esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately. This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisions. Machine learning (ML) has recently emerged as a powerful artificial intelligence (AI) technique to get reliable predictions in particularly volatile and unforeseeable situations. This development makes ML models an attractive complement to other approaches that require more extensive human modeling effort and assumptions about market mechanisms. This study investigates the application of machine and deep learning approaches to predict day-ahead electricity prices for a 7-day horizon on the German spot market to give power plants enough time to ramp up or down. A qualitative and quantitative analysis is conducted, assessing model performance concerning the forecast horizon and their robustness depending on the selected hyperparameters. For evaluation purposes, three test scenarios with different characteristics are manually chosen. Various models are trained, optimized, and compared with each other using common performance metrics. This study shows that deep learning models outperform tree-based and statistical models despite or because of the volatile energy prices.  
electricity price forecasting; machine learning; deep learning; German spot market; short-term; time series +
Footnote †: journal: _Algorithms_  
0000-0001-999-2201-1111][PERSON]  
1 Eranees Analytics Germany GmbH, 20459 Hamburg, Germany; [EMAIL_ADDRESS] (D.E.B.); [EMAIL_ADDRESS] (D.M.); [EMAIL_ADDRESS] (J.A.W.)  
3 Software Systems Engineering, Institute of Computer Science, University of Hildesheim, 31141 Hildesheim, Germany; [EMAIL_ADDRESS]
# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
## 2 Related Work
With a similar concept in the searching mechanism, a series of bit strings can be used to represent the chromosomes and to accord to solutions in the genetic algorithm [76]. The nondominated sorting genetic algorithm II (NSGA-II) [77] is one of the multi-objective evolutionary algorithms (MOEA) that is suggested to overcome the chasm in computational complexity, non-elitism strategy, and parameter-sharing limitation. To achieve a better Pareto frontier, different levels of nondominated fronts are defined, where solutions categorized into various levels have different corresponding probability to be chosen in the next generation. Moreover, the crowding distance is applied to differentiate the solutions located in the same level and to enhance the diversity of solutions chosen into the next generation. By selecting solutions with higher crowding distance, the exploration can prevent the chromosomes from falling into the local optimal. With different selection strategies applied, the characteristics of the population in the next generation could be controlled in order to achieve better performance towards various research problems such as the multicriteria path-planning for robots [78], unmanned aerial vehicles [79], or geofence settlements [80], and gate assignment problems [81], as well as evaluating the design of transit networks [82]. Similar to some of the concepts in NSGA-II, strength Pareto evolutionary algorithm II (SPEA-II) [83] ranks the solutions according to the Pareto advantage by utilizing the fitness assignment, density estimation, and an enhanced archive truncation method. Accordingly, both NSGA-II and SPEA-II are often adopted for testing as well as solving multicriteria optimization problem. To conclude, although research focusing on various issues in multicriteria optimization differs from our targeted research problem and cannot be directly adopted due to the complexity in computational resources and the nonmonotonic nature of the data, several concepts in the proposed methods could still be adopted and modified. For instance, our proposed multisource prioritized selection strategy can be viewed as a variation of the concept from partial optimization in level-based nondominated front. The rest of this paper is organized as follows. First, Section 3 presents preliminary definitions with the adopted inference module. Section 4 then explores various strategies in route construction. Furthermore, Section 5 shows the evaluations for BiasSpan and compares it to other methods. Finally, Section 6 states the conclusion and outlines future works.
