instant. SAT-M shows intermediate distances, between SAT-L and SAT-H. In all cases, the VDD varies proportionally with time and HDD shows an exponential behavior. The maximum VDD achieved by the maneuver corresponds to SAT-L, with 212.80 m, an intermediate value of VDD is reached by SAT-M with 113.79 m, and the minimum VDD is 56.43 m for SAT-H. On the other hand, the maximum HDD achieved by the maneuver corresponds to SAT-L, with 10.45 km, an intermediate value of HDD is reached by SAT-M with 5.54 km, and the minimum HDD is 2.72 km for SAT-H.
## 2 Results
### Evolution of Aroma Compounds during Cold Maceration Stage  
During the pre-fermutation cold soak stage, the grape mash is in an aqueous system. To better understand the leaching patterns of aroma compounds, both free-form and glycosidically bound, sampling was performed at five different time points during this stage. An analysis of clustering heat map was used to explore the changes in compounds. A total of 43 free and 16 bound aroma compounds were leached from \"Marselan\" and \"Merlot\" grapes during this stage. The compounds were clustered into five groups based on their concentration variations in the two varieties, as illustrated in Figure 1. The details of concentrations of substances can be found in Table S1 (Supplementary Materials). Aroma compounds in cluster 1 showed a continuous decrease in concentration during the cold soak stage, with the concentration approaching zero before alcohol fermentation (BAF). The results presented in Figure 1B show that free-form 1-hexen-3-ol, 1-hexanal, 1-hexanal, and (_E_)-2-hexen-1-ol experienced the greatest decrease. This suggests that these C6 compounds can be rapidly extracted from grape berries and subsequently transformed into other components after leaching. The compounds in the cluster 2 and cluster 3 showed an increasing trend throughout the soaking. Cluster 2 was composed of seven bound-form alcohols, six free-form alcohols, as well as free-form (_E_)-\(\beta\)-damascenone and linalool. In this cluster, aroma compounds were continuously released, and their concentration varied significantly between the varieties, especially for free-form compounds. Notably, the concentrations of linalool and
# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
## 3 Exhibition of Recent Literature
### Literature Examination of EL Images from 2025  
From 2025, 12 studies were examined in more detail, of which 6 studies examined EL, 3 studies examined IR, 2 studies examined RGB images, and 1 study examined point clouds. The main data from the publications are summarized in Table 1. [PERSON]'s [65] study uses deep learning to detect solar cell defects. Monocrystalline and polycrystalline cells are classified into three categories (intact, cracked, broken) using seven different CNN architectures. The best accuracy was achieved by SqueezeNet: 97.82% (monocrystalline), 96.29% (polycrystalline). The paper by [PERSON] et al. [73] presents the MRA-YOLOv8 model, which integrates new elements (MBCANet, ResBlock, AMPDIIoU) to significantly improve PV defect detection on complex backgrounds. The mAP50 of the method is 91.7% (PVEL-AD) and 69.3% (SPDI), outperforming both YOLOv8 and DETR. The study by [PERSON] et al. [88] accelerates solar characterization based on EL imaging by machine learning. They use MLP and a modified U-Net (mU-Net) model to regress local parameters Rs and J\({}_{0}\). The accuracy of MLP almost matches that of the classical NLLS algorithm but is significantly faster. The proposed approach provides an efficient solution for the fast processing of large data cubes. The study by [PERSON] et al. [89] presents a lightweight, efficient LMAFF (Lightweight Multiscale Feature Fusion) network for segmenting solar cell defects based on EL images. The network uses multilevel feature fusion and an observation mechanism to accurately detect small targets. It was validated on three databases, including mono- and polycrystalline silicon cells. The results show that the LMAFF network is more efficient than classical segmentation mod-cells. A paper published by [PERSON] [90] presents a CNN-based EL image analysis system for the automatic detection of solar cell defects. Using the RSWS classifier, an accuracy of 98.17% for two-class and 97.02% for four-class problems on the ELPV dataset was achieved with outstanding results. The research work of [PERSON] et al. [69] proposes a new approach for micro-injury segmentation based on EL polarization images. They collect data through three polarization channels (I, DOP, Q) and use an improved U-Net model to improve microcrack detection.
## 3 Model Validation  
To validate the accuracy of our approximated ODE-based model we have developed a custom simulator in Python, implementing the offloading scheme described in Section 2.  
Figure 4: The 1-D vehicular network model.  
The simulator is event-driven and supports both synthetic vehicular scenarios, generated with configurable parameters such as vehicle speed range and transmission range, and real-world mobility traces provided in CSV format, where each meeting is described by the tuple \((id_{1},id_{2},t_{\text{init}},t_{\text{end}})\), representing the node IDs and the time interval of the encounter. The simulation assumes that the inter-contact time is sufficient for message transmission, and no packet losses are considered, ensuring a high-level abstraction consistent with the ODE-based model. All simulations were performed on a server equipped with two Intel(R) Xeon(R) Platinum 8164 CPUs (2.00 GHz), providing 104 logical cores, 512 GB of RAM, and running Ubuntu 20.04.6 LTS. To analyse a broad range of conditions, the following results were obtained considering two mobility scenarios. The first scenario adopts the vehicular mobility model described in Section 2.6. The second scenario is based on a real-world dataset of opportunistic contacts between human-carried wireless devices [36]. A detailed description of the two mobility scenarios is provided in the following section. Regarding the communication model, we do not assume any specific technology (e.g., WiFi or Bluetooth) implementing the D2D communication underlying our offloading scheme. We only assume that the data rate is high enough to ensure that the content can be fully transmitted without losses during the D2D contact. As we discuss later, the simulation scenario we consider has been chosen because it is able to highlight _general_ features of the offloading process and not to show the impact of system parameters such as the channel interference, which are not evaluated here. Finally, the ODE model and the utility maximisation problem were solved using Mathematica (the source code is available upon request).  
Offloading performance is measured in terms of offloading ratio, content delay, and number of content items that are stored in the nodes' caches according to the metrics defined in Section 2.3. However, we also analysed the time evolution of the system state to better highlight specific features of the offloading process, and the ability of the modelling approach to closely match transient behaviours. We measured both average values and 95% confidence intervals, using standard independent replication techniques. The confidence interval is not shown in the figures due to its small size.