esa-sceva/satcom-chunk-collection huggingface dataset:
# Moving towards Personalized Medicine--The Broad Use of Aptamers for Targeted Theranostic
## 2 Cancer Therapeutics
### Final Remarks  
The high affinity displayed by aptamer over their targets, and its facility in conjugating with labelling molecules makes them a suitable tool, acting as recognition elements, for biomarker discovery and disease diagnosis, especially in imaging and biosensor devices [8; 9; 11].  
In the field of both in vivo and in vitro bioimaging, aptamers have advantages over antibodies, owing to their good solubility and low molecular weight when compared with antibodies, which allows its blood clearance and easy tissue penetration abilities [11; 12].  
Biosensors that use aptamers as recognition elements are called aptasensors and generally rely on the conformational changes in the aptamers after binding to its target. They have been applied in point-of-care miniaturized devices to detect environmental contaminants, cancer biomarkers or drugs, as example [85; 131; 132]. Additionally, they can also be used as a theragnostic tool, allowing to detect and treat cancer [133]. In terms of diagnosis, regardless the field of pathology, the main advantage in the application of the sensing strategy relies on early detection. Early detection allows, by itself a higher rate of success in overall survival. Moreover, since most diseases lack proper early diagnosis due to a lack of specific biomarkers, biosensors also allow the possibility of multiplexing analysis, helping eliminate inaccurate diagnoses [134], or even diagnosing diseases with higher feasibility [135]. Ultimately, this translates to proper and personalized therapy with fewer side effects and lower overall costs.
# Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models
## 3 Experiments and Discussions
### Comparison of the Simplification Effect with Other Algorithms
#### 3.3.1 Point Cloud Simplification for Natural Data Sets
In order to further test the simplification effect, we also performed the simplification algorithms on the dragon data set. The dragon data set size is 437,645 points. When experiment parameter \(c\) is set to 400,000, the simplification ratio is 49.4%. The simplification effects of the four algorithms are shown in Figure 13. The same simplification ratio, 49.4%, is applied to curvature-based and uniform-based algorithms. In addition to this, the simplification ratio of grid-based simplification is 49.5%. The experimental results show that when the FPPS or uniform-based simplification algorithm is used, the deviation value range is [\(-\)0.006, +0.006],  
Figure 12: The simplification effect comparison using the bunny data set: (**a**) uniform-based simplification algorithm; (**b**) grid-based simplification algorithm; (**c**) curvature-based simplification algorithm; (**d**) FPPS. which is narrower than that of other simplification algorithms, suggesting that these algorithms are superior to the other algorithms. In order to compare the simplification quality in detail, we amplified the partial area around the dragon's foot, as shown as a sub-figure in Figure 14. The sub-figure in Figure 14 shows the biggest deviation by the red and blue areas; therefore, the uniform-based simplification algorithm produced errors in the corner areas. A similar problem is shown by the red area in sub-figure Figure 14. Compared with Figure 14,b, Figure 14 shows less deviation around the dragon's foot, which means that the FPPS and curvature-based simplification algorithms produced optimal effects on the complex curved surfaces. Relative to the machine component and man-made data, there are always smaller continuous changes on the natural objects' surfaces. Applying the FPPS simplification algorithm to natural data  
Figure 14: Simplification effect comparison of four simplification algorithms using the dragon data set: (**a**) uniform-based simplification algorithm; (**b**) grid-based simplification algorithm; (**c**) curvature-based simplification algorithm; (**d**) FPPS. Figure 13: The reconstructed surface deviation with a decrease in the simplification ratio by four simplification algorithms. sets is superior to the uniform and grid algorithms in data sets with rich features. Additionally, for flat surfaces, FPPS performs slightly worse than the uniform-based simplification algorithm.
## 1 Introduction
The paper is structured as follows: Section 2 introduces the characteristics of the FoF, emerging threats and related attacks to provide guidance for security-related method descriptions, followed by an overview of related work on anomaly detection and mitigation. Section 3 presents the use-case of fleets of Automated Guided Vehicles for factory logistics, which serves as a recurring example in the following chapters. Section 4 describes a variety of anomalies that can occur in the use-case, highlighting the necessity to complement specific methods to identify and mitigate such anomalies. Section 5 presents appropriate identification methods, followed by methods for the future prevention of identified anomalies and the mitigation of their effect. Section 6 discusses the combination of the presented methods to achieve holistic anomaly management. Eventually, limitations and open points of the presented work as well as future work are outlined.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 1 Introduction
This two-fold effort will provide regional MODIS LAI validation across the prior (Mc5) and most recent (Mc6) algorithm updates and investigate the plausibility for modeled leaf area index (LAI) inputs to be used in conjunction with satellite-derived LAI data streams to support various regional and local scale air quality models for retrospective and future meteorological assessments.
