esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Phase-Field Model Validation for Dissolution of Minerals
## 2 Dissolution Mechanisms
### Diffusion-Controlled Dissolution Mechanisms  
Figure 1 shows the dissolved diffusion process of soluble minerals based on the diffusion interface. When dealing with the problem of moving boundaries, the conventional approach separates the different phases by a sharp interface. The interface movement is solved by a partial differential equation describing, for example, mass and thermal diffusion equations. These equations have to be combined with boundary conditions of varying values and positions. When some variables (heat flux or concentration) cross the sharp interface, jump discontinuities can occur, making the calculation very difficult. In the PF model, the interface is described as a diffuse interfacial layer with smooth transitions. Thus, the phase transformation is represented by a change in an order parameter (\(\phi\)). As shown in Figure 1, the solid phase is represented by \"1\" while the liquid phase by \"0\", hence the order parameter varies continuously between 0 and 1 at the solid-liquid interface.  
As the congruent dissolution process occurs, the solute is gradually transferred into the solution, the length of the solute base phase decreases, and the solid-liquid diffuse interface gradually moves toward the inside of the solute base phase. The solute concentration in the initial solution is \(c_{\text{L}0}\), while the solute concentration at equilibrium is \(c_{\text{L}e}\). The solute concentration in the solid is kept constant when the diffusion phenomenon in the solid is not taken into account. The change of solute concentration in solution with time is related to its position. The solute concentration (\(c_{\text{L}}\)) increases with time away from the solute matrix phase; solute concentration (\(c_{\text{IS}}\)) decreases with time near the solute matrix phase. The solute concentration in between (\(c_{\text{I}}\)) is in a state of dynamic increase or decrease. \(x_{\text{Se}}\), \(x_{\text{S}0}\), \(x_{\text{L}e}\) and \(x_{\text{L}0}\) indicates the positions corresponding to the above solute concentrations.
## 4 Experimental Results
### Comparison with Benchmark Methods  
Table 4 presents the classification performance of state-of-the-art ML algorithms in the field of ground-based moving target recognition based on seismic and sound signals, for the comparison of classification performance with the proposed method. These algorithms include genetic algorithm optimized support vector machine (GA-SVM) [15], improved BP neural network (BPNN) [28], and Vib-CNN [21]. GA-SVM and BPNN are TML methods, and Vib-CNN is an end-to-end DL method. GA-SVM uses the wavelet decomposition energy ratio, zero-crossing rate, mean, peak, and waveform factor as feature extraction methods, and improved BPNN uses kurtosis, zero-crossing rate, and ratio of high- and low-frequency energy. To fairly evaluate these methods, a model was trained for 11 experiments, each with 200 epochs. The median classification accuracy of the test set was taken as the classification effectiveness, and the computation time on the test set was recorded. All methods were run on a desktop computer with an Intel Core i710 CPU and a GeForce RTX 2070S GPU.  
The classification results of the models on the test set were similar, but their running times differed significantly. Vib-CNN had a much greater time than the other models, but its accuracy for target recognition was not much different from that of the other models. The classification performance and operation time of GA-SVM and improved BPNN were similar, and the classification accuracy and operation time of GA-SVM was slightly higher than BPNN. GA-SVM is slightly higher than the proposed method in this paper in terms of classification accuracy, but the performance time of the GA-SVM method is five times higher than the proposed method in this paper. Compared with TML and other DL methods, the  
Figure 8: Confusion matrix for the model results on the test set.  
Figure 7: Experimental results of model on the train dataset and validation set. (**a**) Accuracy. (**b**) Loss.  
proposed method has lower algorithmic complexity and similar recognition accuracy, and can be deployed at a lower cost and with lower power consumption.
# Mathematical Modeling of Capillary Drawing Stability for Hollow Optical Fibers
###### Abstract  
The stability problem solution of the manufacturing (drawing) of the quartz capillaries (pipes) for microstructured optical fibers (hole-assisted fiber) is important for determining the effective technological production modes. This importance is also caused by the high cost of fiber production and strict requirements for the accuracy of the fiber's geometric characteristics. Therefore, a theoretical approach to this problem is relevant and necessary. A modified capillary drawing model that takes into account inertial, viscous, and surface tension forces, as well as all types of heat transfer is proposed in the research. Within the framework of the linear theory of stability, a mathematical model of isothermal and nonisothermal capillary drawing has been developed. The stability of the process is studied depending on the drawing ratio and the Reynolds number. The analysis of the sensitivity of the process to perturbations in the boundary conditions is carried out. The secondary flow that occurs upon transition to the region of instability is also studied. It has been found that at draw ratios above critical values (instability region), undamped oscillations arise. The existence of optimal parameters of the heating element is shown: temperature distribution over the furnace surface and furnace radius, at which the stability of the process of drawing quartz tubes increases significantly (several times).  
