esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Performance Estimation
### SKR Analysis under Relatively low PER  
Based on the optimal parameters from the above sections, we investigate the SKR of different transmission distances, especially 25 km and 50 km under relatively low PER conditions. The repetition frequency is set to 292.5 MHz, and the modulation variance V\({}_{\text{A}}\) is set to 1.6. The results are shown in Figure 8. As can be seen from Figure 8, different PERs have different transmission distance limitations. When the transmission distance is 25 km, the required PER should be above 17 dB, which is a relatively low scale for the polarization division multiplexing (PDM) system in the actual experiment. The results show that under the proposed orthogonal dual-polarization scheme in Section 2, higher than 1.02 Mbps SKR can still be achieved even if the PER deteriorates to 17 dB. When the transmission distance is 50 km, as the yellow and green lines show, the required PER should be above 23 dB, which is a relatively low scale for the middle-length transmission distance PDM CV-QKD system. The results show that higher than 0.331 Mbps SKR can still be achieved even if the PER deteriorates to 24 dB. Thus, the proposed scheme has an excellent PER tolerance performance for the dual-polarization LLO CV-QKD system.
# Edge Currents Induced by AC Electric Field in Two-Dimensional Dirac Structures
## 3 Results and Discussion
The parameters used for calculations are given in the caption of Figure 3 and correspond to high-quality bilayer graphene [6]. We use the same electron density \(n=5\times 10^{11}\) cm\({}^{-2}\) as in the case of single-layer graphene. The curves are calculated after Equation (22) for linearly polarized radiation with the electric field directed at the angle \(\pi/4\) with respect to the edge (\(S_{2}/E^{2}=1\), \(S_{3}=0\)) and for circularly polarized radiation (\(S_{3}/E^{2}=1\), \(S_{2}=0\)). For 2D systems with the parabolic spectrum and short-range scatterers, both the mass \(m\) and relaxation times \(\tau_{1}=\tau_{2}\) are independent of energy and Equation (21) yields \(J_{y}=-e\text{Re}\sigma(\omega)\tau_{1}^{2}(E_{F})/m^{*}\) for linearly polarized radiation and \(J_{y}=-e\text{Re}\sigma(\omega)\tau_{1}(E_{F})/\omega m^{*}\) for circularly polarized radiation. For Coulomb scatterers, \(\tau_{1}=2\tau_{2}\propto\varepsilon\), and the frequency dependence of \(J_{y}\) is more complicated. Note that both the direction and magnitude of the current are determined to a great extent by the scattering mechanism. The calculated current magnitude for bilayer graphene is of the order of several nA for the radiation intensity 1 W/cm\({}^{2}\) and is slightly smaller than that for monolayer graphene at the same electron density due to the larger effective mass in bilayer graphene. Experimentally, edge photocurrents are detected as electric currents in short circuits or as voltage drops between contacts in open circuits [5; 6; 9; 10]. If the sample is small and fully illuminated, the photocurrents are generated along all the edges and the resulting distribution of the photoinduced electric potential in the sample is determined by the edge photocurrents and the compensating drift currents in the sample. For linearly polarized radiation, the photocurrents generated along different edges are generally not equal because of different orientations of the edges with respect to the electric field polarization. For circularly polarized radiation, the edge photocurrents form a vortex whose winding direction depends on the radiation helicity. The photocurrents circulating around the sample produce, in turn, a magnetic field, which can be seen as a manifestation of the inverse Faraday effect. Figure 2: Frequency dependence of the edge current in 2D systems with a linear dispersion of carriers. Solid curves correspond to short-range scatterers and linearly polarized field with \(S_{2}=E^{2}\) (green curve) and circularly polarized field with \(S_{3}=E^{2}\) (red curve). Dashed curves present the results for scattering by charged impurities. The curves are calculated after Equation (23) for the parameters corresponding to single-layer graphene: \(v_{0}=10^{8}\) cm/s, \(n=5\times 10^{11}\) cm\({}^{-2}\) (the Fermi energy \(E_{F}\approx 80\) meV and the effective mass at the Fermi level \(m\approx 0.013\)\(m_{0}\)), \(\tau_{1}(E_{F})=1\) ps, and \(E=8\) V/cm corresponding to the radiation intensity \(I=1\) W/cm\({}^{2}\).
