esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Models  
To complete the tasks, the following ML methods were applied: \(RF\), \(SVM\), \(KNN\) and _Extremely Randomized Trees Classifiers_ (_Extra-Trees_).  
RF is an ensemble of Decision Trees [35]. RF trains mainly by means of the bagging method [36]. The workflow behind bagging is as follows: it first trains decision trees in randomly selected subspaces, then receives the prediction from each tree, and eventually, by combining the results, it uses voting to select the best solution. It should be noted that sampling is performed with replacement. Compared to individual trees, bagging causes a reduction in variance, which, in turn, solves the issue of overfitting.  
The concept of Extra-Trees was developed by [PERSON] et al. [37], who described the model in detail and presented its advantages compared to other tree-based ensemble methods. Extra-Trees differs in the way it builds decision trees. It does not require bootstraping observations, and the splitting of the nodes and selection of the cut-points are performed randomly. Randomization improves accuracy and reduces the computational time; it also diminishes variance, which is an issue for other tree-based methods (Classification and Regression Trees, C4.5, etc.).  
SVM aims to find a hyperplane that can classify data points. There may be multiple hyperplanes, and SVM tries to find a hyperplane with a maximum margin. The dimension of the hyperplane depends on the number of features; specifically, it is equal to the number of features minus 1. To solve the problem of finding a nonlinear decision boundary, SVM uses a kernel trick [38].  
KNN is a type of lazy and non-parametric learning algorithm. It works based on the concept of proximity, especially given the belief that closer things share more similarities. The K value is used to control and adjust the proximity distance. KNN uses various distance calculation functions, such as Euclidean distance, Manhattan distance, Hamming distance, etc. The choice of a specific function depends on the data structure. One of the disadvantages of KNN is its high computational cost [39].
## 2 Methodology
### Vibration Signal Data Collection  
In this study, vibration signals are collected using a triaxial accelerometer. To capture these signals at each stage of construction, accelerometers are installed on the web of the H-beam that supports the HBM. The triaxial accelerometer is designed to record acceleration data as analog signals, represented by voltage values from the sensor. The collected raw signals are subsequently amplified and filtered, converting the analog quantity into a stable output signal. This output signal starts as a voltage value and is ultimately converted into a digital signal using an analog-to-digital converter.  
The vibration signals are collected using a data acquisition system with a sampling frequency of 125 Hz. This system is connected to an accelerometer for vibration signal acquisition, storage, and preprocessing. For each reading, the accelerometer generates three data points, each corresponding to the sensor's measured values in the X, Y, and Z directions. This data acquisition system ensures the accuracy and reliability of the collected vibration signals, thus providing a solid foundation for subsequent signal analysis and processing. Figure 3 depicts the installation position of the accelerometer and the measured values in four states.  
The 125 Hz sampling frequency was chosen based on two main considerations. First, given that the premium frequency band of the HBM structure and its ancillary equipment is about 0.5-40 Hz, 125 Hz is an effective sampling rate according to the Shannon sampling theorem. This ensures accurate measurements by adequately sampling signals at at least twice their highest frequency. Second, the system's reliance on wireless data transmission and measurement required a frequency that would minimize packet loss. Empirical evidence from the pre-tests shows that a sampling rate of 125 Hz kept the wireless packet loss rate below 0.5%. In contrast, higher rates exacerbated packet loss, threatening the integrity and reliability of our data.  
Samples were collected at the four states of the HBM construction stages for classifier training, including the climbing, operating, pumping, and idling states. During the data collection stage, construction personnel followed their standard operating procedures to avoid any abnormal or irregular construction activities. Vibration signals were continuously collected throughout all the construction stages, with any abnormal vibration signals or events being recorded and marked for further analysis.  
Reliable construction site datasets are important for utilizing machine learning methods [56]. To ensure high data quality, a preliminary analysis of the vibration signals was conducted to remove any outliers or noises. The segmented data were then used to extract the time-domain, frequency-domain, and time-frequency-domain features.  
Figure 3: Installation position of the accelerometer and the measured values in four states.
