esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In order to evaluate the structural stability of the satellite/payload, an experimental verification process is required at the structural-structural model (STM) phase for the acoustic/vibration loads. Also, the satellites that enter the mission orbit are exposed to severe thermal environment as the satellite operates between the Earth and the Sun at regular intervals during the mission life. Therefore, orbital thermal analysis should be implemented to confirm the temperature range of the satellite/payload. Before performing orbital thermal analysis, the material properties of the thermal analysis model and the thermal conductivity coefficient between components must be corrected, and the temperature data for the model correction must be experimentally obtained to be used in the correlation of the thermal analysis model. In this study, the sine vibration tests and acoustic load tests are performed to verify the structural stability of the STM of the deployable reflector antenna under the dynamic loads in the launch environment. In addition, the temperature data for constructing the orbital thermal analysis model was obtained from the thermal balance tests. Based on the Environmental test performed in this paper, we confirm the detailed design of the deployable reflector antenna. These experimental results will be used in the qualification model (QM) phase.
# Biomass Fly Ash Self-Hardened Adsorbent Monoliths for Methylene Blue Removal from Aqueous Solutions
## 2 Materials and Methods  
### Materials  
Biomass fly ash (BFA) was collected along three consecutive production days at a biomass cogeneration plant, operating in a pulp and paper mill, equipped with a bubbling fluidised bed boiler (BFBB) with an installed power of 99 MW. The combustion temperature is around 825 \({}^{\circ}\)C and sand is used as the bottom bed. Eucalyptus bark and small amounts of knots, which are organic rejects produced from the pulp screening process, were used as fuel. A batch composed of equal amounts of the samples collected each day was prepared, then dried in a ventilated laboratory oven at 105 \({}^{\circ}\)C and sieved through a 2 mm mesh (Retsch AS200, Haan, Germany). Afterwards, the BFA was kept in closed opaque polypropylene containers before characterization and monoliths production.  
A commercial aluminium powder (AP) (Expandit BE 1101, Grimm Metallpulver GmbH, Roth, Germany) was employed as a porogenic agent. In an alkaline medium, the AP reacts with water, releasing gaseous hydrogen following the Chemical reaction (1) [11]:  
\[\text{Al}\;(\text{s})+3\text{H}_{2}\text{O}\;(\text{I})+\text{OH}^{-}\;(\text{ aq})\rightarrow\text{Al}(\text{OH})_{4}^{-}\;(\text{aq})+\frac{3}{2}\text{H}_{2}\;( \text{g}) \tag{1}\]  
The formation of H\({}_{2}\) bubbles during the mixing and self-hardening processes generates pores. To stabilise the foam, a commercial surfactant (Hotaspur OSB, Clariant, Barcelona, Spain) was used. A commercial Portland limestone cement (PLC) (type II/B-L 32.5 N) was also used aiming to preserve the formed porous structure, by accelerating the hardening process while improving the mechanical strength of the cured monoliths.  
Methylene blue (MB), a cationic dye (C\({}_{16}\)H\({}_{18}\)ClN\({}_{3}\)S, Sigma Aldrich, Steinheim, Germany) was selected to evaluate the monoliths' adsorption capacity due to its stability in alkaline environments, as well as its widespread use in several industries [12].
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 8 Challenges and Future Trends
### Potential Future Arabic OCR Applications  
Many OCR applications are not yet been developed or matured for Arabic, even though they are already under development for other languages. This includes, but is not limited to, the following uses and domains:  
* Education: Arabic OCR can be used by educators to automate the scoring of handwritten essays [74]. Moreover, calligraphists can apply this technique in calligraphy classes to assess handwriting calligraphy quantitatively instead of through time-consuming qualitative assessment [75]. In addition, universities, colleges, and schools can develop solutions to automate their admissions processes and the paperwork required during registration [76];
* Legal: Arabic legal sectors frequently use handwritten paperwork, as do courts during trial sessions. The harnessing of OCR capabilities could accelerate these processes and decrease the burden of documenting handwritten text in typed form [77];
* Governmental and healthcare sectors: The conversion to electronic records of documents that are not computer-based or that are usually filled out by hand can take tremendous amounts of time. This can be overcome by developing methodologies for applying OCR solutions in such use cases for the purpose of effective digitization;
* Studies: Many fields of study and focus on the handwritten form of writing. For instance, typography involves the anatomical design of the font, which can be studied in depth and can be further used by deep -earning researchers to generate handwritten Arabic that is as close as possible to human handwriting [78]. Furthermore, historical studies can gain enormous benefits from OCR in converting ancient texts to digital form. However, this necessarily requires the development of Arabic OCR solutions that can appropriately deal with complex font conditions, as there is a lack of investigations on this topic [79];
* Forensics and IoT: In forensics, there is a lack of work that addresses the issue of identifying writers from their handwriting. Much more advanced Arabic OCR is required for this purpose. Additionally, this can be further extended semantically to differentiate writers through both the ability to recognize images and the meanings of words [80]. Moreover, the IoT can be integrated with OCR to provide a mechanism for signature verification [81];
* Psychological Research: Psychological studies involving the use of Arabic handwriting to examine personal traits are examples of the use of OCR within psychological research methods. This field is still in its infancy. Moreover, if appropriate training data are fed to the model, OCR can automate the classification process in the research and help psychology researchers make predictions [82];
* Videos: The application of OCR to recognize Arabic handwriting in sequences of photographs in sequential time frames is an area that is open to research. Moreover, the application of OCR in real-time adds a layer of complexity, but makes OCR more practical [83].