## 2 Materials and Methods
### Study Selection  
A detailed and systematic review of the literature was performed to identify publications that assessed the application of AI in PCa, using PubMed/MEDLINE, Google Scholar, IEEE Explore, and DBLP databases, which is a computer science database, for scientific journals from January 2009 to November 2019. We used the search terms prostatic neoplasm combined with one of the following terms: Artificial Intelligence, Artificial Neural Networks, Machine Learning, Natural-Language Processing, and computer vision.  
Machine Learning is based on a mathematical and statistical approach to give computers the ability to \"learn\" from data and to identify intricate patterns. There are usually two phases in the process of ML: the first is called the \"learning\" phase and the second one the \"verification\" phase. The learning phase is performed to create a model to solve the requested task. According to the type of information that is provided during the learning phase, ML algorithms can be classified into \"supervised\", \"unsupervised\", and \"reinforcement\" learning. Random Forests (RF), Support Vector Machine (SVM) [6], or Naive Bayes classification are examples of supervised ML models [7; 8].  
Artificial Neural Networks (ANNs) are one of the most important supervised ML approaches. ANNs is a system inspired schematically on the functioning of biological neurons [9]. As a biologic neural network, ANNs also contain layers of simple points of information, called nodes, which form an interconnected processing network. Commonly, to have a functional ANNs, a training, validation, and verification steps are necessary with non-overlapping sets of data. The most used ANN in medicine research is the multilayer perceptron, with 3 neuronal layers [9]. Between the first and the last layer, there is an interconnecting layer (hidden nodes), forward feeding neurons (Figure 1).  
There is a subtype of ANN that showed its effectiveness for segmenting, classifying, and recognizing complex patterns (e.g., animals, objects, cells) on digitized images that is called Convolutional Neural Network (CNN) [10]. Identification of the prostate on MRI images [11] or classification of skin cancer from digital images are examples of CNNs abilities [12]. Over the past decade, a growing amount of research in the field of AI has been applied to prostate cancer (PCa). AI has been proposed to help clinicians in the management of PCa and it may be difficult to have a global view of the actual situation [13; 14; 15].
# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 1 Introduction
Lastly, to further improve the accuracy of predicting non-active states of the machines, we utilize MSTV to extract time and frequency domain features. * After statistical features have been extracted, learning models for predicting non-active states of the machines are constructed by combining the raw data from the sensor device and extracted features. Specifically, we construct predictive models using state-of-the-art machine learning methods, namely decision tree, kNN, random forests, and linear support vector machines (SVM). * We evaluated the performance of the proposed method through extensive experiments. In experiments, we measured the accuracy and error rate of state-of-the-art machine learning methods in classifying the active and non-active states of the machine with 17 features and 86,400 observations. The real-world dataset was obtained by automobile part manufacturer. The experiment results demonstrate that linear SVM achieves a 98% accuracy compared with the other models, which can be considered as a promising result in performing predictive maintenance of the machines. The rest of the paper proceeds as follows. Section 2 discusses studies related to the detection and prediction of unstable operations in machines. Section 3 describes the proposed method. Section 4 presents the performance evaluation. Section 5 concludes the paper and discusses future work.
## 2 Materials and Methods
### System Architecture
#### 2.2.3 SCADA Functional Architecture  
The functional architecture of a control system is designed to be modular, and consists of three functional levels: the control level, the supervision level, and the execution/signal level. The control level provides the following functions: communication with the railway energy dispatcher, initiation of commands, validation of commands, ensuring safety conditions, interpretation and display of control information from the remote terminal unit (RTU).  