# A Location-Time-Aware Factorization Machine Based on Fuzzy Set Theory for Game Perception
## 4 Experiments and Results in Discussion
### Evaluation Metrics  
For the game user perception problem, we choose the area of area under curve (AUC curve), precision, recall, F-measure, and training time for evaluation. The metrics are listed in Table 3. First, we should know that true positives (TP) is the number of users who are predicted to be positive classes. False negative (FN) is the number of users who predict the positive class as the negative class. True negative (TN) is the number of users whose negative class is predicted as a negative class. False positive (FP) is the number of users who predict negative classes as positive classes.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Feature** & **Data Type** & **Description** & **Factor Matrix** \\ \hline Player Id & Categorical & Participant number & Users \\ Sex & Binary & Participant sex & Users \\ Skill & Categorical & Skill level of participants & Users \\ Time & Numeric & End time of each game & Services \\ Game IP & Numeric & The IP address of the game server & Services \\ IP Home & Categorical & Home of game server & Services \\ Service Operator & Categorical & The operator of the game server & Services \\ Game Result & Binary & The final result of the game & Services \\ Game Mode & Categorical & Test the different game modes selected & Services \\ Game Team & Categorical & Team of participants entering the game & Services \\ Extra Delay & Numeric & Additional accumulated delay & Services \\ Extra Jitter & Numeric & Additional accumulated jitter & Services \\ Extra Packet Loss & Numeric & Additional accumulated packet loss & Services \\ Delay & Numeric & Total delay per game & Services \\ Jitter & Numeric & Total jitter per game & Services \\ Packet Loss & Numeric & Total packet loss per game & Services \\ Max-Min & Numeric & The difference between the best value of & Services \\  & & the total delay per game & Services \\ Kill & Numeric & Kill record in the game & Services \\ Death & Numeric & Death record in the game & Services \\ Assistant & Numeric & Assistant record in the game & Services \\ Score & Categorical & Game perception evaluation score & Label \\ \hline \end{tabular}
\end{table}
Table 2: Characteristic description.  
The AUC curve represents the area under the receiver operating characteristic curve (ROC curve). The larger the AUC, the better the model performance. Precision is used to calculate the proportion of correct predictions among all samples with a positive prediction class. The recall is the proportion of positive class samples correctly determined by the classifier to the total number of positive class samples. In general, the higher the accuracy rate, the lower the recall rate. In order to balance the effects of precision and recall and to evaluate a classifier more comprehensively, a comprehensive metric, the F-measure, was introduced. The value of the F-measure will be high when both accuracy and recall are high. The training time is used to compare the running speed of each algorithm. The shorter the time, the faster the algorithm runs.
## 2 Background
### Function Model (FM) and Functional Analysis Diagram (FA)  
The FM is a model reflecting the structure of functional relationships (i.e., functions, actions) between the components of the System and its Super system [31]. Functions are characterised by: category (useful, harmful, neutral), quality of performance (insufficient, excessive), cost level (insignificant, acceptable and unacceptable), and cost of corresponding components. The graphic representation of FM is the FA diagram (Functional Analysis Diagram).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Action Character** & **Graphic Notation (Symbol Action)** \\ \hline Desired effect (useful) & \\ \hline Insufficient effect & \\ \hline Excessive useful effect & \\ \hline Harmful effect & \\ \hline Lacking desired useful effect & \\ \hline Missing action & \\ \hline Break of connection & \\ \hline Transformation & \\ \hline Results in & \\ \hline Poorly controlled & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic actions (functions) in the SFA/FA notation (developed by authors based on [26; 39; 40; 41].  
The FA Diagram is an oriented graph with vertices corresponding to system components and edges reflecting their mutual interactions [31; 32]. Arrows point to the action direction. In some versions of the model, the edge pattern reflects the action character (useful or harmful) and the level of performance (sufficient, insufficient, excessive) in the notation of the Substance-Field Analysis (Table 2). Use of the SFA notation allows for the visualization of the existing conflicts, the combination of the modelling process with the problem setting process, the selection of a potential tool for solving the problem, and the solving of the problem by applying the SFA or PhC/TC tools. The function rank (index), value and cost can be specified for the purpose of VE (value engineering).
## 2 Materials and Methods
### Finite Element Analysis  
As mentioned above, in order to perform the experimental calibration of both the critical stress and the critical distances, and also with the aim of subsequently predicting the LCCs of the U-notched polymeric specimens, finite element (FE) analysis is required. Herein, FE analysis was conducted by employing the commercial code ABAQUS/CAE 6.10. Two-dimensional (2D) plane-stress FE models were created according to the dimensions of the U-notched specimens shown in Figure 2a. In order to apply the real loading conditions to the created model, the average critical load for each combination of notch radius and material (see Section 3) was applied to the top line of the specimen model as a uniformly distributed compressive load (see Figure 3). To create appropriate boundary conditions, as shown in Figure 3, the bottom line of the model was completely fixed by constraining the entire set of nodes located on this line.  
Regarding the 3D effects, it should be underlined that the authors also created 3D models and performed the corresponding linear elastic analyses. As expected from previous experience, minor differences (always less than 4%) were found between the stress distri  
Figure 3: Loading and boundary conditions for the U-notched specimen subjected to compressive loading.  
bution in 2D plane-stress/plane-strain model and that in the 3D model, from which the stress values in the mid-plane were taken into consideration. Similar successful 2D FE simulation of 3D notched brittle specimens under compressive loading has also been reported previously in the literature (e.g., see [33]). For compressed notched components made of brittle materials with limited deformations, the stress triaxiality may be neglected. Thus, in notched brittle components, 3D specimens are usually simulated by 2D plane stress/strain models. This practice becomes inaccurate when the strain level (and triaxiality) becomes sufficiently large.  
