esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design
### Heading Control  
The heading controller adopts the PID control algorithm. Using the first-order Nomoto model, the transfer function from the angle of rotation \(\delta\) to the yaw angle \(\psi\) is as follows:  
\[\frac{\psi}{\delta}(s)=\frac{K}{s(Ts+1)} \tag{39}\]where \(K\) and \(T\) are both model parameters. The commanded azimuth angle \(\delta_{\rm c}\) adopts PID control, and its formula is as follows:  
\[\delta_{\rm c}=(K_{\rm p\psi}+K_{\rm d\psi}s+K_{\rm i\psi}\frac{1}{s})(\psi_{\rm d }-\psi) \tag{40}\]  
According to the pole placement method, the parameters of the PID controller can be obtained as follows:  
\[K_{\rm p\psi}=\frac{T}{K}\omega_{\rm n\psi}^{2} \tag{41}\]  
\[K_{\rm d\psi}=\frac{T}{K}(2\zeta_{\rm f\psi}c_{\rm n\psi}-\frac{1}{T}) \tag{42}\]  
\[K_{\rm i\psi}=\frac{\omega_{\rm n\psi}^{3}T}{10K} \tag{43}\]  
The yaw angle reference model adopts a third-order element, and it can be expressed as follows:  
\[\frac{\psi_{\rm d}}{\psi_{\rm c}}=\frac{\omega_{\rm n\psi}^{3}}{(s+\omega_{ \rm nrr\psi})(s^{2}+2\zeta_{\rm r\psi}\omega_{\rm nrr\psi}s+\omega_{\rm nrr\psi }^{2})} \tag{44}\]  
where \(\psi_{\rm d}\) is the reference yaw angle, \(\zeta_{\rm r\psi}\) is the damping ratio, and \(\omega_{\rm nrr\psi}\) is the natural frequency.
## 3 Proposed Methodology
### Data Preprocessing and Transformer-Based Key-Phrase Generation  
This approach relies on natural language processing (NLP) with transformer models, eliminating the need for text-altering preprocessing steps before applying the models [46]. Nonetheless, the exclusion of not exploitable reviews is imperative. A comprehensive algorithm for eliminating inaccurate comments from the corpus is provided in the form of pseudocode, outlined in Algorithm 1. Sequencing these steps is vital, with language detection, a time-intensive process, strategically positioned at the algorithm's end for each row. This language detection serves a dual purpose: it enhances the performance of transformer models by filtering out non-English reviews and, if a significant proportion of reviews are in other languages, facilitates their translation into English. It is well-known that transformer models consistently yield superior results when applied to English-language content [47]. Any review containing fewer than three words consisting exclusively of alphabetical characters is deemed not exploitable, as such short texts are unlikely to have meaningful information. The identification of these reviews involves employing a regular expression. Additionally, a preprocessing procedure entails eliminating leading and trailing whitespace characters from reviews, as shown in line two of Algorithm 1.  
Given that transformer models rely on a predetermined vocabulary from the training data for embedding generation, handling Emojis poses challenges. Emojis are removed using a regular expression to ensure compatibility across multiple models within our proposed methodology. This approach facilitates the application of various models without hindrance.  
Figure 1: Overview of proposed methodology.  
For keyword generation, a transformer is fed with complete reviews. Since reviews typically employ simple language and short sentences, the transformer with the highest semantic search score should be utilized. A practical approach involves using the keyBERT Python library and a sentence-transformer model surpassing the performance of RAKE, SG Rank, and TextRank [48]. keyBERT extracts keywords and phrases from sentences, with key phrases providing a more comprehensive description of natural language texts than single keywords [48]. The process follows the recommendation of [49] to prepare keywords and key phrases for clustering using a pre-trained Tok2 Vec model, as it captures the word similarity required to generate clusters containing keywords belonging to the same topic. To maintain consistent vector sizes for keywords and key phrases, the vectors of all words within key phrases are averaged [49], enabling the calculation of their similarity to keywords. Since no duplicates of crucial phrases and keywords are necessary for clustering them, only distinct key phrases and keywords are used. Also, the performance is increased when less data are used for tokenization and clustering.  
