esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Discussions
### Hardware Experiments and Analyses
#### 5.3.2 Dynamic Range  
As a spectrum sensor, some basic technical indicators have to be calculated, such as dynamic range and noise figure, etc.  
Figure 15 depicts the link budget calculation of the hardware system. To satisfy the sampling interface of oscilloscope, we set the minimal output amplitude level at 100 millivolts peak-to-peak (\(mVptp\)). It is easily to be formulated as  
\[P_{out}\geq-16\;\mathrm{dBm}. \tag{22}\]  
From the simulation results in Figure 13, the SNR is at least \(0 dB\) that can ensure 100% correct detection. So  
\[SNDR\geq 0\;\mathrm{dB} \tag{23}\]  
which means the required signal to noise-and-distortion ratio. Then the minimal input power can be expressed as  
\[P_{in,\mathrm{min}}\geq P_{out}-\sum G_{i}=-16-35.8=-51.8\;\mathrm{dBm}, \tag{24}\]  
where \(G_{i}\) represents the gain of each device, and the attenuator is set to 0 dB.  
As for maximal input, the variable attenuator is set to the maximal level of \(-31.5\;\mathrm{dB}\) as well with the purpose of reducing enormous gain of the frontend. In order to acquire the maximal input power, we have to calculate noise figure and IP3 of the device chain first. The noise figure is formulated as  
\[NF=NF_{1}+\frac{NF_{2}-1}{G_{1}}+\frac{NF_{3}-1}{G_{1}G_{2}}+\ldots \tag{25}\]  
After calculation, the equivalent NF of the system is around 3.97 dB. And the equivalent IP3 of the entire system is counted by [28]  
\[(IP3)^{-1}=\sum_{i=1}^{I}{(IP3_{i}\prod_{j=i+1}^{I}{G_{j}})}^{-1}=(27.1\; \mathrm{dB})^{-1} \tag{26}\]  
According to [20], the  
\[P_{in,\mathrm{max}}\leq IP3-\frac{1}{2}(SNDR+7.3)-10\mathrm{log}_{10}N_{ \mathrm{max}}-\sum G_{i}=9.15\;\mathrm{dBm} \tag{27}\]  
From Equation (24) and (27), the dynamic range of our system is finally derived as 60.95 dB.
###### Abstract  
Slope failure induced by sublevel caving mining is a progressive process, resulting in the large deformation and displacement of rock masses in the slope. Numerical methods are widely used to investigate the above phenomenon. However, conventional numerical methods have difficulties when simulating the process of progressive slope failure. For example, the discrete element method (DEM) for block systems is computationally expensive and possibly fails for large-scale and complex slope models, while the finite difference method (FDM) has a mesh distortion problem when simulating progressive slope failure. To address the above problems, this paper presents a finite difference modeling method using the adaptive local remeshing technique (LREM) to investigate the progressive slope failure induced by sublevel caving mining. In the proposed LREM, (1) the zone of the distorted mesh is adaptively identified, and the landslide body is removed; (2) the updated mesh is regenerated by the local remeshing, and the physical field variables of the original computational model are transferred to the regenerated computational model. The novelty of the proposed method is that (1) compared with the DEM for block systems, the proposed LREM is capable of modeling the progressive slope failure in large-scale rock slopes; (2) the proposed method is able to address the problem of mesh distortion in conventional FDM modeling; and (3) compared with the errors induced by the frequent updating of the mesh of the entire model, the adaptive local remeshing technique effectively reduces calculation errors. To evaluate the effectiveness of the proposed LREM, it is first used to investigate the failure of a simplified slope induced by sublevel caving mining. Moreover, the proposed LREM is applied in a real case, i.e., to investigate the progressive slope failure induced by sublevel caving mining in Yanqianshan Iron Mine.  
progressive slope failure; sublevel caving mining; FDM; distorted mesh; adaptive local remeshing +
Footnote †: journal: Applied Sciences
Therefore, in this work, we evaluated HTAB, which has a vertical self-alignment feature, for use in LCs that have various cell gap conditions. We confirmed the limitation of cell gap thickness with the HTAB for full-homeotropic alignment. However, this work is the first report that considers the cell gap of LC cells that use HTAB. Additionally, we showed that the required HTAB concentration ([HTAB]) for full-homeotropic alignment is proportional to the cell gap until the surface LC molecules' alignment is not properly transferred into the LC medium due to the weak anchoring nature of HTAB. We also reported the effect of [HTAB] on the phase-change characteristic of the LC mixture by analyzing heat flow results from a differential scanning calorimeter (DSC). This result is meaningful because designing RF devices with HTAB driven by light or heat must consider this phase-change temperature tendency. We hope our work will be a milestone in the new approach to the efficient RF device design process using LCs.