The supervision level provides the following functions: verification and validation of transmitted commands, monitoring of command execution, validation of control information received from the execution level, and monitoring of system operation. The execution/signal level provides the following functions: control of terminal equipment, processing of status signals collected from the field into operable digital signals by the computer system, and transmission of information to the dispatch point.  
In Figure 11, the functional architecture of the control system for power supply installations in a railway station is described. This functional architecture provides a framework for the development and implementation of control systems that are safe, efficient, and reliable. By separating the functions of control, supervision, and execution/signal processing into distinct functional levels, the system can be designed, tested, and maintained more easily, with clear interfaces and well-defined responsibilities for each level.  
Figure 11: The functional architecture of the control system for power supply installations in a railway station. CSF—control safety filter; SSF—signal safety filter; ExC—execution of commands; ST—signaling transmission; SMP/PSP—source management block/power supply panels.
# Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages
## 4 Results  
### Participant Demographics  
The survey included 56 valid respondents, consisting of 85.7% males and 14.3% females. Regarding the age distribution, the majority of participants were in the age group of 41-50 (39.3%), followed by 31-40 (32.1%) and 20-30 (19.6%), with the remainder above 50. In terms of education, most respondents held a bachelor's degree (58.9%), while 35.7% had obtained a master's degree or higher. The fields of professional study were diverse, with the largest representation from Electrical Engineering (45.5%), followed by Mechanical Engineering (18.3%), and Computer Science (15.2%). Additional fields included Industrial Engineering, Aeronautics, and Economics.  
Participants reported an average of 10.2 years of experience in development, projects, or systems engineering (SD = 6.8), with their tenure in their current roles averaging 4.3 years (SD = 3.5). The roles were distributed across various domains, with significant representation from Systems Engineering (27%), Project Management (21%), and Integration Engineering (17%).  
Organizational affiliation was heavily skewed toward security organizations (71.4%), with a smaller proportion associated with civilian and governmental institutions. In terms of the size of the organizational domains, 42% of respondents reported being part of domains with more than 100 employees, indicating the scale of their operational environments.  
This demographic profile highlights a respondent pool with substantial experience, advanced education, and representation from diverse engineering fields, reflecting the breadth and depth of expertise relevant to the study's objectives.
# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 3 Result and Discussion
### Detection Results of Scaled Networks
[3], the lightweight network proposed in this paper has a great advantage in detection speed, except for MobilenetV2-Yolov4-1.3, and the fastest detection speed achieved in this paper is approximately 1.7 times faster than the fastest detection speed achieved by Efficientnet-Yolov3, reaching 70 FPS. However, due to the compression and adjustment of channel number, the improved algorithm proposed in this paper is relatively deficient in image information feature extraction ability, and does not achieve a higher detection accuracy as mentioned in Ref. [3]. Figure 7: The effect of adjusting the model scaling factor on the test results: (**a**) the performance comparison between different models, (**b**) comparison of model parameters. The parameters of the target detection model under various zoom ratios are compared in Figure 7b. By comparing the increasing trend of the parameter amount of various improved algorithms under different ratios, it can be confirmed that the size of the parameter amount is proportional to \(\alpha^{2}\). Also can be clearly seen that the combination of Mobilenet and Yolov4 could reduce the amount of parameters of Yolov4 effectively, then combined with the detection speed, it can be proved that reducing the amount of parameters by compressing the channels of networks is an effective measure to improve the target detection speed, but not conducive to ensuring the detection accuracy.