# Quasi-Experimental Design for Medical Studies with the Method of the Fuzzy Pseudo-Control Group
## 5 Validation of the Case Study Conclusions
### Fuzzy DID Method (FDID)  
For the sake of internal validation, we will compare the acquired conclusions on the annuloplasty favorability with the results of the DID method. To improve the comparability of DID and MFFCG results, we developed a fuzzy DID that uses Bootstrap in the four discussed modifications to estimate the confidence interval (CI) of the average treatment effect (_ATE_):  
FDID Algorithm  
1. Select the significance level, \(\alpha\), and the number of pseudo-realities, \(N\), to construct the Bootstrap CIs.
2. Set \(i\) = 1.
3. Choose \(X=X_{i}\).
4. Extract from the database the fuzzy samples \(E_{b}\), \(PC_{b}\), \(E_{e}\), and \(PC_{e}\) for this \(X\).
5. Estimate the fuzzy mean values of the four samples \(M_{E,b}\), \(M_{PC,b}\), \(M_{E,e}\), and \(M_{PC,e}\) using (8).
6. Estimate the fuzzy DID estimator as the _ATE_: \[ATE=(M_{E,e}-M_{PC,e})-(M_{E,b}-M_{PC,b})\] (23)
7. Find the index of the empirical \(\alpha/2\)-quantile from the Bootstrap distribution of _ATE_, as \(Nd\) = round(\(N\)\(\times\)\(\alpha/2\)).
8. Find the index of the empirical (1-\(\alpha/2\))-quantile from the Bootstrap distribution of _ATE_, as \(Nu\) = round(\(N-N\)\(\times\)\(\alpha/2\)).  
* Repeat for Bootstrap Modification BM\(k\) (\(k\) = 1, 2, 3, 4):
* 9.1. Repeat for pseudo-reality \(r\) (\(r\) = 1, 2,..., \(N\)):
* 9.1.1. From sample \(E_{b}\), generate \(sE_{b,r}\), using Bootstrap Modification BM\(k\).
* 9.1.2. From sample \(PC_{b}\), generate \(sPC_{b,r}\), using Bootstrap Modification BM\(k\).
* 9.1.3. From sample \(E_{e}\), generate \(sE_{e,r}\), using Bootstrap Modification BM\(k\).
* 9.1.4. From sample \(PC_{e}\), generate \(sPC_{e,r}\), using Bootstrap Modification BM\(k\).
* 9.1.5. Estimate the fuzzy mean values of the four synthetic samples \(sM_{E,b,r}\), \(sM_{PC,b,r}\), \(sM_{E,e,r}\), and \(sM_{PC,e,r}\), using (8).
* 9.1.6. Estimate the fuzzy DID estimator as \(sATE_{r}\) in the \(r\)-th pseudo-reality: \[sATE_{r}=(sM_{E,e,r}-sM_{PC,e,r})-(sM_{E,b,r}-sM_{PC,b,r})\] (24)
* 9.2. Sort the synthetic \(sATE_{r}\) in ascending order and obtain \(sATE_{sort,r}\) (\(r\) = 1, 2,..., \(N\)).
* \(\alpha\))%-CI for \(ATE\) using Bootstrap Modification BM\(k\): \[\Pr[2\timesATE-sATE_{sort,Nu}<ATE<2\timesATE-sATE_{sort,Nu}]=1-\alpha\] (25)
* Set \(i\) = \(i\)+1.
* If \(i<n\), then go to step 3. Otherwise, end the algorithm.
# Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion
## 4 Experimental Results
### Quantitative Comparison
Firstly, in the case of high-resolution art paintings, our method excels in handling complicated structures and complicated textures. The integration of Weighted Laplacian Synthesis and patch-based completion across multi-regions ensures a more precise and specific completion of missing regions within the local area. The proposed algorithm guides the entire process, from segmentation to Weighted Laplacian Synthesis, patch-based propagation, and pyramid blending. This comprehensive approach results in visually pleasing and artifact-free art painting, as demonstrated in Figure 1.  