## 3 Simulation Results of the Elastoplastic SW-DVIB Method
### Fracture Energy Conservation  
This approach incorporates fracture energy into the fracture model to ensure that the results remain unaffected by mesh-size sensitivity. To examine the impact of fracture energy, three-point bending tests were conducted using two different methods: accounting for fracture energy and disregarding it. The dimensions of the specimen and meshing configuration are illustrated in Figure 7, where three types of meshes were applied: coarse, middle, and fine.  
Figure 6: Reproduced deformation procedure: (**a**) stress-strain relation; fracture opening deformation at (**b**): (**A**) \(\varepsilon=0,t=0\) s; (**B**) \(\varepsilon=0.00108,t=4\) s; (**C**) \(\varepsilon=0.003,t=10\) s; (**D**) \(\varepsilon=0.00192,t=14.2\) s; (**E**) \(\varepsilon=0.00102,t=17.2\) s; (**F**) \(\varepsilon=0,t=20\) s.  
In the initial case, the fracture energy was excluded by setting \(\widetilde{\epsilon}_{f}=\widetilde{\epsilon}_{b}\) with the parameters of the micro-bonds defined as \(E=42.41\)GPa, \(\widetilde{\epsilon}_{y}=1\times 10^{-3}\), \(\widetilde{\epsilon}_{b}=2\times 10^{-3}\), \(\alpha=0.2\), \(v=0.2\), and \(\rho=2700\) kg/m\({}^{3}\). In the second case, the micro-bond parameters remained unchanged from those in Case I, except for the inclusion of fracture energy, specified as \(G_{f}=45\) N/m. The load-displacement behavior at the loading point is depicted in Figure 8. The simulated results for Case I without considering the fracture energy are illustrated in Figure 8a. It is evident that the highest force diminishes gradually with a decrease in element size. In Case II, in which the fracture was considered, as shown in Figure 8b, the simulated results were unaffected by the element dimensions.  
The inclusion of fracture energy not only eliminates mesh independence for the force-displacement curve of the loading point but also ensures that the simulated fracture morphology remains unaffected by the element size. In Case I, because the fracture energy was not incorporated, the fracture condition for micro-scale bonds was determined by a predefined bond length or its associated force. When either of these thresholds is satisfied, bonds break. As illustrated in Figure 9, reducing the element size resulted in a rougher fracture morphology. Conversely, in Case II, in which the fracture energy was considered, the fracture condition for the micro-scale bonds was directly linked to the fracture energy. Consequently, as shown in Figure 10, the fracture shape remains consistent regardless of the element dimensions.  
Figure 8: Relationship between applied force and displacement at the loading point: (**a**) without crack energy; (**b**) with crack energy.  
Figure 7: Three-point bending test configuration and mesh refinement strategy: (**a**) geometry and dimensions of the specimen; (**b**) coarse discretization; (**c**) medium-level mesh; (**d**) fine mesh resolution.
