esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Temperature Rheological Properties of Asphalt Mortar Modified with Spent FCC Catalysts
## 4 Conclusions  
1. At the same test temperature, the phase angle of S-FCC-C-modified asphalt slurry decreases, and the rutting factor increases with an increase in the S-FCC-C replacement dose, leading to a significant enhancement in the high-temperature performance of the modified asphalt slurry.
2. With the same dose, the phase angle of the S-FCC-C-modified asphalt slurry increases, and the rutting factor decreases as the test temperature rises. This results in a decrease in the high-temperature performance of the modified asphalt slurry, although it remains superior to limestone powder asphalt slurry.
3. The deformation recovery rates R\({}_{0.1}\) and R\({}_{3.2}\) increase as the S-FCC-C replacement admixture increases, leading to a substantial improvement in high-temperature performance, particularly at an 80% replacement admixture. Meanwhile, the average non-recoverable creep flexibilities Jnr\({}_{0.1}\) and Jnr\({}_{3.2}\) decrease, with a higher decrease rate observed at low dosing compared to high dosing. This signifies a significant enhancement in the recovery deformation capacity of the asphalt mastic at low S-FCC-C dosing, which gradually diminishes at higher dosing.
4. The alternative admixture of S-FCC-C enhances the filling effect of the filler in the asphalt mastic, resulting in a reversed increase in stress sensitivity at higher admixtures (negative Jnr-diff values). A 20% alternative admixture of HC, SL, and JX samples improves the stress sensitivity of mineral powder asphalt mastic. Therefore, 20% represents the optimal alternative admixture for HC, SL, and JX samples.  
Figure 10: Jnr-diff relationship between the replacement amount of S-FCC catalyst and the irrecoverable creep compliance difference at 60 \({}^{\circ}\)C.  
5. The asphalt mortar filled with JX exhibited the best rutting resistance and resilience performance among the five S-FCC-C asphalt mortars, suggesting that the surface area and hydrophilic coefficient of S-FCC-Cs co-affect the high-temperature rheological performance of asphalt mortar. Considering stress sensitivity, the use of JX can be recommended to replace 20% of mineral powder in the aggregate in the pavement construction.  
Methodology, investigation, data curation, and writing--original draft preparation, Z.W. and S.H.; writing--review and editing, supervision, and funding acquisition, L.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Plan Project of Chongqing Transportation Bureau, grant number CQJT2022 ZC09.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Methods
### Codification Scheme  
#### 2.1.1 Expression Language  
To encode the network structures, we define an expression language capable of simply representing many of the classification architectures found in the state of the art with some exceptions (e.g., DenseNet [76] is not supported due to the syntax we use for parallel processing branches). At this point, we only consider architectures with one input point and one classification output (architectures such as Faster-RCNN [77] with multiple outputs at different levels of the network are not supported in the current version of the system).  
The expressions have the following tokens as basic components:  
* \"in\", \"out\": They represent the network input and output, respectively.
* \"[\", \"]\": Start and end of parallel branches of processing that share the same input (branches are separated by \",\").
* \"direct\": Connection between two network points without any operator being applied (residual-like connection).
* Processing nodes with two main types:
* Traditional operators: convolutions (\"conv\"), max or average poolings (\"mp\" or \"ap\"), \"dropout\" [78], fully-connected layers (\"fc\"), \"softmax\" and \"flatten\".
* Aggregation operators: concatenation (\"concat\") or zero-padded sum (\"sum\"). The last one adds the features of all branches with the same channel index and concatenates the rest.  
Figure 3: VGNet: network architecture design workflow.  
Both aggregation operators are applied immediately after the closing of parallel processing branches to unify the results.  
Some of the traditional operators use a parametric syntax with format **operator[-parameter][0,n]**. The specific number of required parameters varies between operators:  
* \"conv-i-j-k l\": i (dimension), j (number of generated features), k (stride) and l (normalization used). Currently, only Batch Normalization (\"bn\") [79] or no normalization are supported by our system. We also performed some tests including both regular (\"conv\") and separable convolutions (\"sepconv\").
* \"mp-m-k\" and \"ap-m-k\": m (dimension) and k (stride).
* \"dropout-r\": r (rate).
* \"fc-s\": s (size).  