## 4 Results and Discussion
### APT Charges  
Atomic polar tensor (APT) charges at different sites of the L-dopa molecule are collected in Table 4. The APT charges are the derivatives of the dipole moment with respect to the coordinates of the atomic sites [18]. In the present case, the APT charges at different sites retain their respective polarities in going from the monomer to the dimer. However, the magnitudes of the APT charges were found to decrease at all the atomic sites, except at the sites C\({}_{3}\), H\({}_{13}\), H\({}_{15}\) and H\({}_{18}\). The APT charge at the most positive site C\({}_{19}\) was found to be +1.419 e in the monomer and +1.106 e in the dimer. However, the atoms O\({}_{21}\) and O\({}_{20}\) have the APT charges \(-\)1.019 and \(-\)1.021 e in the monomeric form and \(-\)0.938 and \(-\)0.737 e in the dimeric form. The magnitude of the APT charge at the site H\({}_{13}\) was found  
Figure 3: Optimized structure of the lowest-energy dimer (D\({}_{1}\)) of the lowest-energy monomer (C-I).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Bond**} & \multirow{2}{*}{**C-I**} & \multicolumn{2}{c}{**D1**} & \multirow{2}{*}{**Ref. [10]**} & \multirow{2}{*}{**Ref. [11]**} & \multirow{2}{*}{**(2)-(1)**} & \multirow{2}{*}{**(3)-(1)**} & \  
to be reduced in going from the monomeric to the dimeric forms due to inter-molecular H-bonding. The sites H\({}_{11}\) and H\({}_{24}\) have higher values of the APT charges as compared to the H\({}_{13}\) atom of the O\({}_{12}\)H\({}_{13}\) group due to intra-molecular H-bonding.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 7 Challenges and Prospects
### Prospects  
With deepening research, the intelligent monitoring method of tool wear will be continuously improved.  
(1) With the development of the era of Big Data, the deep convolutional neural network has a stronger ability of feature learning and feature expression. Improving the existing network structure or training the learning algorithm according to actual working conditions, appropriately increasing the number of layers of the network, using larger training data sets, and other methods can improve the adaptability of the network and enhance the generalization ability of the model [128, 129].  
(2) The convolutional neural network is appropriate for nonlinear mapping, especially for the monitoring condition tool during machining. Combined with the characteristics of deep neural network structures, the deep mining of key feature parameters can enhance the performance of the monitoring model. For example, in the literature [39], a deep convolution neural network is used to mine the hidden, high-dimensional features in processing features, which improve the monitoring accuracy and generalization performance.  
(3) With the wide application of the feature processing system, data acquisition and data understanding become more and more important. The proper integration of accurate sensor systems and feature processing methods is of paramount importance in the field. Additionally, with the continuous performance improvement in hardware (e.g., sensors), the information collected will be more accurate and comprehensive. By collecting multiple features at the same time to jointly improve the feature characterization rate, multi-feature fusion can be used as the direction to try to improve the accuracy of monitoring results [16].  
(4) Based on cutting-related database analysis, combined with the advantages of different monitoring methods, in-depth analysis of the relationship mechanism between the acquired information data and tool wear, we could obtain a tool wear monitoring model with a better robustness and higher accuracy [130].  
(5) Emerging technologies continue to emerge. Tool wear monitoring technology may jump out of its own field and work together with digital twin and other related technologies to promote each other [9, 131].  
Conceptualization, Q.W.; methodology, Q.W.; software, Q.W.; validation, Q.W.; formal analysis, Q.W. and H.W.; investigation, Q.W.; resources, Q.W. and H.W.; data curation, Q.W. and L.H.; writing--original draft preparation, Q.W.; writing--review and editing, Q.W. and S.Y.; visualization, Q.W.; supervision, H.W.; project administration, H.W.; funding acquisition, Q.W. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Probabilistic Studies on the Shear Strength of Slender Steel Fiber Reinforced Concrete Structures
## 3 Shear Formulations and Experimental Database for SFRC Beams without Shear Reinforcements
### Experimental Database SFRC Sender Beams  
A large database of 488 experimental studies on SFRC beams without stirrups was recently compiled by [21]. From the originally collected 488 experiments, a total of 162 beams with the shear-flexure mode of failure and shear-span to effective depth ratio \(a/d<2.5\) (non-slender beams) were filtered out, resulting in a subset of 326 experimental tests investigated in this study. The resulting database of 326 experiments was adopted for the analysis conducted in this study. Specimens in the evaluation database consist of slender beams with a flanged and rectangular cross-section.  