photonic crystal fibers; capillary drawing; mathematical model; stability; calculation algorithms +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Enhancing Traceability of Italian Almonds through IR Spectroscopy and Chemometric Classifiers
###### Abstract  
Almonds are the seeds of the almond (Prunus Amygdalaus) tree and are a nut consumed worldwide. The present study utilized the ATR FT-IR technique followed by a chemometric analysis to develop predictive models for determining the geographical origin of almonds from three regions in Southern Italy (Apuila, Calabria, and Sicily). IR spectra were collected on both the almond shell and the edible kernel to accurately characterize the three different geographical origins. The spectroscopic data obtained were processed using Soft Independent Modeling of Class Analogies (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Both SIMCA and PLS-DA revealed that the shell spectra are more useful for assessing the geographical origin of samples. In particular, the PLS-DA model applied to these data achieved a 100% correct classification rate (on the external test set of individuals) for all the investigated classes.  
almond; nuts; classification; geographical origin; traceability; FT-IR; PLS-DA; SIMCA Article
## 2 Related Work
Existing literature revealed that many watermarking techniques prioritize robustness at the expense of the other two aspects due to inherent trade-offs. To address this limitation, we propose a novel DWT-based approach that leverages gradient analysis. This approach aligns well with existing research that demonstrates the effectiveness of the DWT for robust watermarking in medical images, often alongside additional techniques like feature extraction. Furthermore, gradient information has proven valuable in some watermarking methods, further supporting our focus on the DWT with gradient analysis to optimize the crucial balance between imperceptibility, capacity, and robustness.
## 3 Results and Discussion
### Pigment Content  
#### 3.2.1 Pigment Content in Beetroot Pomace  
Pigment content in beetroot pomace was divided in measurement into red-violet betalain (Figure 1a) and yellow vulgaxanthin-I (Figure 1b). In the tested samples after drying process, two behaviors were observed in the pigment content. In raw beetroot pomace, pomaces fermented with _Limosilatchocallus fermentatum_ or _Lactiplantibacillus plantarum_ no changes in red/violet pigment content were found while for the rest of the samples a significant decrease was observed. Almost the same observation was made for yellow pigment content in beetroot pomace, in raw beetroot pomace, pomaces fermented with _Lactiplantibacillus plantarum_ or _Levilactobacillus brevis_ where no changes were observed. For the rest of the samples, a statistically significant decrease was noted. The normal situation for the drying process is that the pigments are exposed to hot air in the CD and may also be degraded during the freeze-drying process. However, the pigments in the raw material are protected by the tissue that is not damaged during fermentation.  
The highest degradation of red/violet pigment after drying was observed for samples with fermentation by _Lactobacillus Mix_ (77% and 79% for CD and FD) and _Levilactobacillus brevis_ (73% and 64% for CD and FD), while for the yellow pigment it was observed for samples _Lactobacillus Mix_ (82% and 86% for CD and FD) and _Limosilactobacillus fermentatum_ (81% and 86% for CD and FD). The smallest degradation after drying was observed in raw beetroot samples (about 40% and 0% for CD and FD), independently from the drying type and analyzed pigment. Furthermore, a small change was observed for _Lactiplantibacillus plantarum_ (1% for red/violet pigment and 48% for yellow pigment), however, the amount of red and yellow pigment was the lowest in all analyzed samples.  
After the fermentation process in raw beetroot pomace, a statistically significant increase in red/violet and yellow pigment content was observed, the exception was sample fermented by _Lactiplantibacillus plantarum_ in which a decrease trend in yellow and no change in red pigment was evaluated.  
Figure 1: Pigment content in tested fermented beetroot pomace (**a**) Red pigment (betanin) content in beetroot pomace; (**b**) Yellow pigment content in fermented beetroot pomace. A, B...—different indexes for individual series mean statistically significant differences for given values at the level of \(p<0.05\).  
In fermented samples, the pH during fermentation has changed and decreased below 4. In the case of betroot fermentation, the obtained pH below 4 favors the maintenance of betalain pigments protection for which the optimal pH is 3-6 [51; 52; 53].