Some important notes: \"flatten\" is always present before the first FC layer and all layers use leaky-ReLU [80] activation (with the only exception of the output layer that uses softmax). Regarding the \"in\", \"softmax\" and \"out\" nodes, the input and output dimensions are not specified on the operators themselves since they are problem dependent and configured at library level.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 2 Methods
### Satellite Optical Scattering Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Satellite Design** \\ \hline \multirow{4}{*}{Orbit} & Sun-synchronous circular orbit at 500 km altitude, \\  & 7.6 km/s. Satellite visible to ground station from \\  & elevations of approximately 15\({}^{\circ}\) to 10\({}^{\circ}\). \\  & Approximately 2 m \(\times\) 2 m, coated in metallized \\ Satellite bus & polymer thermal shielding with an albedo of \\  & approximately 0.3. \\ \multirow{4}{*}{Satellite attitude} & Coarse pointing—satellite is bodily oriented \\  & towards ground station with 0.5\({}^{\circ}\) precision. \\ \multirow{4}{*}{Satellite QKD transmitter optic} & 300 mm aperture Cassegrain telescope. 10 \(\upmu\)rad \\  & divergence, 22 dB diffraction loss at 1200 km. \\ \multirow{4}{*}{Ground station QKD receiver optic} & 1 m aperture Ricthey-Chretien telescope. \\  & Approximately 16\% optical efficiency from \\ \multirow{4}{*}{QKD wavelength} & aperture to QKD receiver system. \\ \cline{1-1}  & 848.6 nm \\ \cline{1-1}  & QKD protocol \\ \hline \end{tabular}
\end{table} 
Table 1: QKD satellite design assumptions based on _Micius_.
## 2 Materials and Methods
### Data Analysis  
A descriptive analysis (median, minimum, maximum) was performed. Normality and homogeneity were assessed using the Shapiro-Wilk and Levene tests, respectively. The data subjected to the Shapiro-Wilk test indicated the absence of normality (\(p<0.05\)). Considering that these results violated the assumptions for the use of parametric testing and the sample size, the [PERSON] nonparametric analysis of variance for repeated measures was used. Additionally, the Durbin-Conover post hoc test for paired contrasts was used, avoiding multiple comparisons. Effect sizes (ES) were calculated using Cohen's d test. The ES were qualitatively classified as very small (0.01 to 0.19); small (0.20 to 0.49); moderate (0.50 to 0.79); large (0.8 to 1.19); very large (1.2 to 1.99); and huge (>2) [14]. Significance was set at \(p<0.05\). All statistical analysis was performed using the freeware JAMOVI (the JAMOVI Project, version 1.6.15, retrieved from [[http://www.jamovi.org](http://www.jamovi.org)]([http://www.jamovi.org](http://www.jamovi.org))).
## 4 Results and Discussion
### Optimization Results for Three Degrees of Freedom
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DE Versions** & \begin{tabular}{c} **Average** \\ \((\theta_{max})_{3m}\) \\ \end{tabular} & **Variance** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \
\begin{tabular}{c} **Standard** \\ **Error** \\ \end{tabular} &
\begin{tabular}{c} **RMSE** \\ **(\(\times 10^{-3}\))** \\ \end{tabular} \\ \hline DE1; DE3 & 0.020797 & 0 & 0 & 0 & 0 \\ DE2 & 0.021520 & \(1.91\times 10^{-6}\) & \(1.38\times 10^{-3}\) & \(2.52\times 10^{-4}\) & \(1.54\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Statistical metrics for achievement of \((\theta_{max})_{3m}\) for DE versions with \(NP=30\) and \(NG=100\). Figure 11 shows the evolution of the optimization process, considering one run for each algorithm, to obtain the three times minimized dimensionless maximum excess of temperature, \((\theta_{max})_{3m}\), and the corresponding optimal geometry, i.e., \((S_{1}/H_{0})_{0}\), \((\alpha)_{2o}\), and \((\beta)_{30}\). Figure 11a shows the optimization of \((S_{1}/H_{0})_{0}\), being possible to observe that, in general, the DE2 version reached an optimal ratio of \((S_{1}/H_{0})_{0}\) = 0.9 after almost 1000 simulations. In contrast, other versions of the DE algorithm alternated more, seeking the best configuration, and converged to a value only after 2000 simulations. The convergence velocity of the DE2 version is also observed in Figure 11b-d during the optimization process of \((\beta)_{30}\), \((\alpha)_{2o}\) and search for \((\theta_{max})_{3m}\). DE1 algorithm version presents a significant concentration of \(\beta\) values near the optimal value before 1000 simulations but only converged after 2000 simulations, as shown in Figure 11b. DE3 version is the algorithm with more scattered data for the explorations of the \(\beta\) search space with the advancement of simulations. According to the data of Figure 11b, the DE3 version concentrated the results near the optimal configuration of \(\beta\) near the 2000 simulations, converging after this point. For the angle \(\alpha\), Figure 11c shows that the DE2 version reaches the optimal value of \((\alpha)_{2o}=-53^{\circ}\) with approximately 750 simulations, i.e., 25 generations. It is also important to observe that, versions DE1 and DE3 did not converge to the optimal \((\theta_{max})_{3m}\) for all population individuals. However, these versions have reached the minimum value of \((\theta_{max})_{3m}\) for some individuals in the last generations. Slow convergence of these DE versions helps them to escape from the local minimum, explaining the results shown in Table 8 with null variance for all runs of the algorithms. On the other hand, the fast convergence of the DE2 version takes this algorithm to reach some local minimum, as shown in Figure 10b. Figure 10: Results of the [PERSON]’s test for minimization of \((\theta_{max})_{3m}\) optimization of tree degrees of freedom: (**a**) search space exploration of DE versions for one run; (**b**) results for thirty runs of each algorithm version.