The reviews are split into single sentences using the NLTK library to enable the transformer to conduct the sentiment analysis and classification.
# The Impact of Stimulation Intensity on Spatial Discrimination with Multi-Pad Finger Electrode
## 3 Results
### Stimulation Range  
The estimated probability density functions for LT, SMT and DT expressed as a percent of ST are shown in Figure 3a. The confidence intervals of the density estimation curves (95% of the data) are shaded with appropriate color. The confidence interval for LT spanned from 123% ST to 176% ST, while for SMT it ranged from 166% ST to 314% ST. The DT was characterized with the widest confidence interval (from 185% ST to 526% ST) with no overlap to that of the LT. Figure 3b shows the distribution of the threshold values using boxplots. When expressed as % of ST, the interquartile range of LT does not overlap with that of ST and DT, while the latter two barely overlap (0.3%). Based on the results of the threshold distribution, eight equidistant levels in the range from 110% ST to 250% ST were selected for subsequent spatial discrimination test. This range spans the confidence interval of LT but does not cross the median value of DT.  
Figure 2: (**a**) Sensation (ST), localization (LT), smearing (SMT) and discomfort (DT) thresholds on distal (pink) and proximal (brown) phalanx, averaged across the six pads of the multi-pad electrode; (**b**) Main effect of the phalange—current amplitudes for distal (pink) and proximal (brown) phalanx averaged across thresholds; (**c**) Main effect of the threshold—ST, LT, SMT and DT averaged across phalanx. The red line, box, whiskers and red mark indicate the median, interquartile range upper/lower boundaries and outlier, respectively. The statistically significant differences were present between all four thresholds (***, \(p<0.001\)).
###### Abstract  
Selecting appropriate research methodologies in project management traditionally relies on individual expertise and intuition, leading to variability in study design and reproducibility challenges. To address this gap, we introduce a machine learning-driven recommendation system that objectively matches project management use cases to suitable research methods. Leveraging a curated dataset of 156 instances extracted from over 100 peer-reviewed articles, each example is annotated by one of five application domains (cost estimation, performance analysis, risk assessment, prediction, comparison) and one of seven methodology classes (e.g., regression analysis, time-series analysis, case study). We transformed textual descriptions into TF-IDF features and one-hot-encoded contextual domains, then trained and rigorously tuned three classifiers--random forest, support vector machine, and K-nearest neighbours--using stratified five-fold cross-validation. The random forest model achieved superior performance (93.8% \(\pm\) 1.9% accuracy, macro-F1 = 0.93, ROC-AUC = 0.94), demonstrating robust generalisability across diverse scenarios, while SVM offered the highest precision on dominant classes. Our framework establishes a transparent, reproducible workflow--from literature extraction and annotation to model evaluation--and promises to standardise methodology selection, enhancing consistency and rigour in project management research design.  