## 4 Results and Discussion
### Limitations and Superiority  
Although the PCA-LSTM-GA model proves to be pretty promising in predicting air humidity and temperature at the end of a water trickling roadway, the following challenges still exist. Firstly, the dataset was acquired from an independently developed modeling roadway which simplified the experimental conditions (e.g., fracture networks). Consequently, the trained model might highly possibly fail to be generalizable to real engineering practice. Secondly, there are still some omissions of other influencing variables for air humidity and temperature prediction, such as wind speed, atmospheric pressure, and ventilation resistance. Thus, for the purpose of ensuring prediction reliability and accuracy, it is necessary to validate and improve the generalization capability after an  
Figure 14: Importance scores of input variables for (**a**) air temperature \(T_{f_{out}}\) and (**b**) air \(RH\)\(RH_{out}\) prediction.  
increasing number of influencing features and data instances are incorporated into the database to ensure the accuracy of prediction.  
In this study, a hybrid machine learning (ML) model integrating the PCA-LSTM-GA was proposed for air humidity and temperature prediction. The method requires no thermodynamic parameter tests (e.g., thermal conductivity and heat and mass transfer coefficients) and complex mathematical models once the ML model has been trained. The only thing we need to do is to arrange temperature measurement boreholes in the surrounding rocks at a given interval (Figure 15). The rock temperature values at different depths in boreholes are measured with our independently developed multi-point geo-temperature tester, and the arrangement of temperature sensors is the same as that shown in Figure 3c. The acquired rock temperature data and inlet airflow humidity and temperature data of roadway are transferred to the host computer. If there is thermal water trickling in the roadway, the flow rate and temperature need to be processed for equivalence.  
Another enlightenment given to us by this study is that the thermal water trickling into the roadway, which damages the workers' operation comfort, cannot be ignored. For this type of roadway, we proposed that a composite heat insulation structure with heat insulation and support for the roadway should be arranged. Concrete is sprayed onto the stable roadway walls first, and then heat insulation materials are filled into the loose ring of surrounding rock through grouting anchor cable or anchor rod to block the fracture water. Afterwards, heat insulation materials are sprayed onto the outside of the concrete layer. Finally, concrete is sprayed onto the outermost layer. This method can effectively reduce the heat conduction and convection between rock mass and thermal water.
# Improved PEG-Based Construction of Analog Fountain Codes
## 1 Introduction  
With the emergence of applications such as intelligent transportation, tele-surgery, and industrial automation, three main service categories are defined in the fifth generation (5G) mobile network. Among these, ultra-reliable low latency communication (URLLC) poses the most significant challenges due to its physical layer design [1]. URLLC requires a short end-to-end latency, along with high reliability evaluated by block error rate (BLER). These strict constraints impose stringent requirements for the design of channel coding in short block length regime.  
Recently, analog fountain codes (AFCs) [2; 3; 4], a type of rateless codes, have shown their great potential to improve the latency and reliability while keeping bit-level granularity. Several impressive studies [5; 6; 7; 8; 9; 10; 11; 12] have explored the coding schemes and constructions of AFCs to make them promising for URLLC. In [5], optimal AFC parameters were analyzed and designed to minimize the bit error rate. In [7], a low-complexity weight-adaptive AFC transmission scheme was proposed based on extrinsic information transfer (EXIT) analysis. In [9], Bose-Chaudhuri-Hocquenghem (BCH) precoders were designed for AFCs to improve performance for short packet communication. In [11], joint design of AFCs and quasi-gray constellation mapping modulation was proposed, approaching the benchmark. In [12], a short partitioned transmission strategy based on AFCs was proposed to further enhance the error rate and latency performance.  
maximizes the cycle length, and the earlier selected edges are less likely to be involved in short cycles. We propose to add edges and allocate their weight coefficients in descending order simultaneously. To be specific, we select edges with the smallest reliability measure, and allocate larger weight coefficients to earlier selected edges. Our analysis shows that the proposed construction effectively decreases the probability that larger weight coefficients are involved in short cycles. The simulation results show that it has higher reliability over the existing constructions in short block length regime.  
This paper is organized as follows. Section 2 presents the necessary background. Section 3 discusses our proposed construction as well as the simulation results. Section 4 concludes the paper.