## 4 Enterprise Network Functionality
### Network Virtualization and Cloud-Managed Networking  
Network virtualization increases flexibility and scalability of enterprise networks. This is achieved through the deployment of several virtual networks on a shared physical infrastructure via VLANs and SD-WAN technologies [78]. Segmented traffic, especially for applications with different data and security needs, can be managed in this manner by various organizations. Furthermore, cloud-managed networking technologies simplify IT operations for distributed networks by allowing remote configuration and management.  
Every technology, layer, and security approach in an enterprise network cooperates to guarantee movement of data freely and efficiently between devices and corresponding users [79]. In addition, cloud resources and branch and remote offices are all seamlessly incorporated into the core network to provide reliable, secure, and continuous access from any location. However, this continuous exchange of cloud resources across different platforms may lead to excessive consumption of energy. Thus, a green energy-saving computing solution, similar to the approach developed by [80], will prevent high energy consumption within the cloud-managed environment.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.18 uFlow  
This is an event based framework, where all actions are decomposed into tasks (simple tasks and composite tasks), designed to support software updates without losing runtime status--that is, no reboot is required [72]. Tasks are invoked by an event handler, through a task table, with parameters passed in a manner similar to a function call. All tasks are run to completion, and no task ever interrupts another. Control flow is provided through task hierarchies, Sequence Tasks, Conditional Branch Tasks, and While Loops. The uFlow framework enables dynamic code updates, without losing context, by changing the task event handling structures. New tasks can be added, unwanted tasks removed, and tasks modified by replacing them with a new task.  
uFlow provides no direct support for autonomous updates, but the ability to perform on-the-fly software updates without reboots could be a valuable feature.
The objective of this paper is to apply a mosaik-based co-simulation to study the transient stability consequences of converter-based RES with grid-forming control, and test the efficacy of the implemented cosimulation toolchain (i.e., Powerfactory, mosaik, FMI, FMI++). The scientific contribution is twofold: First, the implementation of a typical droop-based grid-forming converter-based generator within a co-simulation setting. This is novel for this type of controller and this paper proposes a workflow to validate this approach. Second, the extensive transient stability assessment of a converter-dominated power system applying this co-simulation toolchain. The approach is as follows. First, the co-simulation toolchain and its implementation in mosaik are detailed in Section 2. Its application towards transient stability assessment follows in Section 3. Here, a converter-dominated power system is split into (i) the power system modelled in Powerfactory, and (ii) the controller of the converter-based generator modelled in Simulink. Both subsystems are encapsulated into FMUs and are orchestrated by mosaik. The behavioural description, mathematical modelling, and implementation of the proposed grid-forming control into the monolithic reference simulation and into the co-simulation are described next; i.e., in Section 4. This framework is used to test the functionality of the controls, to validate their efficacy in a co-simulation setting against a monolithic powerfactory simulation, and to eventually apply this for transient stability analysis. This is performed by various deterministic (i.e., setpoint changes, short circuit response, and islanded operation) case studies in Section 5. Conclusions and recommendations are provided in Section 6.
# Multi-Scale Features for Transformer Model to Improve the Performance of Sound Event Detection
## 4 Experimental Results
### Evaluation Metrics  
The proposed Transformer model computes the posterior probability for each class in every time frame and identifies a sound event when this probability exceeds 0.5. To improve reliability, median filtering is applied to the probabilities across the frames before the final decision.  
The performance of the Transformer model is measured by the F-score and error rate (ER) using an event-based analysis [30], which compares the output of the model with the ground truth table when the output indicates that an event has occurred. The initial decision comprises three types: true positive (_TP_), false positive (_FP_), and false negative (_FN_). A _TP_ indicates that the period of a detected sound event overlaps with that in the ground truth table. In the decision, a 200-ms onset collar and a 200-ms or 20% of the event length offset collar are allowed. An _FP_ implies that there is no corresponding overlap period in the ground truth table, although the Transformer output indicates an event. An FN implies that there is an event period in the ground truth table, but the Transformer model does not produce the corresponding output.  