machine learning; project management; research methodology; data-driven framework; predictive analytics Article
## 3 Application of the Process Method
### Machine Learning-Based Polynomial Regression
The proposed polynomial regression model \(y\) has a normal distribution withmean and can be determined knowing the number of input variables \(s\), and the regression model parameters \(\beta\), as follows:  
\[\sigma(y)=\sqrt{\frac{\sum\left(y-y_{fit}\right)^{2}}{m-2}} \tag{9}\]  
\[y=\beta_{0}+\beta_{1}z_{1}+\ldots+\beta_{s}z_{s}+\sigma(y) \tag{10}\]  
The hyperparameters \(\beta\) of the model represent the mean increase in \(y\) per unit increase in \(z\), i.e., \(\beta_{i}\) characterizes the influence of \(z\), adjusted for the range of input variables \(Z\). The estimation method represents a classical least squares estimation function, in order to achieve the best performance of the fitted regression models \(y_{fit}\). Suppose the fitted parameters of the regression model \(b=\{b_{0},b_{1},\ldots,b_{\rm{s}}\}\) are the estimates of the actual parameters of the regression model \(\beta=\{\beta_{0},\beta_{2},\ldots,\beta_{\rm{s}}\}\); then, the fitted regression model is expressed as follows:  
\[y_{fit}=b_{0}+b_{1}z_{1}+\ldots+b_{s}z_{s} \tag{11}\]  
The fitted parameters of the regression model \(b=\{b_{0},b_{1},\ldots,b_{\rm{s}}\}\) are determined such that the observed data points \(y_{i}=\{y_{1},y_{2},\ldots,y_{n}\}\) and the corresponding predicted values \(y_{fit,i}=\{\hat{y}_{1},\hat{y}_{2},\ldots,\hat{y}_{n}\}\) deviate minimally, by considering the sum of the squared residuals \(\sum\left|\left|y_{i}-y_{fit,i}\right|\right|^{2}=min\). Given the least squares estimation function, the minimum error \(ss_{\rm{res}}\) is expressed as follows:  
\[ss_{\rm{res}}=\sum_{i=1}^{n}(y_{i}-b_{0}-b_{1}z_{1}-b_{2}z_{2}-\ldots-b_{i}z_{s _{i}})^{2} \tag{12}\]  
The coefficients \(\hat{b}\)[45] estimated by the regression process are expressed as follows:  
\[\tilde{b}=\left(Z^{\rm{T}}Z\right)^{-1}\!\!Z^{\rm{T}}Y \tag{13}\]  
The process of building regression models is enhanced in combination with the RSM method to determine the most appropriate degree of polynomial function for each phase of the sheet metal bending cycle, i.e., bending, leveling, and movement.
# Electroglottograph-Based Speech Emotion Recognition via Cross-Modal Distillation
###### Abstract  
Speech emotion recognition (SER) is an important component of emotion computation and signal processing. Recently, many works have applied abundant acoustic features and complex model architectures to enhance the model's performance, but these works sacrifice the portability of the model. To address this problem, we propose a model utilizing only the fundamental frequency from electroglottograph (EGG) signals. EGG signals are a sort of physiological signal that can directly reflect the movement of the vocal cord. Under the assumption that different acoustic features share similar representations in the internal emotional state, we propose cross-modal emotion distillation (CMED) to train the EGG-based SER model by transferring robust speech emotion representations from the log-Mel-spectrogram-based model. Utilizing the cross-modal emotion distillation, we achieve an increase of recognition accuracy from 58.98% to 66.80% on the S70 subset of the Chinese Dual-mode Emotional Speech Database (CDESD 7-classes) and 32.29% to 42.71% on the EMO-DB (7-classes) dataset, which shows that our proposed method achieves a comparable result with the human subjective experiment and realizes a trade-off between model complexity and performance.  
speech emotion recognition; knowledge distillation; cross-modal transfer; electroglottograph +
Footnote †: journal: Applied Sciences
## 2 Background and Related Works
### Time Series Deep Learning Anomaly Detection
Reference work [47] studies the method framework of unsupervised anomaly detection, adopts the method based on gradient and quadratic programming to jointly train LSTM and single-classification support vector machine OC-SVM, and describes SVDD based on OC-SVM and support vector data to find the decision function. Compared with the traditional method, the anomaly detection effect is also improved. Due to the particularity of spacecraft data, there are fewer open-access data, and it is more difficult to find annotated data. Transfer learning is a branch of deep learning which reduces training costs by transferring knowledge in a certain field to target tasks or builds good models for small sample tasks [19]. Currently, the transfer learning paradigm widely used in the field of deep learning is the method of pre-training and fine-tuning. The pre-trained model is obtained on a large-scale dataset, and then the pre-trained model is used to fine-tune the training set of the target task to obtain the target model. The Bidirectional Encoder model (BERT) [48] in the field of natural language processing, and the method of transfer learning are used to carry out two stages of pre-training for the model. When fine-tuning, the first few layers of the pre-trained model can be frozen to preserve the ability of neural networks to extract shallow features of data. In the field of anomaly detection, transfer learning can solve the problem of less labeled data to a certain extent. The method of transfer learning is adopted in reference work [17], which greatly reduces the time of establishing an anomaly detection model for each channel of the spacecraft and improves the accuracy.