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 2 Background
### NMEA Standard  
As discussed previously, the GNSS chipsets utilize the pseudorange measurements to determine the user location. The results, e.g., the positioning information and the satellite information, are usually delivered to users through the NMEA 0183 protocol, which is supported by all chipsets for exchanging the processed measurements.  
Android smartphones employ NMEA messages to deliver the positioning results. Several NMEA messages are supported by these smartphones, such as the fix data (GGA), the recommended minimum data (RMC), the overall satellite data (GSA), and the detailed satellite data (GSV) messages. In particular, the DGNSS infrastructure presented in the next section utilizes two of these messages: the GGA message and the GSA message. The GGA message gives the position fix in WGS-84, while the GSA message delivers a list of satellites that contribute to determining the position fix. As an example, the definition of the GSA message is explained in Table 1. One GSA message can contain up to 12 active satellites. There can be several GSA messages for one epoch when more than one constellation is used.
## 3 Materials and Methods
### EIM Data Processing and Classification
#### 3.3.1 Integration Methods  
A. The CV measures the relative dispersion of a set of data in a range of values \([0,1]\)[33], where values near 0 suggest little dispersion (homogeneity). At the same time, those near 1 indicate a greater dispersion (heterogeneity) of the pixels. The CV is calculated as the ratio between the standard deviation \(\sigma\) and the mean \(\mu\) of a set of values (in this case, the image pixels), as is shown in Equation (1).  
\[\text{CV}=\frac{\sigma}{\mu} \tag{1}\]  
B. The CLAHE method adjusts the local image contrast by dividing the image into small regions and redistributing the pixel intensities within each region to increase the contrast locally, while avoiding overexposure [21]. When combined with the CV (\(CV_{\text{CLAHE}}\)), it helps to highlight local details, improving the discrimination of structures in the breast tissue. Equation (2) shows how this combination was performed. Each of the seven EIM images was processed using CLAHE to enhance the local contrast. Then, the CV of the improved images was calculated, allowing for the identification of the relative variations between the layers and emphasizing the regions associated with lesions and anomalies [34].  
\[\text{CV}_{\text{CLAHE}}=\frac{\sigma_{\text{CLAHE}}}{\mu_{\text{CLAHE}}} \tag{2}\]  
C. The Root Mean Square Contrast (\(\text{C}_{\text{RMS}}\)) measures the dispersion of the grayscale intensities [35], providing a reference for evaluating the differences in the tissue structure. The sum of the differences in the coordinates \((x,y)\) between the seven depth pixels (layers) from the EIM images and their corresponding mean intensities was computed (Equation (3)).  
Figure 2: Diagram of the process followed to analyze the EIM images.  
This process allowed us to obtain the local variability. Consequently, the \(C_{\text{RMS}}\) integrated information from the different layers of the EIM, taking into account the variations in the breast tissues found in each of them. Figure 3 illustrates the integration of EIM images using the \(C_{\text{RMS}}\), resulting in a single image that emphasizes the key structural differences across the layers. This enhancement facilitated the identification of patterns related to anomalies, such as tumors or calcifications.  
\[C_{\text{RMS}}=\sqrt{\frac{1}{N}\sum_{k=1}^{N}(I_{x,y,k}-\mu_{x,y})^{2}} \tag{3}\]
###### Abstract  
Under the action of wind, traffic, and other influences, long-span bridges are prone to large deformation, resulting in instability and even destruction. To investigate the dynamic characteristics of a long-span concrete-filled steel tubular arch bridge, we chose a global navigation satellite systems-real-time kinematic (GNSS-RTK) to monitor its vibration responses under ambient excitation. A novel approach, the use of complete ensemble empirical mode decomposition with adaptive noise combined with wavelet packet (CEEMDAN-WP) is proposed in this study to increase the accuracy of the signal collected by GNSS-RTK. Fast Fourier transform (FFT) and random decrement technique (RDT) were adopted to calculate structural modal parameters. To verify the combined denoising and modal parameter identification methods proposed in this paper, we established the structural finite element model (FEM) for comparison. Through simulation and comparison, we were able to draw the following conclusions. (1) GNSS-RTK can be used to monitor the dynamic response of long-span bridges under ambient excitation; (2) the CEEMDAN-WP is an efficient method used for the noise reduction of GNSS-RTK signals; (3) after signal filtering and noise reduction, structural modal parameters are successfully derived through RDT and illustrated graphically; and (4) the first-order natural frequency identified by field measurement is slightly higher than the FEM in this work, which may have been caused by bridge damage or the inadequate accuracy of the finite element model.  