## 2 Materials and Methods  
### Experimental Design  
The whole-crop wheat was harvested at 7:00 and 15:00 in Zhuozhou City, Hebei Province of China, respectively, with a cutting height of about 10 cm during the milky ripe stage. Subsequently, a portion of wheat was sun cured (hay) for 5 days and a portion was ensiled in the vacuum bag for forty days. The whole-crop wheat in the dough stage was processed in the same procedure, resulting in eight treatments: wheat harvested at 7:00 in milk stage and preserved as hay (MWH7) or silage (MWS7), wheat harvested at 7:00 in dough stage and preserved as hay (DWH7) or silage (DWS7), wheat harvested at 15:00 in milk stage and preserved as hay (MWH15) or silage (MWS15), wheat harvested at 15:00 in dough stage and preserved as hay (DWH15) or silage (DWS15). The chemical composition of the preserved forages is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{6}{c}{**Treatments**} & \multicolumn{2}{c}{**SEM 3**} & \multicolumn{2}{c}{_p_**-Value 4.5**} \\ \cline{2-13}  & **MWH27** & **MW57** & **DWH7** & **DWS7** & **MWH15** & **MW515** & **DWH15** & **DWS15** & & **S** & **Tr** & **Ti** \\ \hline EE & 1.58 b  & 2.51 b  & 1.6 b  & 3.31 a  & 2.42 b  & 2.82 b  & 1.82 b  & 0.185 & 0.15 & 0.022 & 0.214 \\ ANDF & 47.8 b  & 47.01 b  & 47.13 b  & 43.97 c  & 46.52 b  & 47.68 b  & 51.9 a  & 41.44 c  & 0.556 & 0.009 & 0.031 & 0.185 \\ ADF & 28.58  & 29.09 & 25.81 & 28.66 & 27.08 & 28.99 & 26.37 & 25.98 & 0.423 & 0.189 & 0.159 & 0.128 \\ OM & 91.42 b  & 90.71 d  & 92.18 a  & 90.7 d  & 92.14 a  & 90.89 d  & 92.05 a  & 91.33 b  & 0.132 & 0.056 & 0.043 & 0.033 \\ CP & 10.99 b  & 12.06 a  & 10.44 c  & 11.06 b  & 10.39 c  & 11.23 b  & 10.44 c  & 11.12 b  & 0.1126 & 0.019 & 0.089 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical composition 1 of different treatments 2 (%, dry matter—DM basis).
## 5 The Hybrid Predictor  
A core objective of our solution is to minimize cold start occurrences. The first step toward achieving this is accurately predicting future load over a given time interval. Reliable forecasts allow the scheduler to pre-warm the required number of containers in advance, following the strategy that will be detailed later. Traditional time-series prediction models, such as LSTM, face significant limitations in FaaS environments. While LSTM can capture complex dependencies in invocation patterns, it requires a large volume of data for effective training and is computationally intensive. ARIMA, which has been widely used in various studies [18; 19], struggles to incorporate exogenous variables (e.g., time of day, day of week) and cannot capture non-linear trends. Lastly, Prophet--which combines time series decomposition with machine learning fitting--often performs poorly in environments characterized by irregular or highly dynamic patterns. To address the limitations of standalone models in FaaS workload forecasting, we build a hybrid prediction model that combines ARIMA and Random Forest (RF). While hybrid ARIMA-RF models exist for time-series forecasting, this work pioneers their adaptation to serverless environments through environment-aware feature engineering and tight scheduler integration. This model serves as the forecasting component of our system. By leveraging the strengths of both ARIMA and RF, the hybrid model reduces forecasting  
Figure 3: The Hybrid Model Architecture: The dashed box highlights the new modules we introduced.  
errors and provides more robust predictions: ARIMA captures the linear base patterns, while RF corrects the residuals. This design is also highly adaptive, making it suitable for both steady-state workloads and bursty or spiky traffic.  
Our hybrid predictor is specifically designed for serverless environments. The model consists of an ARIMA component that captures the linear patterns \(\hat{y}\) and a Random Forest component that models the non-linear residuals \(r_{t}\). The final prediction \(\hat{y}_{hybrid}\) is produced by combining the outputs from both components.
## 5 Conclusions  
This paper uses the MODIS-PSN model to simulate the GPP of vegetation on the Qinghai-Tibet Plateau for the period from 2001 to 2022. The simulation is based on the MODIS dataset and the ERA5-Land dataset, among others, to produce remotely sensed GPP products for the Qinghai-Tibet Plateau. The Geodetector method was employed to analyze the impact of natural factors, selecting six key factors--temperature, precipitation, radiation, saturated water vapor pressure difference, evapotranspiration, and leaf area index. These factors were used to quantitatively attribute the spatial drivers of vegetation GPP on the Qinghai-Tibet Plateau from 2001 to 2022.  
On the climate change-sensitive Qinghai-Tibet Plateau, studying GPP can help assess the responsiveness of vegetation ecosystems to climate change. The development of agriculture and animal husbandry on the plateau is closely linked to the health of vegetation ecosystems. It is of great significance to study the changes in vegetation GPP and its relationship with driving factors on the Qinghai-Tibet Plateau, to quantitatively investigate the relationship between vegetation GPP changes and climatic factors in the Qinghai-Tibet Plateau, and to safeguard the ecological security of the Qinghai-Tibet Plateau while improving the ecological environment.  