## 3 Methodology
### Definition of Requirements and Automated Testing  
Traceability between requirements and test scripts is a key output of the validation process. Simulink Requirements Editor was used in this work to author and manage the system requirements as it offers the capability to link requirements to various model elements and test scripts, thus achieving the traceability required to prove that the model was validated.  
Table 3 displays the sixteen requirements which characterise the behaviour of the model in response to the input signals. Simulink Design Verifier was used to automatically generate a test harness for the model. A test harness is an instance of a model where input values can be simulated, and the corresponding outputs are monitored [53]. In this instance, the test harness was generated in Simulink Design Verifier by defining the model element for custom code (Figure 8), as the component to be tested. The test inputs were automatically generated by the design verifier tool with the model coverage objective being Modified Condition Decision Coverage. These analyse whether the conditions within decisions independently affect the decision outcome during execution [54]. The resulting test harness contained forty-one test cases simulating all possible operating scenarios of the state machine and its equipment phases. Appendix A of this paper contains an example of a typical test case, its associated data and an explanation of each element of the test case.  
The generated test harness was then used to form a test suite in Simulink Test Manager, where logical assessments could be applied to assess event occurrence, and all test scenarios could be automatically simulated. The results of the test simulation were then examined and linked to the relevant requirements to validate the model. Documentation of the test results were automatically generated using the built-in report generator in the Test Manager tool.  
Figure 9: Sample StateFlow logic used to set position value to 90\({}^{\circ}\).
## 7 Validation with Users  
### User Study Objectives  
In this section, we describe the trials carried out over the two prototypes described in Section 6. The purpose of these tests is twofold. On the one hand, we seek to technically validate the prototypes built over HARP, with real users to demonstrate that the architecture enables to deliver haptically augmented AR applications, both to increase perception (Haptic Inspector) and to improve interaction (Simon). On the other hand, we seek to better understand the contribution of haptic feedback to AR-based tasks, both in functional and acceptance terms. First, we explore the perception of the contribution given by haptic feedback with AR visualization for inspection tasks. Second, we explore the expressiveness of Ultrahaptic-based haptics for shape identification procedures. Third, we study the usefulness of haptic aids for AR resizing tasks. Fourth, we explore haptic adaptations for interaction metaphors with mid-air haptics and AR visualization. Finally, we study the grade of acceptance of AR with mid-air haptics with respect to a mobile baseline. The details of user study are presented below.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 5 ECM-Derived Peptide Sequences and Their Use in Biomimetic Hydrogel Functionalisation
### Collagen: P15 Motif  
P15 is a short synthetic peptide sequence (GTPGPQGIAQGRGVV) [109], derived from the \(\alpha\)1 helix domain of type I collagen at residues 947-961. It acts as a cell-binding domain of collagen by stimulating \(\alpha\)2 integrin activation. Numerous studies have demonstrated that P15 can promote attachment for cell types such as fibroblasts and osteoblasts [110]. The P15 motif has also been reported to have applications for coating synthetic bone materials in order to promote osteoblast differentiation, cell adhesion, migration and survival. In addition, it has been linked to an increased MSC expression of ALP, which is an early indicator of osteoblastic differentiation [109].  
The P15 collagen motif has been used to coat anorganic bone material (ABM), which was suspended in hyalurone (Hy) hydrogels to mimic the ECM of bone [111].The study suggested that the ABM/P15 hydrogel matrices can act as an injectable, biomimetic material to promote bone repair. Hydrogels with (ABM/P-15/Hy) and without P15 (ABM/Hy) were tested, where osteoblast-like cells were seeded onto each hydrogel. A greater cell presence and increased cell scattering were observed on the ABM/P15/Hy surface, but only small areas of cells on ABM/Hy, suggesting the P15 peptide promotes cell attachment and migration [111]. These results were confirmed by quantitative assays whereby adherence on ABM/P15/Hy was significantly increased compared with the ABM/Hy surface (\(p<0.05\)), and cell viability was double, for cells cultured on ABM/P-15/Hy [111]. Alongside fundamental cell processes, the ability for P15 to influence osteoblastic activity was considered. Following 10 days culture, ALP, bone morphogenetic protein 2 (BMP-2) and bone morphogenetic protein 7 (BMP-7) were found to have significantly higher expression levels in cells grown on ABM/P-15/Hy, compared with those grown on ABM/Hy [111]. Increased expression of osteogenic genes suggests that P15 may be an initiator of osteoblastic commitment by promoting a capacity for differentiation, much like the native ECM [111]. Even though this study incorporated the P15 motif on a material suspended within a hydrogel, promising results were still obtained in relation to enhancing biomimetic capacity. Further work should be undertaken to investigate the biomimetic traits of P15 through direct hydrogel functionalisation.