All the beam specimens in the database failed majorly by shear-compression and diagonal tension and had an \(a/d\) ratio greater or equal to 2.5. The experimental database covers a wide variety of shear configurations from small to large geometry and low to high reinforcement. The statistical properties of the main parameters of the evaluation database are presented in Table 2. The main parameters include the shear capacity \(V_{R}\), the \(a/d\) ratio, beam effective depth \(d\), beam width \(b_{w}\), longitudinal reinforcement \(\rho_{l}\) ratio, concrete strength \(f_{c}\), aggregate size \(d_{a}\), and the steel fiber factor \(F_{sf}\). The steel fiber factor is a function of the percentage volume \(V_{f}\), diameter \(d_{f}\) and length of fibers \(L_{f}\) (see Equation (7)).  
\[F_{sf}=\frac{V_{f}L_{f}}{d_{f}} \tag{7}\]  
The range of parameters of the database corresponds to what can be found in practical design situations. Although data for large beam sizes are few, the dataset is deemed representative of most real applications and of design situations covered by current design provisions.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 2 Methodology
### Driver Acceptance  
Both field-level studies followed the same approach and applied the 'Technology Acceptance Model' (TAM) [24] as a theoretical basis for the driver acceptance evaluation design (see Figure 2). In the meantime, several extensions of this TAM were developed [25]. For the paper, the focus is on the original one because the paper wants to evaluate \"perceived usefulness\", \"perceived ease of use\", and \"behavioural intention to use\"(see Section 1.6).  
The starting point of the model is \"External Variables (EV)\", which provide some descriptive information and influence the \"Perceived Usefulness (U)\" and the \"Perceived Ease of Use (E)\". \"Perceived Usefulness\" and \"Perceived Ease of Use\" both are the basis for \"Attitude Toward Using (A)\", and \"Perceived Ease of Use\" additionally has some influence on \"Perceived Usefulness\". The \"Attitude Toward Using\" influences the \"Behavioural Intention to Use (BI)\", which also has a direct link to the \"Perceived Usefulness\". Finally, the \"Behavioural Intention to Use\" is the core variable for the \"Actual System Use\" as the main target of all innovation ideas. This mirrors the acceptance of the user.  
The driver acceptance evaluation design includes two parts:  
* _Online questionnaire_: Evaluation of the driver acceptance with a questionnaire.
* _Test drives_: Evaluation of the driver acceptance with selected test drivers who experience the services and give feedback. Interviews with detailed questions for each use case in the different question categories were conducted before and after the test drives.  
Figure 1: Overview of the evaluation design of user acceptance.
## 5 Discussion
### Comparison with Other Models  
Traditional models like Logistic Regression and SVM achieved F1 scores of around 62%, highlighting their limitations in handling tweet data. Deep learning models like standard LSTM and CNN performed slightly better but still fell short compared to the DLCTC model. Their inability to fully capture the nuances of informal language and contextual relationships in short texts limited their effectiveness. However, the DLCTC model introduces greater complexity compared to LSTM and CNN, with more trainable parameters and a longer training time per epoch. This added complexity allows for a richer understanding of linguistic features, which is crucial for handling the informal and context-dependent nature of social media text.  
The DLCTC model's architecture, which combines multiple levels of embeddings with advanced neural network components, demonstrates the advantage of a more sophisticated approach. By integrating character, word, and concept embeddings, along with Probase for effective term disambiguation, DLCTC captures linguistic patterns at various levels. Combined with the attention mechanism to focus on the most informative features, this approach enhances classification accuracy and robustness.