Moreover, our method employs the abundance of pixels in high-resolution paintings to make consistent completion decisions across various resolutions. Inspired by [PERSON] et al. [32], the use of Laplacian and upsampled Gaussian pyramids progressively enhances completion quality from the coarsest to the finest layer. Texture synthesis, employing Laplacian of a Gaussian pyramid at different levels, addresses structure diffusion challenges, ensuring enhanced edge awareness. The improved nearest neighbor search algorithm, employed for matching patches at different levels, ensures accurate matching of areas for robust completion. Our method innovates the voting similarity function by considering potential overlap of nearest neighbor pixels in color, even across different levels. This refinement results in a more accurate estimation of optimal pixel values for completing missing regions. To address errors introduced by ambiguity in restored pixels, patch-based propagation is seamlessly integrated, applying an isophote-driven technique locally. This ensuresthat matched patches align along the boundary of a hole between two distinct colored regions, with the best-matched patch duplicated into the target area based on confidence and data terms. In the context of low-resolution art paintings, our method demonstrates its versatility by handling randomly irregular missing regions effectively. The comparison with deep learning (EdgeConnect [27]) and traditional (Criminisi [1] and Laplacian [32]) methods showcases the robustness of our approach across different resolutions.
## 3 Simulation of Thermal Processes in the Food Industry Using CFD
### Examples of Thermal Processes Modeled Using CFD
#### 3.2.1 Baking, Frying, and Grilling Processes
For enhanced accuracy in predicting airflow patterns and heat transfer in grilling, LES models are often employed to capture the fine-scale turbulence structures and their interaction with the grill surface. LES resolves the large turbulent eddies directly while modeling the smaller scales using subgrid-scale models, providing a more precise description of the airflow and heat transfer around the food. CFD also facilitates the investigation of surface interactions such as Maillard browning and fat rendering. These processes are crucial for flavour development and product quality. To model these, CFD simulations incorporate reaction kinetics for the Maillard reaction and other thermal degradation processes, such as lipid oxidation [34; 78]. The temperature-dependent Arrhenius equation for reaction rates can be used (Equation (5)):  
\[k=Aexp\left(\frac{-E_{a}}{RT}\right) \tag{5}\]  
where \(k\) is the reaction rate, \(A\) is the pre-exponential factor, \(E_{a}\) is the activation energy, \(R\) is the gas constant, and \(T\) is the absolute temperature [79].
# Multi-Level Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
## 5 Experiments  
### Environment Setting  
MARL has gained great progress over recent decades but there exists a period the widely accepted benchmarks are missing. That is because the introduction of the neural networks improves the application scenarios of the MARL algorithms greatly and the toy cases used before are no longer good benchmarks for newly proposed algorithms. Thus, some challenging benchmarks are proposed to evaluate the performance of MARL algorithms. Among them, the StarCraft Multi-Agent Challenge (SMAC) provides the decentralized control of each agent and serves as the most popular, standard, and challenging benchmark [26].  
SMAC is developed based on the real-time strategy game, StarCraft II. Each agent in SMAC can observe its own states, i.e., health, shield, and others within its field of observation, it also observes other units' statistics such as health, location, and unit type. Agents can only attack enemies within their shooting range. Agents in a team will receive a global reward for battle victory, as well as damaging or killing enemy units. Each battle will last for at most 250 steps and may end early because agents in a team all die.  
SMAC provides a series of challenging tasks for MARL algorithms to learn and conquer. We select three classical tasks, 2s3z, 5m_vs_6m, and MMM2 to evaluate our proposed method. 2s3z is an easy task, 5m_vs_6m is a hard task and MMM2 is a super hard task. These three challenges with different levels of difficulties are able to demonstrate the performance of MLCA.  
Three very effective algorithms, DOP, COMA, and QMIX serve as baseline methods [11; 16; 21]. DOP is the most recent method and serves as the state-of-the-art method currently. DOP and COMA are policy-based and QMIX is value-based. We follow the setting in [16] where all methods use the same batch size (i.e., 32 episodes) and the same number of training iterations which is 2 million. All methods' performance is tested every 10,000 training iterations using 32 test episodes where agents act deterministically, All experiments are carried out with 3 different random seeds. The maximum and minimum values of the test win rate at different timesteps are plotted for every method in every challenge. Taking Figure 4 as an example, the horizontal axis represents the number of timesteps experienced by the agent, denoted as \(T\). \(T(mil)\) represents \(T\) in millions. The vertical axis represents the test win rate which ranges from 0 to 1. The MLCA is marked in red. The red line in the block denotes the mean test win rate, the upper and lower bound of the red area are the largest and smallest test win rate, respectively.  
