esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Indoor Visible Light Communication PAM4 System
## 3 Key Component Design
### Bit Synchronization  
In order to recover the source data and ensure the reliable transmission of information, the receiving end must know the start and end time of the symbol and make a decision at the best decision clock, that is, the intermediate moment of the received symbol. The process of extracting the decision clock is bit synchronization, also known as symbol synchronization. This paper uses a lead-lag phase detector to recover the bit-synchronous clock signal. The principle of the lead-lag synchronization method is shown in Figure 5. This is a closed-loop feedback system composed of four parts, namely a phase detector, a controller, a crystal oscillator, and a frequency divider. First, the phase of the input signal and the phase of the clock signal output by the local crystal oscillator are compared through the phase detector to determine whether the phase relationship between the two is ahead or behind. The controller will generate a corresponding control signal and output it to the frequency divider according to the judgment result of the phase relationship. The frequency divider adjusts the phase of the input in real-time. Finally, the phase of the output signal is compared with the phase of the input signal. If the same, the output of the phase signal. If not the same, then repeat the above process again so that the phase tracking and adjustment of the input signal can be achieved, and finally, the phase of the input signal is consistent with the bit synchronization clock signal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part** & **Symbol Value** & **Part** & **Symbol Value** \\ \hline NRZ1 & 0 1 0 1 0 1 0 1 & Decider2(Output) & 0 1 0 1 0 1 0 1 \\ NRZ2 & 0 1 1 0 0 1 1 0 & Decider3 & 0 1 0 0 0 1 0 0 \\ PAM4 & 00 11 01 00 11 0 & XOR & 0 0 1 0 0 0 1 0 \\ Decider1 & 0 1 1 1 0 1 1 & OR(Output2) & 0 1 1 0 0 1 1 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbol output values for each part.  
Figure 4: The decoding scheme of the PAM4 signal.
## 5 Experimental Procedure and Results
### Parameters and Fine-Tuning Strategy  
In fine-tuning models for multi-task classification of hotel reviews, ensuring an optimal environment and strategy to achieve the best performance is crucial. For this research, we used a powerful computing infrastructure, including a computer with an i9-10900X CPU @ 3.70 GHz processor, 64 GB of RAM, and an NVIDIA RTX 3090 graphics card with 24 GB of memory. This configuration allows for the efficient training and evaluation of models, significantly speeding up the experimental process.  
A key component in achieving high-performance models is selecting the right hyperparameters. We employed a \"random search\" method to find the optimal parameters for each model individually, focusing on three key hyperparameters: number of epochs, learning rate, and train batch size. This method randomly samples different combinations of these hyperparameters within predefined ranges to identify the configuration that yields the best results on the validation dataset [16]. For our study, we defined the range for epochs from 5 to 30, the learning rate from 0.00001 to 0.0001, and the train batch size from 16 to 128.  
For each model, we conducted approximately 10 iterations of random search. During the random search process, we evaluated each configuration based on accuracy metrics. The combination with the highest accuracy scores was selected as the optimal configuration for each model. The results of our hyperparameter optimization process are presented in Table 1, which shows the selected hyperparameters for each fine-tuning model. As we can see, the optimal configurations vary across models, highlighting the importance of individualized tuning.  
Additionally, we implemented a comprehensive fine-tuning strategy for each transformer model. This process involved adapting the pre-trained models to our specific multi-task classification problem. We used the Grouphug library [32], which extends the Hugging Face Transformers library, to load pre-trained models and add custom classification heads for sentiment and category tasks. The fine-tuning process involved training all model layers, allowing it to adapt its pre-trained knowledge to our domain. We used a combined loss function that included both sentiment and category classification tasks and a language modeling task. The weighting of these tasks was controlled through the head configs parameter, with the language modeling task given a lower weight (0.1) than the classification tasks. We used the AdamW optimizer with learning rates, number of epochs, and batch sizes individually optimized for each model through a random search, as detailed in Table 1. To prevent overfitting, we implemented early stopping based on the evaluation loss. This approach allowed us to optimize each model's performance while maintaining comparability across models, providing a fair basis for evaluating their capabilities in the multi-task classification of hotel reviews.
# Microwave-Supported Modulation of Functional Characteristics of Gluten-Free Breads
###### Abstract  
Currently, the market for gluten-free products is experiencing a significant growth due to, mainly, the increase in the number of gluten-intolerant patients diagnosed and to the merging of a new niche market for consumers who optionally avoid gluten. Native flours are perceived as viable alternatives for industrially used starch and hydrocolloids blends, which lack in the vitamins and minerals that are in abundance in non-refined raw materials. This study delves into the potential of microwave technology in enhancing the functional and nutritional attributes of gluten-free breads. The research was conducted by employing 900 W microwave power for 8 min for buckwheat and teff flours modification with an initial moisture content of 30%. The modified flours were blended with rice flour and baked to verify the potential of microwave high moisture treatment for adjusting the texture and nutritional quality of the bread. The results revealed that microwave treatment of flours helped in retaining a higher level of essential nutrients and bioactive compounds in gluten-free breads.  
buckwheat; teff; bread; microwave; heat-moisture treatment; polyphenols Article
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment  
### Introduction  
In the application of a BHMS-based GNSS, monitoring is proactively performed and is completely dependent on the GNSS positioning solution. The developed quality control algorithm should therefore be applied to improve the reliability of the monitoring system. To test it, the authors applied the developed algorithm to GNSS data collected from an in-service bridge, the SeoHae cable-stayed bridge located in South Korea, which is a test-bed on which the application of GNSS has already been demonstrated [8; 9]. However, although previous investigations have involved improvement of the GNSS solution, case studies on removing outliers to improve the accuracy of a GNSS solution have rarely been performed.