The F-score (_F_) is computed based on the initial three decisions, and is the harmonic average of the precision (_P_) and recall (_R_). These are computed as follows:  
\[P=\frac{TP}{TP+FP},\ \ R=\frac{TP}{TP+FN},\ \ F=\frac{2 PR}{P+R} \tag{7}\]  
The error rate is computed as:  
\[\text{Error rate}=\frac{S+D+I}{N} \tag{8}\]  
where \(N\) is the total number of sound events active in the ground truth table. Sound events with correct temporal positions but incorrect class labels are counted as substitutions (_S_). Insertions (_I_) are sound events present in the system output but not in the reference, and deletions (_D_) are the sound events present in the reference but not in the output [33].
## 3 Related Work
Second, they developed a human-machine joint subjective assignment scoring model by combining bidirectional long short-term memory (Bi-LSTM) with the TA-S3 WD model. In [45], the authors suggest a novel architecture built on top of recurrent neural networks (RNNs) and convolutional neural networks (CNNs). From the word embedding vectors and the important semantic ideas, the multilayer convolutional layer in their architecture learns and captures the contextual aspects of the word n-gram to create the feature vector at the essay level. To retrieve past and future contextual models, a bi-gated recurrent unit RNN (BGRU) version was used. In [46], the authors provide a technique for automating the prediction of subjective scores that is both more accurate and more streamlined than previous approaches. Twenty-one language characteristics are evaluated from the assignments, including syntactical, grammatical, emotional, and readability, to account for all the main considerations of a human evaluator. The Automated Student Assessment Prize (ASAP) competition dataset, hosted on Kaggle, is used in this investigation. Quadratic weighted kappa (QWK) is used as the assessment tool; it calculates the degree to which an algorithm's prediction matches a human-graded score. Using the discovered language characteristics, we explore four different machine learning methods. Among the chosen ML methods, a three-layer neural network with feature selection had the highest QWK. Table 2 summarizes the major categories that recent AES systems follow from the perspective of their strengths and limitations. Though research on automatic essay grading goes back several decades, it still has considerable room for improvement in terms of efficiency and applicability. The analysis indicates that previous research mainly focused on (1) evaluating essays based on their level of linguistic and vocabulary difficulty, (2) ignoring essay consistency (coherence), and (3) failing to give students feedback on their errors. According to our Google Scholar research, little attention has been paid to suggesting novel methods of assessing the grammatical and semantic quality of student essays, as well as checking for internal consistency and providing students with constructive criticism. Apart from feature extraction and training machine learning models, no system is accessing the essay's completeness. No system provides feedback to the student response and is not retrieving coherence vectors from the essay--another perspective on the constructive, irrelevant, and adversarial student responses still questioning AES systems. All research concentrated on extracting the features using some NLP libraries, training their models, and testing the results. Yet there is no explanation in the essay evaluation system about consistency and completeness. Another limitation is that there is no domain knowledge-based evaluation of essays using Machine Learning models. In the following section, we will dive into the specifics of the proposed AEE model, which is based on a neutrosophic ontology and aims to improve semantically- based evaluation.
## 3 Target Degradation Strategy (TDOS)  
The TDOS relies on the injection capability judgment (ICJ), which is proposed to judge the reachability of the original orbit by whether the remaining flight time \(t_{go}\) calculated by the ATU-IGM is greater than the maximum working time \(t_{fud}\) supported by the remaining fuel. When the launch vehicle does not have the flight capability of the original orbit, the orbit-injection strategy based on the target degradation rule is used to adjust the orbit parameters to reduce the mission loss caused by the power failure.  
The basic degradation methods are divided into two categories: the payload reduction method and the energy reduction method. A payload reduction method refers to a launch mission carrying multiple payloads with the same or similar function, but there is only a weak dependence between these payloads. After the fairing is thrown off, part of the payload can be released in advance so that the remaining payloads can enter the predetermined target orbit or safe orbit. An energy reduction method refers to matching the remaining flight capability of the launch vehicle after the power failure by reducing the energy required for the target orbit. In the second stage of flight, due to the smaller thrust of the launch vehicle itself, the thrust-to-weight ratio after a power failure is even smaller, the impact of the payload reduction method on the acceleration effect and climbing efficiency of the subsequent flight stages of the launch vehicle is not significant, and if the payload can be kept at an altitude lower than the original target orbit but higher than the minimum safety orbit, the mission can be saved to the greatest extent by the payload's orbit change capability or space rescue. Therefore, an energy reduction method is normally preferred over a load reduction method. The schematic diagram of the target degradation rule based on the orbit-injection strategy is shown in Figure 1.