In order to reproduce results easily, hyperparameters are listed in Table 1.
# Development and Field Testing of a Suspended Mulberry Branch Harvesting and Stubble Cutting Machine
## 3 Key Component Design
### Cutter Design
#### 3.1.1 Lifting Apparatus Design  
To ensure safe and reliable adjustment of the cutting height while adapting to terrain changes, a lifting device was designed for the cutting platform. The device includes key components, such as a hydraulic cylinder, lifting chains, tapered rollers, and an inner-outer guide rail frame, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Length \(\times\) Width \(\times\) Height of the header (mm \(\times\) mm \(\times\) mm) & 800 \(\times\) 980 \(\times\) 1260 \\ Harvesting width (mm) & 800 \\ Cutting stubble height (mm) & 0–300 \\ The linear velocity of the grain separation star wheel (m/s) & 0–3.5 \\ The linear velocity of the circular saw (m/s) & 0–90 \\ Forward speed (km/h) & 0–27.62 \\ Cutting inclination angle (\({}^{\circ}\)) & 10 \\ Star wheel height (mm) & 630–830 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main Technical Parameter Table of the Whole Machine.  
The inner-outer guide rail frame is constructed from parallel-welded channel steel, serving both as a planar support structure and as a stable moving track for the suspension frame and hydraulic cylinder. This structure is crucial for the lifting system's operation.  
Tapered rollers at both ends of the suspension frame slide along the outer and inner guide rails, respectively. The roller connected to the hydraulic cylinder moves along the inner guide rail, working in coordination to adjust the cutting platform height. One end of the lifting chain is attached to the top of the suspension frame, while the other end is fixed to the inner guide rail crossbeam. Adjustment bolts and nuts ensure the two chains remain equal in length, achieving balanced loading.  
The sprocket, sharing a coaxial point with the hydraulic cylinder's pivot, moves in a straight line under the guidance of the inner guide rail frame and tapered rollers, functioning as a movable pulley. The cutting platform is suspended from the outer guide rail frame by tapered rollers, driven by the hydraulic cylinder, and lifted vertically along the outer guide rail by the lifting chain.  
Figure 3: D Structure Diagram of the Lifting Device.  
Figure 2: Schematic diagram of the cutting platform.  
During operation, the hydraulic cylinder precisely controls the cutting platform's vertical movement. A four-point positioning mechanism ensures the platform remains upright throughout the lifting process, effectively preventing uneven cuts caused by angle deviations and supporting the healthy regrowth of mulberry branches.
## 1 Introduction
The mean of the distributions indicates the position in semantic space, and the variance of the distributions indicates the uncertainty for each KG components. Compared with previous works that regard relations as an operation between entities, co-embedding of entities and relations in the same semantic space can improve the performance of KG representation. For example, in Freebase, the relation 'Perfession' is used in ([PERSON], Perfession, Architect) and ([PERSON], Perfession, Screen Writer) uses two distinct semantic information categories, corresponding to a scientist and a writer, resulting in the finding that the resulting representations calculated using the two triples are not the same. The co-embedding model embeds entities and relations at the same semantic space, thus providing high-quality embeddings for both of them. In summary, our contributions in this work are as follows:  
1. We propose a co-embedding model for knowledge graphs, which learns low-dimensional representations for KG components, including entities and relations in the same semantic space, as a result of measuring their affinities effectively. 2. To address the issue of neglecting uncertainty, we introduce a variational auto-encoder into our model, which represents KG components as Gaussian distributions. The variational auto-encoder consists of two parts: (1) an inference model to encode KG components into latent vector spaces, (2) a generative model to reconstruct random variables from latent embeddings. 3. We conduct experiments on real-world datasets to evaluate the performance of our model in link prediction. The experimental result demonstrates that our model outperforms the state-of-the-art baselines.
# Parameter Conversion between Controlled Pass-By Method and Alternative Close Proximity Method
## 3 Results and Correlation Analysis
### Theoretical Derivation of the Propagation Relationship between Near-Field and Far-Field Noise  
We assumed that tire noise is a spherical wave. The expression of spherical wave attenuation with distance at normal temperature is:  
\[\mathrm{SPL_{CPBi}}=\mathrm{SPL_{CPXi}}-20\mathrm{lgr_{i}}-k \tag{1}\]  
where \(\mathrm{SPL_{CPBi}}\) is the far-field noise of the i-th tire measured by the CPB method (dB); \(\mathrm{SPL_{CPXi}}\) is the near-field noise of the i-th tire measured by the A-CPX method (dB); \(\mathrm{r_{i}}\) is the distance from  
Figure 4: The physical configuration for the extrapolation of signals acquired by the proximity of the tire signals.  