A suitable convergence of the FE results was obtained by performing several FE analyses with various numbers of elements. Finally, FE models containing around 161,000 eight-node plane-stress quadratic elements with a medial axis mesh algorithm were used to obtain the desired outputs. The fine sweep mesh pattern, with a size of 0.02 mm, utilized around the U-notch border is shown in Figure 4.
# Influence of Micro-Textures on Cutting Insert Heat Dissipation
## 2 System Design and Implementation
### CAD Modelling  
The CAD model was developed in SolidWorks(r) (Waltham, MA, USA) to reproduce the geometry and the heat transfer from the cutting insert surface to the cooling fluid that occurs during the machining process. The final model was found after a few iterations taking into consideration the objectives and the constraints of the problem. Figure 1 depicts the final model with the identification of its main components. The heat flux is introduced into the cutting insert lateral faces, the coolant fluid is guided over its upper surface and the inlet and outlet temperatures are monitored to quantify the heat transfer rate. The main body (1) has the purpose of supporting and fixing all components, with the additional function of providing thermal inertia equivalent to a tool holder. The support seat (2) includes slots to allow the installation of two thermocouples to monitor the main body temperature and four thermocouples to monitor the lower surface temperature of the cutting insert (3). The main stud (4) is used to guide and fix the support seat, the insert, and the top chamber (5) to the main body (1). The heat power is laterally introduced into the insert by its contact with the two heat sources (6). Each heat source is capable of generating 200 W, through the mounting of 4 resistive elements of 50 W each (7), making a total power generation of 400 W.  
Figure 1: CAD model with components identification: (**a**) full assembly; (**b**) middle cross-section.  
To enable the assembly of these elements and ensure a good heat transfer, the heat source elements are made of aluminum and possess four reamed holes. The compression force generated by the helicoidal springs (8) mounted on the two threaded rods, guarantees permanent pressure between the cutting insert and heat source surfaces, and minimizes the stresses created by the thermal expansion of the materials. The top chamber includes the coolant inlet and outlet channels (9) and (10), respectively, and two instrumentation channels, (11) and (12), for the temperature gradient measurement, needed for the evaluation of the heat transfer rate. In the center of the chamber, a core element (13) was added to promote the uniform coolant fluid flow over the entire top surface of the cutting insert. The top chamber sealing is ensured by the compression force against the insert cutting edge, produced by the tightening of the nut (14), located on the top of the main stud (4). The two guide pins (15) ensure alignment between the main body (1) and the top chamber (3).
## 4 Conclusions  
In this paper, mesoporous NiO hierarchical nanomaterials were prepared by a facile hydrothermal method and subsequent calcination. By adjusting the ratio of Ni\({}^{2+}\)/HMT, the precursors of Ni\({}_{3}\)(NO\({}_{3}\))\({}_{2}\)(OH)\({}_{4}\) nanosheet, Ni\({}_{3}\)(NO\({}_{3}\))\({}_{2}\)(OH)\({}_{4}\) microsphere, and Ni(HCO\({}_{3}\))\({}_{2}\) sub-microsphere were prepared. After calcination at 400 \({}^{\circ}\)C for 2 h, the precursors can be transformed to mesoporous NiO nanosheet, microsphere, and sub-microsphere. When used as additives for AP thermal decomposition, the as-prepared NiO nanomaterials showed an obvious catalytic role. Among them, NiO sub-microspheres have the best catalytic role, which reduces the HTD and LTD temperature by 75.2 and 19.1 \({}^{\circ}\)C, respectively. Therefore, the synthetic route adopted in this paper can rapidly prepare mesoporous NiO hierarchical nanomaterials, which can be used as AP thermal decomposition additives.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/13/2599/s1](http://www.mdpi.com/2076-3417/9/13/2599/s1)]([http://www.mdpi.com/2076-3417/9/13/2599/s1](http://www.mdpi.com/2076-3417/9/13/2599/s1)), Figure S1: EDS spectrum of (a) NiO nanosheet; (b) NiO sub-microsphere, Figure S2: (a) Low-magnification TEM image, (b) high-magnification TEM image; and (c) HRTEM image of the precursor of Ni\({}_{3}\)(NO\({}_{3}\))\({}_{2}\)(OH)\({}_{4}\) microsphere.  
**Author Contributions:** [PERSON] performed the experiments and wrote the paper; [PERSON] conceived and designed the experiments, analyzed the data, and revised the paper.  