dynamic deformation monitoring; long-span arch bridge; GNSS-RTK; CEEMDAN-WP; RDT; finite element simulation +
Footnote †: journal: Applied Mechanics and Physics Letters
## 5 Discussion
### Numerical Simulation and Correlation with Experimental Results
The Mg/Si and Fe/Si ratios increased over time, resulting from the continuous dissolution of clinopyroxene and olivine and precipitating trace amounts of Mg-Fe-rich carbonates, as well as clay minerals. SEM and XRD observations showed a significant amount of salt crystals lining the samples' surfaces. However, small amounts of Na\({}_{2}\)O were measured in the rock, contrasting with the noticeable quantity of crystals observed in SEM images. The Na and Cl concentrations also showed a cyclic decrease and increase in concentration, not consistent with an unlikely continuous evaporation of the brine during the experiments. We believe that halite and gypsum crystallized after opening the chamber and degassing, or during drying. As discussed in Section 2, other authors with similar experimental approaches have observed mineral carbonation occurring in time frames comparable to those applied in our experiments. Still, the lack of noticeable precipitation of carbonate minerals during our experiments was not unexpected, since the experiment was designed to look at early-phase dissolution conditions. The proportion of SC CO\({}_{2}\)/brine/rock, was defined to ensure that the brine would always be supersaturated CO\({}_{2}\) during the experiments, as is expected in early-phase injections when dissolution is the dominant process. Such proportions imply that, during the whole duration of the experiments, the pH remained very low in the autoclave, and carbonates could not precipitate. Exposure of the samples to the CO\({}_{2}\)-supersaturated brine for longer periods would probably result in consumption of the CO\({}_{2}\), pH increase and precipitation of carbonate minerals. Given the time constraints of the project, for the second run of experiments, with the gabbro-diorite sample, the duration of the runs was kept constant, but the CO\({}_{2}\)/brine/rock proportion was changed. The volume of CO\({}_{2}\) was decreased and the mass of rock increased, to see if mineral carbonation would be observed during at least the 64-day runs. The results of this second round of experiments have not yet been fully explored as geochemical modelling is still being done, but they will be the basis for defining the physical conditions and duration of the next set of experiments.
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 2 Background and Motivation
### The Few-Shot Problem  
The few-shot problem [7, 8] in time series prediction refers to the challenge of making accurate forecasts when only limited training data are available, necessitating models that can generalize effectively from just a few examples. Several critical issues stem from this scarcity of data, including the limitation of prediction length and model overfitting [9], as accurately modeling long-term dependencies with insufficient historical information becomes challenging.  
In CDF, the few-shot problem could be more acute. CDF requires forecasting future trend changes over periods of 2 to 4 months to allow sufficient time for resource procurement. However, traditional time series forecasting algorithms require extensive historical data, of more than 300 days, to achieve a reliable generalization to achieve a prediction length of 60 days. In practice, resource pools during the resource delivery phase often contain merely 30 to 60 days of data. This significant shortfall exacerbates the challenge of limited prediction length and overfitting. Consequently, enhancing prediction accuracy under these constraints through few-shot models or by integrating data augmentation becomes essential in CDF.