This study only considered factors such as evapotranspiration, leaf area index, precipitation, radiation, air temperature, and saturated water vapor pressure difference as drivers of vegetation GPP. However, elevation, vegetation cover, and anthropogenic factors also have significant influences on vegetation GPP. Future studies will integrate both natural and anthropogenic factors to more comprehensively explore the relationship between vegetation GPP and its drivers on the Qinghai-Tibet Plateau.  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] analyzed the data, created the tables and figures, and finished the first version of the paper; [PERSON], [PERSON] and [PERSON] contributed valuable opinions during manuscript writing; [PERSON] and [PERSON] revised the whole manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Natural Science Foundation of China (42161027).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 3 Proposed Name Disambiguation Scheme
### Author Name Disambiguation
#### 3.4.1 Rule-Based Scheme
In this context, \(x\) is the number of identical co-authors, similar to the co-author count formula, and \(y\) is the total number of co-authors in the document with more co-authors among the two being compared. Regarding the journal and conference attributes, a comparison was made between the publication venues of the two papers. If the two documents were identical venues, a weight of one was assigned. If the venues were different or did not include the journal and conference attributes, a weight of zero was allocated. Figure 4 shows an example of weight determination based on the rules listed in Table 3. For the affiliation attribute, the Jaro-Winkler distance value was derived from the redundant words \"Chungbuk National Univ.\" and \"Chungbuk National Univ. Bigdata Depart\", resulting in the \(s_{j}\) value. In the Jaro-Winkler similarity, \(m\) denotes the number of common characters in the two strings. In the above example, all the letters of \(s_{1}\) are common. m is calculated as 21. Therefore, the \(s_{j}\) value is 0.7. To calculate \(s_{w}\), the common prefix length \(w\) is set to 4 (maximum value), and \(p\) is calculated as a standard value of 0.1. As a result, \(s_{w}\) is 0.83. The publication year attribute defines the data collection period as 5 years. By using the absolute value function to calculate the publication year difference between the two documents, a score of 0.4 was assigned. The co-author attribute determines the number of identical co-authors in the two papers, excluding the co-author with the same name, \"[PERSON]\". The co-author ratio divides the number of co-authors by the number of co-authors in the document with the larger number of co-authors. If we evaluate only the number of co-authors in a paper written by one author, we get an ambiguous value of approximately 0.63. This value is a very uncertain number to perform an analysis with the same name. In addition, if the number of co-authors of the same name accidentally increases in a paper with a large number of authors, the value of the number of co-authors converges to 1. To solve this problem, we supplemented this by adding a co-authors ratio. Here, the combined values of the co-author count and ratio were halved, and scores of 0.475 and 0.375, respectively, were obtained. Thus, the co-author attribute value was represented as 0.85. For the journal and conference attributes, since conferences of both the documents were \"Journal of Bigdata\", they were identical, and a score of one was assigned. By adding the results from all the rules, the final weight was determined. The weight of the sample documents \"Jang_0\" and \"Jang_1\" was 3.08, indicating a high similarity.
## 4 Open Challenges and Future Research Directions
### Challenges of Blockchain-Based Security Models in IoE  
While blockchain-based security solutions offer benefits such as decentralized identity management, data integrity, and access control in IoE systems, there are critical challenges that hinder their widespread adoption.  
_Scalability, Security, and Performance Trade-Offs_: Blockchain struggles with high computational costs and slow transaction times when processing large-scale IoE datasets, making real-time applications difficult. Current Proof of Work (PoW) and Proof of Stake (PoS) systems are not fully optimized for IoE environments. Therefore, more lightweight and efficient consensus mechanisms need to be developed for IoE applications [11; 47; 88].  
_Integration with Decentralized Identity (DID and SSI)_: As the number of IoE devices grows, identity authentication and authorization processes need to be conducted through decentralized methods. Although DID and SSI solutions are evolving, their practical applicability at the IoE scale remains an open research topic [38; 93].  
Security of Smart Contracts in IoESmart contracts are utilized in IoE to enable automated device-to-device transactions. However, software vulnerabilities, cyberattacks, and auditability issues pose significant security risks. There is a critical need to strengthen smart contracts through formal verification and advanced security auditing mechanisms [84; 92].  
