esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Objective Function  
The objective of the model was to minimize total costs. Costs are composed of four elements: transportation cost, storage cost, infrastructure cost and technology cost.  
* Transportation costs result from each transport between two nodes \(n\) and \(nn\) and therefore depends on the distance \(dist_{n,nn}\), as well as on the transportation type allowed between those two nodes \(tt_{t,n,m}\). The transportation cost was calculated task-wise and did not depend on the different contents such as SNF or vitrified waste, yet each task caused fixed transportation costs \(cost\_t\_fix_{t}\) due to loading and unloading as well as variable transportation cost \(cost\_t\_var_{t}\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{4}{c}{**Final Repository**} \\ \cline{2-5}  & **Opening of Yuca Mountain** & **Opening of Second Final Repository** & **Need to Store all Waste in Yucca Mountain** \\ \cline{2-5}  & **2030** & **2060** & **2030** & **2060** \\ \hline \hline BAU & & & & \\ \hline Yuc\_2030 & ✓ & & & \\ \hline Yuc\_2060 & & ✓ & & \\ \hline Yuc+\_2030 & ✓ & & ✓ & \\ \hline Yuc+\_2060 & & ✓ & ✓ & \\ \hline Yuc\_reproc & ✓ & & & ✓ \\ \hline \hline \end{tabular} Source: own representation.  
\end{table}
Table 1: Scenarios implemented in the model.  
Figure 3: Scheme of model. Source: own representation.  
(although in reality only full casks were transported, we assumed that the high number of casks to be transported allowed modeling the number of casks as real numbers for the sake of a linear model).
* The storage cost depended on the amount of casks--independent of their content--stored in a single node per year and further depends on the local storage cost \(cost\_s\_var_{n}\).
* The infrastructure cost refers to the optional cost which are caused by building new CISFs \(cost\_B_{i}\).
* The technology cost were the combination of both vitrification cost \(cost\_VI\) as well as cost due to cask decay which will require the waste to be refilled into a new cask \(cost\_NC\). Although both these processes can only be conducted inside the hot cells, which requires further transportation, the technology cost refers only to the technological process.  
All costs are discounted by a constant rate, due to technological development. We chose a discount rate of 1%, which seems in the reasonable range, and has been chosen by a similar study [27].  
\[\begin{split} min\ totalcost=&\sum_{y}\Bigg{(} \Bigg{(}\sum_{n,nn}\Bigg{(}\sum_{t}\bigg{(}(tt_{t,n,nn}\cdot(cost\_t\_fix_{t}+ cost\_t\_var_{t}\cdot dist_{n,nn}))\cdot\Big{(}SNF\_t\_n,nn,y\\ &\quad+WAST\_NC\_t\_n,m,y+WAST\_VI\_t,nn,y\Big{)}\bigg{)}\Bigg{)} \Bigg{)}\\ &+\sum_{n}\big{(}\big{(}SNF\_s\_n,y+WAST\_NC\_s,y+WAST\_VI\_s,y \big{)}\cdot cost\_s\_var_{n}\big{)}\\ &+\sum_{i}\big{(}B_{i,y}\cdot cost\_B_{i}\big{)}+\sum_{n,hc} \Big{(}SNF\_t\_n,hc,y\cdot cost\_NC\Big{)}\\ &+\sum_{hc,n}\Big{(}v\_effect\cdot WAST\_VI\_h,nc,y\cdot cost\_ VI\Big{)}\Bigg{)}/\left(1+interest\right)^{y}\Bigg{)}.\end{split} \tag{1}\]
## 5 Results and Analytical Discussion
### Computational Efficiency Analysis
#### 5.2.2 Training Time Efficiency  
The training times required for each model are presented in Figure 3. Among the KNN variations, KNN (K = 7) needed just 0.162 s, KNN (K = 3) needed 0.164 s, and KNN (K = 5) required 0.175 s, thus demonstrating remarkable training efficiency. RFC was also relatively efficient during the training phase, requiring 16.455 s. By contrast, the DL models  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline RFC & 99.987 & 99.9 & 99.9 & 99.9 \\ CNN & 99.796 & 99.0 & 99.0 & 99.0 \\ SVM & 99.261 & 99.0 & 97.0 & 98.0 \\ LSTM & 99.251 & 97.0 & 99.0 & 98.0 \\ KNN (K = 3) & 99.136 & 99.0 & 99.0 & 99.0 \\ KNN (K = 5) & 99.093 & 99.0 & 98.0 & 99.0 \\ KNN (K = 7) & 99.097 & 99.0 & 98.0 & 99.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation of classification models.  