## 2 Sensor Structure and Mathematical Model
### Transient Simulation of the Induced Voltage  
As shown in Figure 3, the magnetic field distribution in the oil path is not uniform. The magnetic flux density \(B\) is relatively strong close to the air gap. With a current of 0.5 A, \(B\) can reach more than 100 mT. \(B\) decreases with the increase of height in z-axis direction. The variation of \(B\) along the x-axis is shown in Figure 4. When the z-height is small, \(B\) changes sharply along the x-axis, and a local high gradient magnetic field is formed along the x-axis. When z-height exceeds 10 mm, \(B\) changes gently along the x-axis. In addition, the maximum value of \(B\) is not at x = 0.  
\(\alpha\) can also be seen as the angle between \(B\) and the x-axis. Under different z-height conditions, the variations of \(\alpha\) with x-axis is shown in Figure 5. Due to the error in the simulation calculation, the curve of \(\alpha\) is not smooth. When x \(<\) \(-\)30 or x \(>\) 20, the variation law is more complicated, and it often changes sharply between 90 and \(-\)90. When \(-\)30 \(<\) x \(<\) 20, it is decremented, and positive and negative reversal occurs at x = \(-\)5.  
Figure 4: Variation of the magnetic flux density over z-height.  
Figure 3: Magnetic distribution in the large-diameter oil tube. (**a**) radial; (**b**) axial.  
Figure 2: Working principle of sensor.  
Figure 6 shows a simulated output voltage signal wave when a piece of ferromagnetic debris flows through the sensor at a constant speed of 1 m/s. A pulse output signal is generated, which is similar to the output waveform of the three-coil induction wear debris sensor [14]. According to Equation (3), the inducted voltage is related to the change law of \(\alpha\). The simulated pulse signal also has a positive and negative reversal, which is consistent with the change of \(\alpha\). According to Equation (3), the induced voltage is related to \(\frac{\partial\alpha}{\partial x}\), and the rough \(\alpha\) curve will definitely causes noise in the simulated output voltage signal.
###### Abstract  
The THz Atmospheric Limb Sounder (TALIS) is a Chinese sub-millimeter limb sounder being designed by National Space Science Center of the Chinese Academy of Sciences to measure the temperature and chemical constituents vertically in the middle and upper atmosphere, with good precision and vertical resolution. This paper presents a simulation study that assesses the measurement errors and their impacts on the retrievals. Three error sources, including instrument uncertainties, calibration errors and a priori errors, are considered. The sideband weight uncertainty, the local oscillator, the pointing angle offsets and the measurement noise (NEDT), are considered as instrument uncertainties. Calibration errors consist of the hot target offset, the nonlinearity residual of the two-point calibration, use of the Rayleigh-Jeans (R-J) approximation and the choice of the antenna pattern. A priori profile errors of temperature, pressure and species are also considered. The results suggest that the antenna pattern mainly affects the retrievals in the troposphere. The NEDT is a major error source affecting all of the retrievals. The R-J approximation has a great impact upon the retrievals at 643 GHz, and should not be used. The local oscillator offset leads to an obvious error above 50 km. The effect of nonlinearity residuals cannot be neglected above 70 km. The impact of the sideband weight uncertainty and the hot target offset are relatively small. The pointing and the a priori errors can be neglected in most observation regions.  
TALIS; THz atmospheric limb sounding; error budget; retrievals +
Footnote †: journal:  
Article