## 3 Experimental Results
### Analysis of Precise Time Alignment Using DTW
Similarly,  
Figure 11: Acceleration time history recorded from 2 structures: (**a**) the preprocessed acceleration time history of the full side panel (Structure 1). (**b**) the preprocessed acceleration time history of the partition side panel (Structure 2). Figure 13b effectively demonstrates the resampled acceleration data and its corresponding magnitude spectrum for Structure 2, further illustrating the benefits of the synchronization process. Figure 12: Segmentation of response data for analysis: **(a)** Time interval selection for Structure 1, from 25 s to 65 s. **(b)** Time interval selection for Structure 2, from 35 s to 75 s. The labels S01, S02, and S03 represent the sensor positions on the 3 DOF structure, corresponding to the first, second, and third storeys, respectively. Figure 13: Synchronized acceleration time history using DTW and magnitude spectra: **(a)** Adaptive, resampled acceleration data and magnitude spectrum for Structure 1, highlighting the effectiveness of the alignment. **(b)** Adaptive resampled acceleration data and magnitude spectrum for Structure 2, further illustrating the benefits of the synchronization process. Figure 13a,b further illustrate the benefits of the synchronization process, highlighting the improved coherence and distinguishability of the synchronized signals. In particular, the magnitude spectra reveal distinct characteristics for each structure. Structure 1 exhibits its first peak around 1.50 Hz, while Structure 2 shows its first peak at approximately 1.25 Hz. This difference in dominant frequencies further emphasizes the contrasting nature of the two structures. In addition to the magnitude spectrum analysis of Structure 1, illustrated in Figure 13a, two distinct peaks were observed in the frequency spectrum, indicating the presence of the first two fundamental frequencies of the structure. These frequencies were identified as 1.50 Hz and 3.15 Hz, representing the primary modes of vibration for Structure 1. On the other hand, Figure 13b showcases the magnitude spectrum analysis of Structure 2, where multiple peaks at higher modes are observed. Specifically, a prominent peak can be identified at around 8.40-8.90 Hz, indicating the presence of a higher mode of vibration within Structure 2. Due to the lower stiffness, Structure 2 exhibits more pronounced deformations and larger displacements when subjected to dynamic loading. These larger displacements enable the higher-order modes to contribute significantly to the overall structural response.
## 5 Analysis of Test Results
### Influence of Temperature  
Temperature has a strong influence on the adhesion oil of emulsified asphalt, and then affects the interlayer characteristics. In this section, adhesion coefficient \(K_{\text{max}}\) is selected as an evaluation index of interlayer bonding capacity. Experimental data are shown in Figures 9 and 10.  
It can be seen from Figures 9 and 10 that the value of adhesion coefficient \(K\) gradually decreases with the increase in temperature, indicating that the higher the temperature, the weaker the interlayer bonding ability and the more vulnerable it is to damage.  
We know that there are three stages in the variation of the adhesion coefficient with the change in temperature. When the temperature rises from \(-\)10 to 0 \({}^{\circ}\)C, the average adhesion coefficients of oil A and oil B bond specimens decrease by 27.3% and 23.5%, respectively. When the temperature rises from 0 to 25 \({}^{\circ}\)C, the average adhesion coefficients of oil A and oil B bond specimens decrease by 65.7% and 72.0%, respectively. However, when the temperature rises from 25 to 50 \({}^{\circ}\)C, the average adhesion coefficients of oil A and oil B bond specimens are almost the same. It can be seen that the type of the viscosity-level oil has a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Variable** & **Experiment with the Selected Variables** \\ \hline Upper layer & AC-13 \\ Following layer & AC-20 \\ Viscosity-level & Viscosity-level oil A and B \\ Experimental temperature & \(-\)10, 0, 25, 50 \({}^{\circ}\)C \\ Amount of viscosity-level oil spread & 0, 0.5, 1.0, 1.5 kg/m\({}^{2}\) \\ Vertical compression stress & 0.7 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental parameters of interlayer interface model.  
Figure 9: Influence of temperature on the bonding coefficient of oil A bond specimen.  
significant influence on interlayer bonding performance at room temperature and below. When the temperature is high, this difference is small.
