esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dispersive Fourier Transformation Technique
### Implementation of DFT
#### 2.2.4. Incoherent DFT  
Most of DFT operations are usually performed in the coherent regime using a transform-limited ultrashort optical pulse. Some recent work has explored the possibility of implementing DFT in the incoherent regime [78], using incoherent broadband optical sources, e.g., amplified spontaneous emission (ASE) radiation, where an external modulator is used to generate gated pulses. The potential of incoherent DFT in photonic microwave arbitrary waveform generation has also been studied [79]. Incoherent DFT may reduce the system cost, but at the price of a significantly higher noise added to the generated microwave waveform. This is because there is no phase correlation between the spectral components of an incoherent source. A full statistical study of the signal-to-noise ratio (SNR) of incoherent DFT process can be found in [80].
# MSA K-BERT: A Method for Medical Text Intent Classification
## 7 Conclusions  
In this paper, we propose a knowledge-enhanced bidirectional encoder representation model that combines multi-scale attention mechanisms to enhance prediction, aiming to utilize domain knowledge and multi-scale information to promote the accuracy of medical text intent classification. The novelty of this paper is the introduction of the knowledge layer, where relevant triples from KG are inserted into sentences to generate a sentence tree enhanced with semantic information. Meanwhile, to prevent the interference of KG on the sentences, we introduce a seeing layer and use the visibility matrix to limit the visible region of each token, thereby preserving the original order and semantic relationship of the sentences. In the multi-scale attention layer, we use filters of different sizes to convolve the input content and connect the resulting feature maps, and then generate a multiscale attention output to capture information at different scales by calculating the attention weights to enhance the robustness and accuracy of the model. In addition, to better deal with the imbalance problem among different categories of medical intent texts, we optimize the model parameters using the cross-entropy loss function, which improves the generalization ability and interpretability of the model. Through experiments on the publicly available medical text intent classification dataset IMCS-21, MSA K-BERT achieves better performance compared with other advanced deep learning models.  
In conclusion, this study provides an effective new method for the medical text intent classification task and offers new perspectives for other similar NLP tasks. However, our model still has potential for enhancement in terms of dataset diversity and computational cost. One limitation of the current study is that all experiments are conducted on Chinese medical texts from the IMCS-21 dataset. While our model demonstrates strong performance in this context, its generalizability to other languages and clinical settings has not yet been evaluated. In the future, we can further expand the scale of the experiment and explore different application scenarios to verify model practicability. We can also reduce the model parameters by using techniques such as lightweight network structures and knowledge distillation to save computational costs.  
Conceptualization, Y.Y.; Methodology, Y.Y.; Writing--original draft preparation, Y.Y.; Writing--review and editing, G.X. and Y.Y.; Supervision, G.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
###### Abstract  
Maritime shipping companies have identified continuous tracking of intermodal containers as a key tool for increasing shipment reliability and generating important economies of scale. Equipping all dry containers with an Internet-connected tracking device is a need in the global shipping market that is still waiting to be met. This paper presents the methods and tools to build and test a prototype of a Container Tracking Device (CTD) that integrates NB-IoT, BLE Mesh telecommunication and low-power consumption technologies for the massive deployment of the IoT. The work was carried out as part of a project to build the so-called \"5G Global Tracking System\", enabling several different logistic applications relying on massive IoT, M2M standard platforms, as well as satellite networks to collect data from dry containers when the vessel is in open sea. Starting from a preliminary phase, in which state-of-the-art technologies, research approaches, industrial initiatives and developing standards were investigated, a prototype version of the CTD has been designed, verified and developed as the first fundamental step for subsequent industrial engineering. The results of specific tests are shown: after verifying that the firmware is capable of handling the various functions of the device, a special focus is devoted to the power consumption measurements of the CTD to size the battery pack.  
NB-IoT; mesh networks; Bluetooth low energy; container logistics; maritime transport Article
# Development of a Virtual Reality-Based Environment for Telerehabilitation
## 2 Materials and Methods
### The Main User Console
#### 2.2.2 Software Application Development
The sequence in which the exercises were performed is recorded as well, providing further insights into the patient's patterns or preferences. This information allows the rehabilitation program to be adjusted to maximize effectiveness. The menu details the type of exercises performed, allowing the therapist to see the variety of activities and ensure that all the necessary muscle groups are adequately trained. The number of repetitions for each exercise is another key parameter, helping to assess the patient's workload and adjust exercise intensity based on their progress. The exercise amplitude provides insight into the patient's range of motion, indicating their flexibility and mobility. The speed of exercise execution is also monitored to ensure movements are performed at an appropriate pace, thus minimizing the risk of injury. Additionally, the exercise time is recorded to assess the overall duration of the rehabilitation sessions and to ensure that the patient is training long enough to achieve effective results. This combined data provides a complete picture of the  
Figure 10: User interface: Session history. patient's progress and allows the therapist to tailor the rehabilitation program to meet the patient's specific needs.