Figure 3: Tire noise correlation of CPB and A-CPX methods.  
the near-field microphone to the far-field microphone (m), where i = 1,2,3,4; and k is the correction factor. In free space, k = 11; in semi-free space, k = 8.  
When the distances between the near-field and far-field microphones are r\({}_{1}\) and r\({}_{2}\), the far-field tire noise measured by the CPB method can be expressed as follows:  
\[\text{SPL}_{\text{CPB1}}=\text{SPL}_{\text{CPX1}}-20\text{lgr}_{1}-\text{k} \tag{2}\]  
\[\text{SPL}_{\text{CPB2}}=\text{SPL}_{\text{CPX2}}-20\text{lgr}_{2}-\text{k} \tag{3}\]  
The difference in the sound pressure level between them can be expressed as follows:  
\[\Delta\text{L}=\text{SPL}_{\text{CPB1}}-\text{SPL}_{\text{CPB2}}=20\text{lg} \left(\frac{r_{2}}{r_{1}}\right) \tag{4}\]  
Ignoring the shielding effect of the vehicle body on tire noise, the above equation is used to calculate the sound attenuation values in the sound propagation path from the near-field microphone to the far-field microphone corresponding to the four tires. The theoretical value of far-field tire noise can be obtained by removing the sound attenuation value in the propagation path from the value of near-field tire noise.  
Since the distance between the near-field and the far-field microphones of T1 and T3, and T2 and T4 tires is the same, the theoretically calculated noise attenuation values in the propagation path are the same. The total noise prediction value at the far-field measurement point can be calculated using the four tires' noises using the superposition equation. The noise superposition equation can be expressed as:  
\[\text{SPL}_{\text{CPB}}=10\log_{10}\left(\sum_{i=1}^{n}10^{0.1\text{SPL}_{ \text{CPB}}}\right) \tag{5}\]  
where SPL\({}_{\text{CPB}}\) is the far-field noise of four tires measured by the CPB method (dB).  
The difference between the predicted noise and the measured noise at the far-field experiment point of five tread patterns is shown in Table 4. Table 4 shows that although assumptions were made in theoretical analysis, the difference between the measured value and the theoretically predicted value was still large. The average difference was about 6.56 dB. Therefore, it is not advisable to theoretically deduce the quantitative relationship between the noise experiment results of the two methods.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Experimental Evaluation
#### 5.4.5 Summary of Findings  
* The proposed EX framework preserves QoS feasibility under tight constraints significantly better than heuristic approaches.
* It scales robustly with increasing QoS dimensionality and supports improved coverage through intelligent BR deployment.  
Figure 8: Comparison of the number of feasible nodes under varying numbers and placements of border routers in scenarios (**a**) 60 nodes with a communication range of 25 and (**b**) 120 nodes with a range of 12.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Number of** & **Not Conn.** & **Feasible** & **Feasible** & **Active BRs** & **Feasible** & **Nodes in** \\  & **BRs** & **with the** & **First BR** & **(All BRs)** & **from 4** & **Nodes** & **DODAG** \\ \hline
1 st case & & & & & & & \\ \hline Nb of & 1 & 0 & 57 & 57 & 1 & 48 & 40 \\ Nodes = 60 & 2 & 1 & 50 & 58 & 2 & 58 & 47 \\  & 3 & 4 & 47 & 58 & 3 & 58 & 50 \\ Range = 25 & 4 & 5 & 48 & 59 & 4 & 59 & 54 \\ \hline
2 nd case & & & & & & & \\ \hline Nb of & 1 & 48 & 25 & 25 & 1 & 22 & 20 \\ Nodes = 120 & 2 & 55 & 20 & 40 & 2 & 42 & 29 \\  & 3 & 68 & 21 & 43 & 3 & 61 & 37 \\ Range = 12 & 4 & 57 & 22 & 82 & 4 & 82 & 46 \\ \hline \hline \end{tabular} Note: All numerical values are rounded to the nearest whole number. Original values represent the mean across multiple simulation runs.  
\end{table}
Table 9: Illustration for the effect of multiple BRs.  
Figure 9: The effect of the number of BRs also depends on the QoS requirement.  
By enabling conflict-free multi-DODAG routing, EX ensures both structural resilience and better performance metrics (feasibility, delay, and path uniformity).