# Incorporating Synonym for Lexical Sememe Prediction: An Attention-Based Model
## 5 Discussion  
### The Two Ways of Combining Synonyms and Word Embedding Vectors  
Two score functions are introduced in Section 3.2 for combining knowledge from synonyms and word embedding vector. One is the static SPSW, as shown in Equation (2), and the other is attention-based ASPSW, as shown in Equation (3). The former score function combines the knowledge between synonyms and from pre-trained word vector by the hyperparameters, \(\alpha\), and the later score  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MAP** \\ \hline SPWE [14] & 0.5610 \\ SPSE [14] & 0.3916 \\ SPASE [14] & 0.3538 \\ SPWE+SPSE [14] & 0.5690 \\ SPWE+SPASE [14] & 0.5684 \\ \hline SPCSE [15] & 0.3105 \\ SPWCF [15] & 0.4529 \\ SPWCF+SPCSE [15] & 0.4849 \\ CSP [15] & 0.6408 \\ \hline LD-seq2 seq [23] & 0.3765 \\ \hline SPS & 0.5818 \\ SPSW & 0.6578 \\ ASPSW & **0.6774** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction accuracy: Mean Average Precision (MAP); the best result is in bold-faced.  
function dynamically balances two kinds of knowledge using an attention strategy. To examine the performance of two models, we performed experiments with a different value on static SPSW, and listed the results in Table 3.  
As shown in Table 3, the values of \(\alpha\) have made a significant effect on the prediction accuracy. When it was set to 0.7, the model SPSW achieved the best results, and the ASPSW obtained the second-best results. Despite an appropriately selected \(\alpha\) value, static SPSW achieves better results, the best and the second results are a little different. Considering the robustness of methods, we argue that ASPSW is a more promising model for sememe prediction.  
To observe the difference caused by models, we performed experiments on random-selected 100 words with three typical models (score function), SPWE, SPS, and ASPSW. The scores of the three models are recorded and plotted in Figure 3. The figure shows that some of the scores of the SPS model are close to 0, which may be because the knowledge in the synonym dictionary is incomplete. For a new word, SPS can rarely find a valid synonym for inferring sememes. In most cases, the prediction score of the ASPSW model is higher than that of the SPWE model and the SPS model, indicating that the dynamical weights in the joint model can make full use of different knowledge and avoid false predictions due to incompleteness or inaccuracy in a single type of knowledge.  
Figure 3: Randomly select 100 words and test their sememes prediction score (MAP value) of the SPWE, SPS, and ASPSW models.  
\begin{table}
\begin{tabular}{c c} \hline \(\alpha\) & **MAP** \\ \hline
0.1 & 0.5820 \\
0.2 & 0.6023 \\
0.3 & 0.6222 \\
0.4 & 0.6416 \\
0.5 & 0.6578 \\
0.6 & 0.6718 \\
0.7 & **0.6787** \\
0.8 & 0.6764 \\
0.9 & 0.6674 \\ \hline ASPSW & 0.6774 \\ \hline \end{tabular}
\end{table}
Table 3: The prediction accuracy of different \(\alpha\); the best result is in bold-face.
## 2 Materials and Methods
### FTIR Analysis  
Spectral measurements were performed using an FTIR Jasco 6300 spectrometer (Madison, WI, USA) equipped with a Pike Technologies ATR (Attenuated Total Reflection) diamond crystal accessory. Spectral data for peach pomace and experimental biscuits were recorded at a resolution of 4 cm\({}^{-1}\) in the range 4000-400 cm\({}^{-1}\) and with 100 scans. Data processing was performed with JASCO Spectra Manager II software. Background reference spectra were recorded using air after every sample to minimize interference from carbon dioxide and water vapor in the atmosphere. After each measurement, the ATR crystal was carefully cleaned with pure acetone (Sigma-Aldrich Co., Darmstadt, Germany) and dried with a soft cloth [23; 24]. All measurements were taken at room temperature (22 \(\pm\) 2 \({}^{\circ}\)C). To ensure spectral reproducibility and assess analytical precision, three replicate spectra were recorded for each sample, and the average spectrum was obtained.
# Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems
## 3 System Architecture, Scenarios of Multi-Hop LoRa, and Results
### Flat Topology Calculations  
#### 3.1.1 Measurement Ranges and Traffic Size Calculations  
We calculate the measurement range and the density along 10 km of linear pipeline. Table 4 shows the three scenarios of the different multi-hop routing, where the system may deploy one of the available packet sizes: 1 byte and 255 bytes. Various distances between sensors from 10 m to 500 m are analyzed. For each distance, the number of hops is calculated for one packet. The network density and the number of hops give a good indication of the different possible traffic sizes. For the density of the sensor's calculation, we must consider the measurement types, since they have different requirements in terms of measurement ranges and consequently network densities. Based on the inner distances between the devices, we spread the devices along the 10 km of the pipeline and calculate the number of devices required for each distance [10 m, 50 m,..., 500 m]. The number of hops was obtained for the three communication scenarios. For example, at the distance of 200 m (the inner distance between the devices), we need 50 sensors to cover all the 10 km of the pipeline. If we are using the second communication scenario, fewer sensors cooperate, so there are 17 hops to transmit data to the gateway. When we deploy more sensors with less distance between them, it increases the number of packets generated, which leads to more energy being consumed. The amount of energy consumption also depends on the sensor's role within the communication chain: if it only transmits its data or if it is a cluster head.  
Figure 2: Hierarchical multi-hop communication scenarios.  
Results show an increase in the traffic that will be generated as the inner-sensor distance decreases. Scenario 3 has a lower number of bytes than other scenarios. To design a sensor network with low requirements in terms of the number of generated bytes, we can decrease the network density (use a large inner distance ex. 200 m) and send it through a smaller number of hops (scenario 3 has minimum hops).
## 2 Literature Review
### Embedding-Based Topic Extraction for Patent Analysis  
As a paradigm shift in intelligent mobile communication, 6G addresses more complex and demanding challenges, such as achieving truly global coverage, supporting ultra-high data rates, and enabling ultra-low latency communications [1; 11]. Given their increasing significance and the rapid pace of technological innovation in this area, understanding the evolving technology landscape in this sector becomes essential. Patents serve as crucial references for analyzing the core technologies in this field. However, the patent text is characterized by specific, technical, legal, and, therefore, ambiguous jargon [24]. Moreover, patent texts contain novel technologies. Therefore, the traditional keyword-based approaches [25] using co-occurrence approaches, such as pointwise mutual information (PMI) or n-grams [26], often fail to adequately consider the context when defining the semantics of words and suffer from the computation expense because of the large and extremely sparse embedding matrices.  
In recent years, neural network-based embedding approaches have emerged to map words or sentences into fixed-length numeric vectors. Thereby, latent semantics in massive textual data can be translated into low-dimensional dense space [27]. Word2 vec and doc2 vec are representative embedding techniques using neural networks based on static embedding that does not change with context once learned [28; 29]. Top2 vec [30], a variant of Word2 Vec, capitalizes on the joint embedding of document and word semantics to find more representative and informative topics than topic models such as TF-IDF or LDA. More recently, the BERT model has utilized the advantages of LLMs and emerged as a focal point for researchers engaged in topic extraction [31]. This approach relies on dynamic embedding techniques, learning novel word vectors from the corpus by considering the contextual information. In the field of patent text mining, PatentBERT [32] and PatentSBERTa [33] focus primarily on patent text analysis and have already been shown to be very effective in these tasks. Compared to static embedding, dynamic embedding might better capture the meaning of words in a context environment; however, it may require more training time and cost.  
Even though embedding-based approaches integrated with clustering techniques have been employed for topic extraction and analysis [21], there is still no consensus on evaluating these embedding methods for identifying the evolutionary states of topics in patent text analysis. Additionally, applying embedding techniques to the noise patent data without proper pre-processing may fail clustering results in the topic modeling process. In this work, we performed data pre-processing on redundant patent data using LLMs and conducted a comprehensive comparative analysis to show the performance of various embedding-based approaches to reveal the effectiveness of dynamic topic modeling for exploring technological evolution tasks.