**Funding:** This work was funded by Natural Science Foundation of Fujian Province (2017)[01676], the Key Research Foundation for Young Scholars of Fujian Education Department of China (JZ160486), and the Program for New Century Excellent Talents in Fujian Province University (Minjiaoke (2018) No. 47).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 7: (**a**) Differential thermal analyzer (DTA) curves of pure ammonium perchlorate (AP) and AP mixture with as-prepared NiO additives; (**b**) histogram of high temperature decomposition (HTD) and low temperature decomposition (LTD) temperatures in (**a**).
###### Abstract  
Lithium-ion batteries have become the most promising energy storage devices in recent years. However, the simultaneous increase of energy density and power density is still a huge challenge. Ultrafast laser structuring of electrodes is feasible to increase power density of lithium-ion batteries by improving the lithium-ion diffusion kinetics. The influences of laser processing pattern and film thickness on the rate capability and energy density were investigated using Li(Ni\({}_{0.6}\)Mn\({}_{0.2}\)Co\({}_{0.2}\))O\({}_{2}\) (NMC 622) as cathode material. NMC 622 electrodes with thicknesses from 91 \(\upmu\)m to 250 \(\upmu\)m were prepared, while line patterns with pitch distances varying from 200 \(\upmu\)m to 600 \(\upmu\)m were applied. The NMC 622 cathodes were assembled opposing lithium using coin cell design. Cells with structured, 91 \(\upmu\)m thick film cathodes showed lesser capacity losses with C-rates 3C compared to cells with unstructured cathode. Cells with 250 \(\upmu\)m thick film cathode showed higher discharge capacity with low C-rates of up to C/5, and the structured cathodes showed higher discharge capacity, with C-rates of up to 1C. However, the discharge capacity deteriorated with higher C-rate. An appropriate choice of laser generated patterns and electrode thickness depends on the requested battery application scenario; i.e., charge/discharge rate and specific/volumetric energy density.  
Keywords: lithium-ion battery; thick film electrode; ultrafast laser ablation; NMC 622; cyclic voltammetry; galvanostatic measurement +
Footnote †: journal: Applied Physics & Technology Letters
# Q-Learning Approach Applied to Network Security
## 4 Results  
We evaluated the performance of Q-learning for intrusion detection response. Unlike the regular machine learning methods limited to intrusion detection and classification, the Q-learning method can create an intrusion response mechanism. We developed a Q-learning model to train an agent that learns the actions that maximize the action-value function for various network conditions. The agent aims to improve the response by determining the actions that maximize his expected reward.
# BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization
## 4 Experiments
### Fine-Tuning Datasets  
Abstractive summarization is a challenging natural language processing task because it entails conveying the key information contained in a long piece of text into a short summary. Most of the benchmarks available for this task consist of a collection of text documents and the corresponding manually written summaries. In our experiments, we fine-tune BART-IT on three different Italian summarization datasets to evaluate its performance on the abstractive summarization task.  
* **FanPage**[30] is a dataset of Italian news articles from the online newspaper Fanpage.it ([[https://www.fanpage.it/](https://www.fanpage.it/)]([https://www.fanpage.it/](https://www.fanpage.it/)), latest access: 19 December 2022). It includes 84,365 news articles as well as their corresponding summaries. The dataset is split into a training set of 67,492 documents, a validation set of 8436 documents, and a test set of 8437 documents. The average length of the documents is approximately 312 words and the average length of the summaries is approximately 43 words.
* **IIPost**[30], similar to FanPage, is a dataset of Italian news articles from the online newspaper IIPost.it ([[https://www.ilpost.it/](https://www.ilpost.it/)]([https://www.ilpost.it/](https://www.ilpost.it/)), latest access: 19 December 2022). It contains a total of 44,001 article-summary pairs divided into a training set of 35,201 documents, a validation set of 4400 documents, and a test set of 4400 documents. The average length of the documents is shorter than FanPage, with an average length of approximately 174 words, while the average length of the summaries is approximately 26 words.
* **WITS**[31] is a dataset of Wikipedia articles and their corresponding summaries. The dataset is automatically generated by crawling the Italian Wikipedia ([[https://it.wikipedia.org/](https://it.wikipedia.org/)]([https://it.wikipedia.org/](https://it.wikipedia.org/)), latest access: 19 December 2022) and extracting the leading section of each Wikipedia article and using it as the summary. The dataset is the largest among the three, containing a total of 700,000 article-summary pairs. Analogously to the original authors, we randomly select 10,000 articles as the test set, 10,000 articles as the validation set, and the remaining articles are used for training. Given the different nature of the articles, the average length of the documents is 956.66 words, and the average length of the summaries is 70.93 words.  
Document type, documents' average length, and summaries' average length are rather diversified across the considered benchmarks, making them suitable for evaluating the performance of BART-IT under multiple aspects.  
We use the same fine-tuning procedure for all the datasets, using a maximum sequence length of 1024 tokens for the input documents (i.e., longer documents are truncated) and a maximum sequence length of 128 tokens for the summaries (i.e., longer summaries are truncated).