Figure 2: Detection time comparison across all models.  
demanded considerably more computational resources during training. LSTM displayed the longest training time at 4986.316 s (about 83 min), while CNN required 1618.678 s (almost 27 min). Requiring 85.322 s, SVM positioned itself between traditional ML methods and DL approaches.  
These differences in training times highlight important considerations for the cycle of model development and application. While KNN variants offer rapid training, which is helpful in constrained computing environments or scenarios requiring frequent model updates, their detection performance for DDoS attacks remains weaker compared to RFC. Although the DL models impose heavy computational demands during training, making them less suitable for contexts with limited computational resources, they still achieve respectable accuracy in DDoS detection. Table 4 reflects the computational efficiency for all models with ranking based on their performance.
## 1 Introduction  
For developing advanced driving assist systems (ADAS) and automated driving systems (ADS) in the automotive industry area, the real-car test often shows limitation because of the high cost, time constraints and the higher risk of accidents in the case of complicated traffic scenarios. To overcome these limitations, the vehicle driving simulator (VDS) has been used for driving tests and experiments. However, the VDS is also limited by the problem of simulator sickness [1; 2; 3; 4; 5].  
The motion platforms of the VDS have been used in real-time for many applications to simulate real-car motion [2; 3; 4; 5]. However, the motion platforms have limitations in mechanical motion areas, so the washout algorithms that limit motion areas while providing a sense of motion like the actual vehicle motion are needed [3; 4; 5]. The optimal control strategy, false cues reduction method in case of roll motion, enhanced yaw motion method and others are developed for flight simulator [6; 7; 8; 9]. However, since aircraft and vehicles have different motion characteristics, it is necessary to develop a washout algorithm suitable for vehicle motion.  
A representative washout algorithm is a classical one. Although a classical washout algorithm has advantages such as short analysis time and verified stability, there are several problems such that it is difficult to select cutoff frequency of used filters and the original signal is falsified on tilt coordination [3]. To overcome these problems, a various washout algorithm is proposed. Artificial intelligent algorithms, such as fuzzy and genetic algorithms, are studied for this area [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. It consists of the fuzzy and genetic algorithm to reduce simulator sickness. However, there are various tunning parameters in these algorithms; it is difficult to select an optimized parameter and apply it to the motion platform. Because the tilt coordination algorithm in the classical washout algorithm uses the low pass filter, studies have also been conducted to remove low pass filters [3]. However, as a result, the translational motion sensation is improved, but the rotational motion sensation is deteriorated. To improve the rotational motion sensation, a method for directly reflecting the angle value of the vehicle to the motion platform has been proposed [4; 5], but there is still a problem of a lack of mathematical consideration to select scale parameters.  
In this paper, we propose the new tilt coordination algorithm with proportional gain to generate enhanced translation motion sensation and prevent the rotational motion sensation from deteriorating and analyze parameters in the subregion scale method. Two algorithms are integrated and simulated based on the human sensation model to verify their performance.
## 6 Applications and Simulations
### Data Storage and Retrieval
#### 6.2.2 Route Data and Query  
Once a datum is mapped to a point location \((\rho,\alpha,\beta)\), it is routed toward the location by following our proposed greedy routing algorithm. In each hop of routing, it checks if \((\rho,\alpha,\beta)\) is inside the current tetrahedron. If it is true, the routing terminates, and the datum is stored by one of the tetrahedron's vertices that has the lowest load. Query and retrieval of data can be realized similarly. The data being queried is mapped to a point location, and the query is routed to the corresponding tetrahedron that contains the requested data.