## 2 Materials and Methods
### Model Evaluation  
Most of the tasks involved in this paper belong to the Question and Answer (Q&A) task, which is one of the important tasks in the field of natural language processing. The Q&A task aims to predict the correct answer by precisely understanding the question, requiring the model to have a series of comprehensive capabilities, such as text understand  
Figure 2: Knowledge extraction process.  
ing, information extraction, text generation, etc. The Q&A task is mainly evaluated by measuring the consistency between the answers given by the model and the real answers.  
During the experimental process of the K-Extract method, manual answers are firstly obtained for the literature abstract data. There are two kinds of manual answers: with answer (the answer is the correct answer, i.e., a sentence or a word in the original text) and without answer (the answer is none). The actual answers from the large language model are of three types: with answer (the answer is the correct answer or an incorrect answer) and without answer (the answer is none). Therefore, in this study, we have the following definitions:  
* True Positive (TP): Samples where the manual answer is the correct answer and the actual answer is also the correct answer;
* True Negative (TN): Samples where the manual answer is none and the actual answer is also none;
* False Positive (FP): Samples where the manual answer is none but there is an actual answer;
* False Negative (FN): Samples where the manual answer is the correct answer but the actual answer is none or an incorrect answer.  
In this paper, we mainly use performance indicators such as accuracy and precision to evaluate the quality of the answer results of the five large language models. The calculation formulas for each performance indicator are as follows:  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FP+FN)}\times 100\% \tag{1}\]  
\[Precision=\frac{TP}{(TP+FP)}\times 100\% \tag{2}\]  
These indicators will be used to comprehensively evaluate the performance of large language models in Q&A tasks.
## 3 Methods  
### Signal Model  
A typical communication system is made up of three parts: a transmitter, a channel, and a receiver. To transmit wireless signals over long distances in the air, the transmitter must modulate the low-frequency baseband signal to the high-frequency carrier signal in a specific way. The receiver uses the opposite modulation process after receiving the modulated signal to separate the baseband signal from the modulated signal. The identification of the signal modulation type is a critical step in signal demodulation. Signals received by the receiver are typically modulated in-phase and quadrature (IQ). In quadrature are two signals whose phases are 90 degrees apart. In mathematical terms, the samples obtained after sampling the signals can be expressed as L-dimensional arrays with two channels, denoted as follows:  
\[x=\begin{bmatrix}I\\ Q\end{bmatrix} \tag{1}\]  
where \(I=[i_{1},i_{2},\ldots,i_{L}]\), \(Q=[q_{1},q_{2},\ldots,q_{L}]\). In addition to representing the signal as IQs, amplitudes and phases (APs) can also be used to represent the signals.  
\[x=\begin{bmatrix}A\\ \phi\end{bmatrix} \tag{2}\]  
where \(A=\sqrt{I^{2}+Q^{2}}\), \(\phi=\arctan 2(Q/I)\). A single sample in a deep learning-based AMR task is typically represented as \((x,y)\), where \(y\) denotes the label of sample \(x\).
## 1 Introduction
Therefore, this approach might be less accurate for near-field noise predictions, or for noise from rotors with transonic helical tip speeds where turbulence effects may be notable. Nevertheless, FODNOPP is effective in directly evaluating open rotor noise and in conducting the geometry design of quieter rotors. Utilizing four analytical formulations by [PERSON], FODNOPP efficiently calculates the discrete frequency noise radiated by open rotors, ensuring high computational efficiency and broad selectivity. FODNOPP generates comprehensive data, including the acoustic pressure time histories and the spectra for thickness noise, load noise, and overall noise as a composite of the former two. The program also provides the A-weighted sound pressure level \(L_{\text{A}}\) in dBA, the effective perceived noise level \(EPNL\) in EPNLB, and other relevant acoustic engineering indices. When compared to the resource-intensive and time-consuming Computational AeroAcoustics (CAA) method, FODNOPP demonstrates certain advantages in computing the open-rotor noise. The four examples demonstrate that FODNOPP accurately computes the linear source discrete noise of diverse open rotors, including single propellers, propfans, counter-rotating propfans, rotorcraft rotors, and wind turbines, under a range of operating conditions.