# Predicting Team Well-Being through Face Video Analysis with AI
## 5 Discussion
#### Limitations  
The results presented here are valid only for the discussed case study. Thus, although the methodology employed is generalizable, more similar case studies in different contexts and with different participants should be conducted to further investigate these conclusions in the field of cognitive sciences. These results show links but do not allow causalities to be determined. This is one of the limitations of the proposed methodology, but other factors should also be acknowledged. In data preparation, the FAS did not utilize explicit face alignment and treated each video frame in isolation, possibly overlooking the importance of temporal dynamics. These two factors could have a negative impact on the performance of the proposed model as they could, respectively, complicate emotion recognition and neglect temporal entanglements. Moreover, inherent assumptions in the employed algorithms, like using the field of view (FOV) cone model for gaze pattern estimation, can also introduce errors to the proposed findings. That is also true for the data preprocessing techniques employed, such as smoothing or linear interpolation, coupled with the dependence on specific feature selection strategies, which may introduce potential biases and uncertainties. Another limitation of the proposed study is the small number of data points available, which restricts an accurate exploration of the feature space. The relative scarcity of data points limits our predictive model's capacity to generalize beyond this study. While hyperparameter search space was leveraged by grid-search cross-validation, they might not capture the entirety of potential configurations. Also, the use of the SHAP-based feature analysis brings its own set of challenges. Finally, the modeling strategy relies on the fundamental assumption of relative independence among features, an ideal scenario that is challenging to achieve consistently. This assumption may mean that the model sometimes does not accurately capture interactions between features or possible non-linear effects.
## 1 Introduction
Many word embeddings have started to appear in the field, such as Word2 vec [19] and Glove [20], which deal with the meaning of the word. They are context-independent, producing only one vector (embedding) for each word, combining all of the word's different senses into a single vector. BERT and ELMo are context-dependent and can generate different word embeddings for the same word depending on its position in the context. These models, trained on massive amounts of data, can significantly improve the performance of a wide range of NLP problems. As vocabulary size determines vector size, this can lead to lengthy, complex vectors that take up space and slow down algorithms. Moreover, embeddings are domain-specific and task-dependent, hindering transfer learning to a model with a different vocabulary on the same scale. However, modifying the vocabulary requires retraining the entire model from scratch. Therefore, contextual word embeddings become crucial in addressing these challenges and capturing the contextual nuances of words. BERT and ELMo are two cutting-edge models that use pretrained representations to pick up on the semantic and syntactic information of words in different situations. This lets them learn from each other and improve their performance [5]. We apply these models to Twitter and YouTube data and compare them with older methods to find the best RNN architectures for classifying spam. We also provide a detailed comparative analysis, offering insights that could guide future efforts in spam detection and contribute to strengthening cybersecurity measures on rapidly growing OSNs. Our main goal is to not only improve current methodologies but also to direct future research trajectories in spam detection technologies. The key research questions explored are as follows:  
* **RQ1**: Do contextualized embeddings (BERT, ELMo) outperform noncontextualized word vectors (Word2 Vec, GloVe) for detecting spam? * **RQ2**: How do RNN models (LSTM) compare with standalone BERT and ELMo models in terms of accuracy? * **RQ3**: Which embedding technique and model combination achieves the highest performance across metrics?
# A Distributed RF Threat Sensing Architecture
## 3 Proposed Approach
### Sensor Interconnection with a SOC  
As mentioned in Section 3.1, the sensors support an Ethernet connection and the MQTT protocol. Using a secure connection, we forward the MQTT messages to the MQTT server hosted in a SOC. The typical message includes:  
* Device name and hostname.
* Device ID and IP addresses.
* Various measurement values, including threshold, delta, and geographic coordinates (longitude and latitude).
* Antenna orientation details (azimuth, elevation, roll).
* Measurement settings (samples, period).
* Alarm status.
* Sensor attenuation and actual measurement values.
* Device information (location, band, name, group).
* Timestamp and version.  
The messages, which include both raw measurements and alerts, are retrieved in near real time, at an adjustable by the user time interval (heart bits), and are visualized--using _OpenSearch Dashboards_--in the SOC platform, as can be seen in Figure 6.  
In this way, SOC has the capability to continuously monitor, long term, the RF spectrum in multiple areas, and at the same time investigate any alerts that may arise in the filtered bands of interest. Also, if one considers the use of directional antennas on the distributed sensors, the true direction of the jamming source can be also identified.
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 5 Performance Evaluation
### Execution Time  
The execution time is based on the total number of operations required for the authentication process. Figure 7 depicts the execution time comparison between A-MAC protocol and the existing protocols. It can be seen that the execution time is lesser in case of the proposed protocols.  
Figure 5: Communication cost.  
Figure 6: Storage cost.  
protocol. This improvement is attributed to the cause that A-MAC relies upon relaying (multi-hop) message delivery.