# ObFuzzer: Object-Oriented Hybrid Fuzzer for Binaries
## 2 Background  
Fuzzing has become a mainstream approach in recent years due to the presence of AFL. AFL has set the standards for the fuzzing field with its approach to program execution, information acquisition and coverage comparisons. Many technologies have been proposed to increase the ability of coverage-guided vulnerability discovery. CollAFL solves the coverage collide problem [2]. With more precise coverage, the coverage-guided fuzzer can obtain better results. INSTRIM [3], INSTRCR [4] and ZAFL [5] use highly efficient compilation techniques to improves target program execution speed. The \"laff-intel\" changes the binary roadblocks and tests the code behind roadblocks [6]. Angora uses a principled search to solve the magic bytes in programs [7]. VUZZER uses static analysis and taints tracking methods to improve the test cases' efficiency of coverage-guided vulnerability discovery [8]. PIN [9] and QEMU [10] use the dynamic instrumentation technology to extend the coverage-guided fuzzer to closed-source targets. PTFuzzer uses Intel Processor Trace technology to obtain trace information with low overhead on closed-source targets [11]. NEUFuzz uses deep learning technology reordering seeds to increase the finding vulnerability possibilities [12]. ParmeSan [13] and AddressSanitizer [14] use Sanitizer as a new guidance method to try to trigger potential vulnerabilities in the program. Avatar [15], Avatar2 [16] and IoTFuzzer [17] extend traditional vulnerability discovery to embedded devices. P-Fuzz uses parallelization to improve performance utilization in fuzzing environments more effectively, enhancing the fuzzer performance [18].  
As fuzzing techniques become more developed, their ability to discover vulnerabilities decreases. Its original high-performance advantage is no longer enough to compensate for the difficulty of discovering new deficiencies in pure coverage guidance at this stage. Therefore, to overcome the shortcoming in traditional coverage-based fuzzing, hybrid fuzzing is proposed. Static analysis and symbolic execution are added to traditional fuzzing to assist fuzzing in discovering new vulnerabilities. Driller [19] provides a simple hybrid fuzzing idea. It introduces symbolic execution to assist in improving the efficiency of vulnerability discovery when it is difficult to improve coverage with fuzzing. QSYM [20] focuses on making symbolic execution more suitable for hybrid fuzzing usage cases. [PERSON]'s [21] goal is to improve the reuse rate of information obtained during the symbolic execution phase and to use the results of constraint solving to guide test case generation in the fuzzing phase. SAVIOR [22] proposes a bug-driven hybrid fuzzing approach. It focuses more attention on the parts of the code that have bugs by the Undefined Behavior Sanitizer. HFL [23] improves the applicability of hybrid fuzzing and introduces hybrid fuzzing into kernel vulnerability discovery. How to combine multiple techniques more effectively to discover more vulnerabilities has become a hot research point in this field.
# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 5 The Emergency Strategy Case Study: Needs Analysis and Formalization
### Emergency Strategy Overview
#### 5.2.1 KG Enhancement  
The KG enhancement process consists of two key steps, executed during the pre-processing phase:  
Step A: Adding a risk attribute to diseases:  
A Boolean attribute named _risk_? is introduced for each disease in the KG, indicating whether the disease requires immediate attention to rule out life-threatening conditions.  
Step B: Incorporating patient indicators as Boolean rules:  
Three key indicators--age, gender, and pre-existing conditions--are integrated into the KG. To capture the influence of these indicators on life-threatening conditions, a new edge type, _conditional SymptomOf edges_, is defined. These edges include an attribute formulated as a logical rule involving one or more indicators connected with AND/OR operators.  
For instance, if a symptom, s1, indicates a life-threatening disease, d1, in male patients over the age of 60, the rule governing the relationship between s1 and d1 would be \"age > 60 AND gender = M\". This rule is represented using a Boolean function, where if the function evaluates to true, the edge s1->d1 exists; otherwise, it does not. This implies that the edge is conditional, hence the term _conditional edge_. Refer to Figure 2 for the enhanced knowledge graph, which includes rules defining the _conditional SymptomOf edges_.  
Step C: Categorizing SymptomOf edges:  
The KG distinguishes between two types of edges:  
Unconditional edges represent static relationships universally applicable to all patients.  