Open Research Areas:  
* Development of lightweight and scalable blockchain consensus mechanisms
* Investigation of decentralized identity management systems for IoE scalability
* Protection of smart contracts against cyberattacks  
Blockchain-based solutions provide crucial advantages such as secure data recording and identity verification in IoE systems, but also introduce high latency and scalability limitations. The large volume of data generated by IoE devices can create bottlenecks in classical blockchain architectures. This leads to a growing need to balance security and processing speed.  
Research DirectionFuture studies should focus on more scalable infrastructures such as Layer-2 solutions [152; 153; 154; 155] and DAG-based blockchain systems [153; 156]. Additionally, energy-efficient consensus algorithms need to be adapted for IoE environments [157; 158]. As the number of IoE devices continues to rise, centralized authentication is becoming insufficient. Enhancing the applicability of DID and SSI-based decentralized identity management systems in IoE devices will provide a substantial contribution to the field [159; 160].
## 4 Results and Discussion
### Validation of the Landslide Susceptibility Maps  
In this study, we evaluated several models for predicting landslide pixels using statistical evaluation indices. The ensemble model based on BLS showed the best performance in predicting landslide pixels (sensitivity = 94.3%), followed by the ensemble models based on SVM (sensitivity = 91.4%) and CART (sensitivity = 88.6%). For predicting non-landslide pixels, the BLS-AdaBoost model (specificity = 93.3%) showed the best performance, followed by the SVM-AdaBoost model (specificity = 90.0%) and the CART-AdaBoost model (specificity = 87.1%). In terms of overall accuracy, the best performance was found for BLS-AdaBoost (accuracy = 87.1%), followed by SVM-AdaBoost (accuracy = 84.3%) and CART-AdaBoost (accuracy = 82.9%).  
The results of ROC-AUC, as another evaluation method, are displayed in Figure 10. The ROC curves of the CART-AdaBoost model, the SVM-AdaBoost model and the BLS-AdaBoost model for the validation dataset are represented by green, red and blue, respectively. BLS-AdaBoost (AUC = 0.889) had the highest AUC value among the three ensemble models, followed by SVM-AdaBoost (AUC = 0.873) and CART-AdaBoost (AUC = 0.846).
## 5 Numerical Experiments and Discussion  
In this section, three case studies are presented to illustrate the usage of the proposed approach. Case Study-1 is a toy example that highlights the notion of mixed strategy, steady state analysis, and data estimation. In Case Study-2, synthetic data on an arbitrary \(6\times 6\) game are utilized to depict the applicability of the proposed game. In Case Study-3, a non-square game with overlapping intervals, which is most likely related to the real-world scenarios, is presented.
## 3 Results
### Comparison of the Mitigation of Targeted Gases as a Function of UV Wavelength
#### 3.2.4 Greenhouse Gases
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**UV** & **UV Dose** & **Light Intensity** & **Control** & **Treatment** & **\% Reduction** \\
**(nm)** & **(\(\mu\)/cm\({}^{2}\))** & **(\(\mu\)W/cm\({}^{2}\))** & **(ppb)** & **(ppb)** & **(\(p\)-Value)** \\ \hline
185 + 254 & 0.03 & 0.01 & & 1.1 \(\pm\) 0.0 & **70.9 (0.04)** \\
222 & 2.83 & 0.59 & & 1.7 \(\pm\) 0.0 & 55.7 (0.06) \\
254 & 1.78 & 0.37 & 3.7 \(\pm\) 0.9 & 1.4 \(\pm\) 0.2 & 63.6 (0.06) \\  & 192 & 40 & & 3.9 \(\pm\) 0.5 & \(-\)3.7 (0.77) \\
367 & 1968 & 410 & & 3.2 \(\pm\) 1.3 & 15.9 (0.59) \\ \hline \hline \end{tabular} Note: Treatment time = 4.8 s (airflow = 0.25 m\({}^{3}\)/s), inlet and outlet air temp. = 16 \(\pm\) 1 \({}^{\circ}\)C and 19 \(\pm\) 2 \({}^{\circ}\)C.  
\end{table} 
Table 9: Mitigation of CH\({}_{4}\) concentration with different UV wavelength irradiating gaseous emissions inside the #2 chamber. Bold signifies statistical significance.