# Linear Mixed Model for Genotype Selection of Sorghum Yield
###### Abstract  
Data analysis using the General linear model assumes the factors to be fixed effects, and the BLUE method, which is based on their mean performance, is appropriate to select the best performing genotypes. The linear mixed model incorporates fixed and random effects that are very important to compare a genotype's performance through BLUP. The purpose of this study was to identify the best performing genotypes that provided a high grain yield using a mixed model, compare the mean performance of genotypes on grain yield using BLUP and BLUE, and determine the impact of drought on sorghum production in Ethiopia. The experiment used water availability as a treatment, and each replication within the treatment levels used a lattice square design for data collection. The design consisted of 14 \(\times\) 14 square experimental units (plots) comprising 196 genotypes, where each row of the square was represented as a block receiving 14 genotypes. The phenotypic characteristics were measured for the study. The statistical methods used for the study were ANOVA and the linear mixed model to identify the best performing genotypes of sorghum. The study found that sorghum production was influenced by drought, which restricted sorghum growth due to a shortage of water. The implementation of irrigation increased the grain yield from 2.48 to 3.17 t/ha, indicating that the difference in grain yield between treatments (with and without irrigation) was 0.69 t/ha. The study compared the general linear model and linear mixed model, and the investigation revealed that the mixed model was more accurate than the general linear model. The linear mixed model selected the best performing genotypes in grain yield with better accuracy. It is recommended to use the linear mixed model to select the best performing genotypes in grain yield.  
Keywords:linear mixed model; best performing genotype; genotype selection +
Footnote †: journal: Applied Sciences
## 4 Results
### Detection of Surface Deformation (DInSAR, AI)
The systematic analysis of both ground deformation (amplitude, pattern, orientation) related to the intrusion of magma in the shallow portions of the edifice, and lava effusion rate once the magma breaches the surface, should help better understand the mechanisms controlling effusive eruptions in closed-vent volcano systems such as Piton de la Fournaise.
## 5 Discussion
### Computational Drug Discovery  
Computer simulations and machine learning for drug discovery are rapidly gaining popularity. The search for new drugs based on machine learning leads to a significant reduction in combinations and a reduction in the diversity of drug candidate compounds, which is significantly different from the traditionally used methods. The development of drugs and the discovery of new compounds in drugs is carried out by the structural synthesis of new compounds and modeling, the inapplicable condition of which is the difference between the drugs being created and those already released into circulation or at the stage of clinical trials. Classical drug design is based on time-consuming manual procedures in which the researcher performs a series of pharmacokinetic and/or molecular dynamics experiments. The use of machine learning allows you to set the required molecular properties, find, extract, and combine new molecules with fragmentary computer modeling. For this, various types of molecular properties are used--these are the physicochemical properties of the compounds and the three-dimensional structures of targets with information about the points or regions of binding. Such computer-generated drug candidates, generated using computational algorithms, previously unknown chemical compounds are subsequently synthesized by traditional chemical technologies and provided as new molecules in bioassays to test predicted interactions with appropriate targets.  
When developing drugs using ML methods, the pipeline of operations performed is divided into five stages:  
* Initial generation of molecules with the desired structural properties using conventional labeled interaction data similar to classical drug design;
* Identification and clustering of different types of created molecules, which have identified properties, are generated using calculations with a coefficient of randomization to increase the diversity. This work is done by generative models;
* Construction of vectors of features for molecules, generated by calculations, and their transfer to the interaction prediction model as input data;
* Computer modeling and obtaining output parameters for each molecule generated by calculations, which allows us to estimate the probability and efficiency of interaction with the target;
* The problem is solved iteratively until the search for an extremum--the maximum estimate of the probability.  
This technology is used to build both generative and test models. An example of automated implementation of the described technology is presented in [88].
###### Abstract  
Magnetic anomaly data rapidly decay with distance and are susceptible to environmental magnetic noise, which leads to reduced accuracy and robustness in estimating magnetic source parameters. This shows significant differences between estimated and true values. Therefore, this study proposes a method for estimating magnetic source parameters based on the LM-OBF algorithm. This method transforms magnetic anomaly data into a two-dimensional orthogonal basis function space using the Gram-Schmidt orthogonalization process, establishing a new forward modeling relationship. It then constructs an objective function within a least squares framework and optimizes it using the Levenberg-Marquardt (LM) algorithm to achieve a stable estimation of magnetic source parameters. The experimental section tests this method using synthetic and field data, comparing it to traditional detection methods. The results demonstrated that the method maintains stable and accurate estimation of magnetic source parameters even at a signal-to-noise ratio (SNR) of \(-10\) dB, outperforming traditional methods in terms of performance under strong noise interference conditions.  