## 4 Conclusions
In terms of the radiometric properties, homogeneous features, such as roads, show better performance when compared to heterogenous features, such as roofs, which are constructed with a variety of materials. The heterogenous features are especially affected by object size in the presence of geometric distortion. Similar tendencies are observed in the case of phenological properties, where the normalization of the homogeneous features, such as forests, is better than of the heterogenous features, such as grass and crops. Quantitatively, the NRMSE of the proposed method is the most improved when compared with the performance of the subject image. In other words, the normalized image of the proposed method is the most similar to the radiometric and phenological properties of the reference image, which represents the applicability of the proposed method as a preprocessing step in change detection when vegetation changes exist in multi-temporal images. Future works will focus on the following aspects. First, acquiring enough images for each season, period, and sensor to more thoroughly evaluate the various characteristics should be undertaken to perform additional verification. Second, spectral indices excluding those used in this study should be considered, and further analysis on the effect of their combinations should be studied. Third, further research on the hyperparameters, such as the number of hidden layers and the number of neurons, should be conducted to achieve the most optimal normalization results. Finally, the applicability of change detection should be investigated by applying normalized images from the proposed method to change detection. Conceptualization, Y.D.E. ; methodology, D.K.S. ; software, D.K.S. ; validation, D.K.S. ; formal analysis, D.K.S. ; investigators, D.K.S. ; data curation, Y.D.E. ; writing--original draft preparation, Y.D.E. ; writing--review and editing, Y.D.E. ; visualization, D.K.S. ; supervision, Y.D.E. ; project administration, Y.D.E. ; funding acquisition, Y.D.E. This research was supported by a grant (19 SIUE-B148326-02) from Satellite Information Utilization Center Establishment Program by Ministry of Land, Infrastructure and Transport of Korean government. The authors declare no conflict of interest.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 4 Spear: Neighbor Discovery Framework
### Application Module  
WSNs are used in many applications and there could be many different requirements for the nodes. For example, when a node detects an emergency, such as a very low temperature, a moving enemy, etc., it needs to inform the whole network quickly. We regard these requirements as latency constraints. That is, the node has to discover the neighbors within a bounded latency. Therefore, in order to send out the information quickly, the node has to increase the duty cycle and turn on the radio more frequently. The application module passes the latency constraints to the energy module and the algorithmic module. Two main functions are implemented in the module (for node \(u_{i}\)):  
1. To collect latency constraints at time \(t\), such that the discovery latency should be bounded within \(\hat{L}_{i}(t)\);
2. to compute an appropriate duty cycle according to the latency constraint.
## 5 Conclusions and Future Directions  
In this paper, we evaluated recent GPT-2 and LSTM-based language modelling architectures to balance the highly imbalanced datasets by generating new text. The generated text was completely new and retained the proper grammatical structure, context and semantic information in relation with the original text. The aim was to improve text classification results by balancing the data with generated text for the underrepresented classes. To achieve the objective of this paper, we selected three datasets and for those datasets we generated text and then performed text classification using the existing networks which were already applied on the original datasets. The obtained results supported our objective by giving improved results for classes having low precision and recall values in the original experiments. We performed experiments on two different kinds of datasets. (1) Sentence-level and (2) Document-level. The LSTM-based text generation algorithm performed very well for sentence-level datasets. But for the document-level it performed very poor, possibly because LSTM-based networks can learn the contextual dependencies upto certain limit and failed for longer texts. However, the GPT-2 based text generation algorithm solved this bottleneck and gives excellent results specially for document-level dataset. Furthermore, two of the datasets were from the academic domain and one was from the social-media domain. The text generation models performed exceptionally well in order to incorporate domain-specific information while generating text for three different datasets. Overall, for all three datasets we observed the improvement of upto 17% in overall performance of the text classification models for the balanced dataset compared to the original results.  
Currently, we evaluated the use of text generation on education and social media domains but this technique can be easily applied to other domains with little modifications to the text generation algorithms parameters. Also, another future direction can be the use of contextualized word embeddings models like BERT and ELMo to understand if these also can work with the text generation models.  
[PERSON] performed all experiments related to text generation. He also contributed in writing results section related to Dataset-1 and evaluating text generation quality. GPT-2 related part in methodology is also contributed by [PERSON] [PERSON] contributed in writing methodology part related to LSTM text generation & classification and results section of Dataset-2. [PERSON] conceived overall idea of the paper, wrote introduction and abstract parts along with his contribution in related work and overall reviewing the text quality. [PERSON] contributed in writing related work, performed experiments on Dataset-3 and write relevant parts of results section. 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.
## 4 Experiments and Result Analysis
### Confusion Matrix Analysis  
Figure 9 presents the confusion matrix generated on the test set, providing a detailed evaluation of the classification performance of the improved model. The horizontal axis denotes the ground truth labels, while the vertical axis represents the predicted labels; values along the diagonal indicate correctly classified instances for each defect type. The improved model demonstrates strong predictive performance across most defect categories. Specifically, the detection accuracy for circular defects reaches 97%, with 2% misclassified as linear defects and 1% as background. For linear defects, the accuracy is 89%, with 3% misclassified as circular defects, 3% as cracks, and 5% as background. For non-fusion defects, the model achieves 96% accuracy, with 2% incorrectly labeled as linear defects and 2% as background. Non-penetration defects are classified with 95% accuracy, with 5% misclassified as background. Crack defects are detected with 89% accuracy, with 11% misclassified as background. These results indicate that the improved model performs satisfactorily across all defect types; however, some degree of false positives and false negatives remains. Notably, the misclassification rate for linear defects is relatively high, and the missed detection rate for crack defects is also considerable. Future work will focus on expanding targeted datasets and refining the model to further enhance overall performance and robustness.  
Figure 8: Flowchart of ablation experiment.  
Figure 9: Confusion matrix of improved model.