Conditional edges reflect relationships dependent on patient-specific indicators, dynamically included in the patient's graph during runtime if the logical conditions are met.  
This dynamic adjustment enables the KG to adapt to individual patient profiles, enhancing precision and personalization in the diagnostic process.  
Figure 2 illustrates an enhanced KG including two diseases (d2, d5) marked as high-risk and conditional edges (e.g., the edge s5 -> d3, defined by the rule \(\text{age }<\text{ 2}\)).
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Beyond IoT Technologies
#### 2.6.2 Fog Computing  
Fog computing serves as a link between cloud systems and edge devices, creating a safe intermediate layer through which data can be gathered, filtered, and analyzed geographically [58]. In terms of smart grid security, it enables synchronized anomaly detection and threat response across distributed zones. Fog computing enhances IoT performance by mitigating central system overload, enabling load prediction, behavior modeling, and threat pattern detection on a larger scale when connected to IoT nodes [59].
# Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
## 4 Experiments and Results
### Evaluation of Local Motion Controlling  
To evaluate the LSTM-based network, we compare IA3C with A3C+, which uses an FC layer to substitute an LSTM layer of IA3C's network in the training process. State values denote the outputs of the critic-network, which can reflect the performances of learned policies [7]. Success rates are probabilities of accomplishing training tasks in the last 1000 episodes.  
In Figure 7(a), when the episode is less than 20,000, A3C+ performs better than IA3C. It demonstrates that A3C+ can learn reactive policies to deal with simple learning tasks that only require a robot to navigate for short distances while avoiding a few moving obstacles, and the learning efficiency of LSTM-based networks is slower than that of FC-based networks. When the complexity of learning tasks increases further (i.e., the episode increases to 20,000), the performance of IA3C is better than that of A3C+. It proves that the reactive policies generated by A3C+ only consider current observations, while IA3C can integrate current observations and abstract history information to learn more rational policies. Besides, modified curriculum learning can transfer experiences from learned tasks to new complex tasks, which is more suitable to accelerate the training efficiencies of LSTM-based networks. In Figure 7(b), the data of success rates presents the same trend as that of the mean V-value, which further demonstrates the former conclusion.  
To evaluate the effectiveness of the novel reward function framework, we remove individual reward shaping or single modified curriculum learning from the reward framework, but there is no  
Figure 8: The performances of IA3C and A3C+ in the training phase.  
way to converge. It shows that the proposed reward function framework can solve the problem of sparse reward and be beneficial for learning useful policies.
## 5 Experimental Results
### Experiment Evaluation
#### 5.2.2 Accuracy Evaluation  
HTPR was compared with basic CF, UCCF and ICCF on the prediction quality (MAE), and on the quality of recommendation (Precision, Recall and F1) to evaluate accuracy.  
(1) Comparison of HTPR with CF, UCCF and ICCF on MAE. In this experiment, the number of clusters and nearest neighbors for item clustering was respectively 40 and 30; for user clustering, the number of clusters was 50; the similarity threshold \(\theta\) was 0.7; the regulating parameter \(\rho\) was 0.4. We varied the number of nearest neighbors from 10 to 100 with a step of 10.  
Figure 10 shows the MAE values of CF, ICCF, UCCF and HTPR. It can be found that the MAE values of ICCF, UCCF and HTPR were much lower than CF. In addition, the MAE value of HTPR was the lowest, the curve of HTPR shows down first and then up, and the MAE achieved a lowest value 0.732 when the number of nearest neighbors was 40. Thus the proposed HTPR method was proven to provide more accurate predictions than CF, ICCF and UCCF.  
(2) Comparison of HTPR with CF, ICCF and UCCF on precision, recall and F1. In most recommendation applications, users would be recommended a personalized recommendation list, i.e., Top-\(N\) list. The services/items in higher positions should be more appropriate than the services/items in lower positions of the Top-\(N\) list. Precision, recall and F1 were used as evaluation metrics to evaluate the quality of the Top-\(N\) recommendation list. The experiment setup was the same as for the previous comparison, except that the number of nearest neighbors for user clustering was set as 40 according to the above experiments.  