Keywords:parameter estimation; magnetic anomaly; orthogonal basis function; optimization problem +
Footnote †: journal: Applied sciences
## 4 Experiments and Results
### Detailed Experimental Settings  
The named entity recognition tags used in each experiment were identical to those in previous studies, and for GENIA data, unified tags were used in accordance with [3]. For experiments with GENIA data, BioBERT-v1.1 [22] was used as the basic backbone model due to domain characteristics, while the BERT-base-cased model [16] was used as the backbone model for ACE 2004, ACE 2005, and CoNLL 2003 English data experiments. The hyperparameters of the model are detailed in Table A7 of the Appendix A. We applied identical settings across all datasets and model variants to ensure fair comparison. All experiments were implemented using a PyTorch v.2.4.0 framework and conducted on NVIDIA RTX A6000 hardware. For the FFNN size, we conducted a parameter sweep across \(\{200,400,600,800,1200,1600\}\) and selected the optimal value based on performance for each experimental configuration. The experiments were optimized using each development set to measure performance on the test set. For GENIA data, which lack a development set, experiments were designed identical to [3] to measure performance on the evaluation data. For the few-shot environment setup, k-shot training data were created by randomly extracting k samples for each label to create five different k-shot learning datasets. For comparison models, experiments were conducted using the BERT-LSTM-CRF model utilizing nested tags from [6] and the biaffine model from [5].
## 2 Background
### Microgrid and Energy Management System
However, one of the main drawbacks of using high-end machine learning algorithms is the lack of valid data set for effective training. That is why our study focuses not only on developing an optimised machine learning algorithm but also on establishing a novel synthetic load generator to evaluate our proposed model. For the synthetic load generator to be built, an investigation into standard controllable loads, uncontrollable loads, and critical loads with their usage pattern in a typical household was carried out. Any device or appliance's power consumption can be controlled by adjusting their duration on and off time [7]. Controllable loads can be differentiated from uncontrollable loads based on their ability to be turned off without sacrificing the user's comfort [8]. For example, air conditioners and refrigerators can be turned off for a certain period to save power without significantly affecting household comfort. However, microwave ovens or toasters, examples of uncontrollable load, should not be turned off because this directly impacts the occupant's comfort. The critical loads are loads that will result in a significant loss or damage when power off [9]. Examples of essential loads are a smoke alarm, a water pump, and critical load panels in an energy storage system [10]. Following these definitions, in a distributed power system, popular controllable loads, such as refrigerators, HVAC systems, entertainment devices, and fans are considered in the mathematical model formulated in this study. The most commonly used loads in residential settings are supposed to generate an extensive data set of load profiles with artificially induced non-linearity. A list of loads being considered is explained in the methodology section below. Another fact about microgrids is that majority of them are built-in integration with renewable energy sources such as wind turbines, solar photovoltaic (PV), and fuel cells due to the global transition to green technologies. In addition to the eco-friendly advantages of such systems, the renewable resources add more non-linearity into the system due to their stochastic nature, resulting in an advanced/robust energy management algorithm. In 2009, for the EU, nearly 55% of the new installed capacity based on renewable sources corresponded to the wind and PV intermittent generation (39% and 16%, respectively) [11]. According to the Australian Department of Industry, Science, Energy, and Resources, in 2019, 21% of Australia's total electricity generation was based on the renewable energy resources, which consisted of 7% of wind, 7% of solar, and 5% of hydro, making the share of renewables the highest recorded since the 1970s [12]. On this note, the study of load forecasting methodology is critical because it will enable an accurate sizing/scheduling of renewable sources for a microgrid system, preventing resource wastage or shortage and many other system failures.
## 4 Changing the Cultivation Conditions for Optimizing the Production of Bioactive Compounds
### Lipids
#### Light Intensity  
Light intensity level is the major parameter involved in the microalgal metabolism, in relation with photosynthesis activity. Several studies have been conducted in _Pavlova lutheri_, a pymnesiophyte rich in both EPA and DHA. The proportions of PUFA, especially EPA, have been shown to be significantly higher under low light, and saturated fatty acids and DHA levels were significantly higher under high light. In this microalga, the galactolipids, a major component of chloroplast lipid membranes, made up approximately 54%-66% of total lipids. The highest PUFA levels, such as those of EPA, were predominantly found in the galactolipid fraction when the cells were grown at low light [264]. Using radiolabelling carbon sources as substrate, the response to light intensity was specific of the use of hydropercarbonate or acetate [265]. Indeed, it has been shown that the lipid synthesis, including galactolipids and phospholipids, was increased with light intensity when the microalgae used inorganic carbon (hydrogencarbonate) but a less sensitivity was observed to the light intensity changes when acetate, as carbon source, was used. Concerning the omega-3 fatty acids, EPA and DHA synthesis has been shown to be lower with high light intensities with hydrogenocarbonate but not with acetate. These results led the authors to hypothesize the fact that _Pavlova lutheri_ was able to synthesize LC-PUFA by two distinct pathways: one inside the chloroplast, depending on light intensity, and another one outside the chloroplast, independent of light.  
Studies of light intensity were made with the diatom _Thalassiosira pseudonana_ showing significant changes in fatty acid and lipid composition. Cells grown under moderate continuous light level showed high accumulation of TAG and a reduced level of polar lipids. In the diatom _Skeletonema costatum_, the highest levels of EPA are observed with the highest light levels during late exponential phase and during the stationary phase [266]. In _Nitzschia closterium_ and _Isochrysis zhangjiangensis_, after a 3 day-UVA-stress period, the PUFA content increased too [239]. By opposition, in _Nannochloropsis_ sp., the degree of unsaturation of fatty acids decreased with increasing irradiance, with a 3-fold decrease of the percentage of total omega-3 fatty acids, caused mainly by a decrease of EPA [267]. Dark exposure can also impact the lipid content; for instance, the dinoflagellate _Prorocentrum minimum_ reduces its content of TAG and galactolipids, whereas the total content of phospholipids changed little (PC, PE and phosphatidylglycerol decreased; phosphatidylserine, phosphatidic acid and phosphatidylinositol increased) [268].
## 2 Materials and Methods
### Mathematical Model
#### 2.3.4 Random Forest Regression  
Random forest regression (RFR) has several advantages that make it a popular choice for regression tasks, including its robustness in dealing with many input features, both numerical and categorical variables, and its ability to deal with outliers and missing values  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phase** & **Parameter** & **Value** \\ \hline Train and Test Sets & test\_size & 0.2 \\  & random\_state & 0 \\ \hline  & n\_splits & 10 \\ K-Fold Cross-Validation & random\_state & 0 \\  & shuffle & True \\ \hline Feature Importance Estimation & n\_repeats & 10 \\ \hline Visualisation & start\_point & 0 \\  & boundary\_shift & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical hyperparameters for the multiple linear regression code, including parameters for both with and without the application of 10-fold CV.  
in the data, reducing the need for extensive data preprocessing. Furthermore, RFR is capable of capturing complex non-linear correlations between input data and the target variable, making it appropriate for applications where linear models are insufficient. Random forest regression also provides useful insights on feature importance, which aids in finding the underlying relationships in the data. Because of its versatility, it can be used in a variety of regression tasks and can effectively handle large datasets, making it a useful technique for a wide range of applications. The RFR code utilises the scikit-learn library with the default hyperparameter values. Furthermore, the numerical hyperparameters that were set for pre-processing data, model, and the visualisation process are displayed in Table 3.
# Design of Particle Dampers for Laser Powder Bed Fusion
## 3 Experimental Methodology
### Machine and Process Parameter  
The beams with parameters 1 to 31 are made of the aluminium alloy AlSi10 Mg on the LPBF system EOS M280. The beams made of tool steel 1.2709 (par. 40 to 51) are also produced on the LPBF system EOS M280. On the other hand, the beams with the titanium alloy Ti6 Al4V (par 52 to 63) are produced on the LPBF system Concept Laser M2. For the production of all beams, the standard parameter sets of the respective manufacturers are selected for the respective material. The layer thickness is 30 \(\upmu\)m for all beams. The average grain size D50 of the powders was 47 \(\upmu\)m for the aluminium alloy, 31 \(\upmu\)m for the steel alloy and 38 \(\upmu\)m for the titanium alloy. The aluminium and steel alloy beams are removed from the building platform with a saw. No heat treatment is carried out. Due to the high residual stresses of titanium, before the support structures are removed, the components together with the build platform are stress-relieved for 6 h in an argon inert gas atmosphere at 840 \({}^{\circ}\)C and then the components are removed from the build platform by wire erosion. The support structures of the aluminium components could be broken off, whereas the support structures of the steel and titanium alloy beams were milled off. Subsequently, all components were sandblasted. In order to obtain detailed information about the packing density, CT scans of selected beams were carried out. In addition, the mass of all beams was measured in order to draw conclusions about the particle mass.