Figure 10: Comparison of CF, ICCF, UCCF and HTPR in MAE.  
Figure 9: Impact of the regulating parameter on MAE.  
integrating collaborative filtering and clustering techniques. In this hybrid two-phase recommendation method, the feature matrix was developed to alleviate the sparsity problem by using a feature description and combination approach, the user-item category tendency was defined to integrate users' preferences with users' concern degrees for item category, the concept of integrating similarity between users was proposed by considering user behaviors and their frequencies, a parallelized strategy to optimize the recommendation process was also studied. The experiments from a real-world dataset indicated that the proposed HTPR method was effective.  
Our future work will focus on the application of the HTPR method and analyze the strengths and weaknesses of the real-time group-buying e-commerce application.  
Methodology, writing--original draft preparation, L.B.; software and validation, M.H.; data curation, Y.M.; conceptualization, review and editing and funding acquisition, M.L.  
Research was funded by the National Nature Science Foundation of China, grant number 61573257 and 71690234.  
We thank [PERSON], the Editor and the reviewing experts for their helpful suggestions. The authors declare no conflict of interest.
## 1 Introduction
These include auto-encoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, and more. Deep learning represents an attractive option to process massive data for RUL prediction. [PERSON] and [PERSON] [28] presented a deep learning-based bearing RUL prediction method using a deep brief network, and compared RUL prediction performance with a particle filter-based approach. Their comparison results showed that even though the RUL prediction performance of the deep learning based approach was comparable with particle filter-based approach, the RUL prediction accuracy of the deep learning-based approach was slightly lower than that of the particle filter. Therefore, it was of interest to investigate an integrated method that can use the strength of deep learning to overcome the limitations of the particle filter. To date, no research on combining deep learning with particle filter for RUL prediction of hybrid ceramic bearings has been reported in the literature. In this paper, a new integrated method that combines a deep belief network with a particle filter for remaining useful life prediction of hybrid ceramic bearings using vibration signals was presented. Real vibration data collected from hybrid ceramic bearing run-to-failure tests were used to test and validate the integrated method. The performance of the integrated method was also compared with a deep belief network and particle filter-based approaches.
## 3 Comparative Analysis of FEM-Calculated and ART-Measured Eigenfrequencies
### Step-by-Step Analysis of Modeling Errors of Simulated Eigenfrequencies
For the three models assumed, the averaged difference between simulation and measurement has a value of approx. 1.68%, 0.72% and 0.46% by amount. It is also worth mentioning that the range of the error bars can be reduced from almost 2% to less than 1% when using the orthotropic model. Still, significant deviations can be observed regarding the simulation data, even with the improvements due to the updated material properties. This is explained due to numerous idealizations and a quite high degree of uncertainty in the elastic constants. Regarding the experimental procedure for material characterization, including many assumptions and simplifications, a more elaborate procedure and other material models could lead to more realistic material data. Therefore, there is some further potential for improving the differences from simulation data to measured eigenfrequencies. Of course, the results regarding material vary when using a geometric model with higher accuracy. However, the effect is not decisive since geometry updating showed only small influences. Figure 7: Relative deviations between eigenfrequencies of virtual representations simulated by FEM using different material models and eigenfrequencies of the machine-made reference parts measured by ART depending on vibration mode (averaged values from all reference part; red line: overall average; \(\rho\): density; \(E\) and \(G\): Young’s modulus and shear modulus; \(C_{11}\) to \(C_{23}\): orthotropic elastic constants). As a conclusion, the differences between simulated and measured eigenfrequencies are due to a combination of many influences. A part of them cannot be quantified or only with non-representable effort. Therefore, it must be noted that a physical-based model updating seems to not be suitable to reach an appropriate accuracy for simulated eigenfrequencies. There will always be many aspects that cannot be modeled with a reasonable effort or parameters which are in principle not accessible, for example, regarding geometry, material properties, damping effects, or other practice-relevant influences. Hence, irrespective of the effort involved, a physical-based model update will eventually reach its limits. Therefore this does not seem reasonable for a transfer into practice. That is why we use a more pragmatic model-like approach to reach valid simulation data close to measured data.