## 3 Effect of Surfactants on Mechanico-Thermal Properties
### Effect of Surfactants on Thermal Conductivity  
Table 9 displays the effect of different types of surfactants on the thermal conductivity of polymer nanocomposites. The thermal conductivity of G NP/SR nanocomposites increased by up to 14% when the G NPs were modified with Triton X-100. The increase is due to the modified G NPs that have a good dispersion and good interface in the nanocomposites [11]. The thermal conductivity of CNT/PS nanocomposites increased by up to 202% when the CNTs were modified with a non-ionic surfactant, such as PEG (chemical structure showed in Figure 5a). The increase is because the modified CNTs have a more even dispersion character, which was acquired through ultrasonic dispersion with PEG [42]. The thermal conductivity of CNT/SR nanocomposites increased by up to 38% when the CNTs were modified with an anionic surfactant like DBSA (chemical structure showed in Figure 5b). The increase is because the modified CNTs have a good homogeneity, which creates a large interface area with SR [43].  
On the other hand, the thermal conductivity of CNT/TPU nanocomposites increased by up to 2971% when the CNTs were modified with an anionic surfactant, such as SC (chemical structure showed in Figure 5c). The increase is because the modified CNTs were smaller and had a fine distribution that generated effective interactions with the TPU matrix [44]. The thermal conductivity of EG NP/PEVA nanocomposites increased by up to 137% when the EG NPs were modified with SDS. The increase is because the modified EG NPs have a better dispersion, which formed the constant EG NP pathways [45]. The thermal conductivity of GO NS/EDTS nanocomposites increased by up to 404% when the GO NSs were modified with HTAB. The increase is because the modified GO NSs formed a thin, finely dispersed layer, which created an uninterrupted heat flux pathway and interacted with the EDTS matrix [46]. Table 9 clearly shows that the modification of inorganic nanofillers by non-ionic, anionic, and cationic surfactants has increased the thermal conductivity of the prepared polymer nanocomposites.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Surfactant** & **Nanofiller** & **Polymer Matrix** & **Thermal Conductivity** & **References** \\ \hline Triton X-100 & G NPs & SR & \(\uparrow\) (14\%) & [11] \\ PEG & CNTs & PS & \(\uparrow\) (202\%) & [42] \\ DBSA & CNTs & SR & \(\uparrow\) (38\%) & [43] \\ SC & CNTs & TPU & \(\uparrow\) (2971\%) & [44] \\ SDS & EG NPs & PEVA & \(\uparrow\) (137\%) & [45] \\ HTAB & GO NSs & EDTS & \(\uparrow\) (404\%) & [46] \\ \hline \hline \end{tabular}
\end{table}
Table 9: Effect of different types of surfactants on the thermal conductivity of polymer nanocomposites.
# Blind Image Deblurring Based on Local Edges Selection
## 2 Method
### Stopping Criterion  
In the proposed method, the intermediate clear image and blurring kernel are alternately obtained in each iteration. With the iteration time increases, the estimated intermediate clear image and blurring kernel are closer to the real ones. When the iteration time reaches a certain number, the results converge to a higher accuracy, and the iteration can be stopped. In the proposed method, we utilize the kernel similarity to decide the stopping criterion. Kernel similarity [17] is defined as follows:  
\[KS(H,H_{1})=\max_{\gamma}\frac{\sum_{\tau}H(\tau)\cdot H_{1}(\tau+\gamma)}{\|H \|\cdot\|H_{1}\|} \tag{17}\]  
where \(H\) is the real kernel, \(H_{1}\) is the intermediate estimated kernel. The value of kernel similarity ranges from 0 to 1, the larger value of kernel similarity reflects the better result.  
In the proposed method, we set \(H\) and \(H_{1}\) to be the estimated kernel in two contiguous iterative processes. In the first few iterations, the kernel similarity changes largely. After a certain number of iterations, the change of blurring kernel tends to be slow, then kernel similarity increases. After exhaustive experiments, the stopping criterion of the proposed method can be summarized as follows: After the iteration number reaches 10, when the kernel similarity is between \(H\) and \(H_{1}\) higher than 0.95, the iteration stops.
## 3 Results
### Analysis of Spatiotemporal Movement Patterns  
The velocity field of the Sichuan reference station was calculated using the time series model, and Figure 6 and Table 2 show the motion rates of the reference station in the horizontal and vertical directions. The same site may have different velocities in different reference frames, so it is meaningful to discuss site velocities with specifying reference frame. In this paper, all site velocities referred in Figure 6 and Table 2 are aligned to the reference frame of IGS14. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Statistics**} & \multicolumn{3}{c}{**Reference Station Components**} & \multicolumn{3}{c}{**Baseline Components**} \\ \cline{2-7}  & **N** & **E** & **U** & **N** & **E** & **U** & **L** \\ \hline Max & 2.31 & 2.96 & 6.36 & 2.17 & 2.11 & 5.90 & 2.32 \\ Min & 0.33 & 1.49 & 3.44 & 0.05 & 0.04 & 2.55 & 0.10 \\ Average & 1.01 & 2.24 & 4.34 & 0.87 & 1.18 & 3.65 & 1.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the absolute mean value of the influence of CME on each reference station and each baseline (unit: mm). Figure 5: Comparison of residual SCML-SCMN baseline series before and after removing CME. (**a**) Residual sequence of SCML-SCMN without removing CME; (**b**) CME extracted from SCML-SCMN; (**c**) Residual sequence of SCML-SCMN after removing CME. The average velocity of reference stations in the north-south direction is 12.4 mm/y, and 37.0 mm/y in the east-west direction. Among them, the change velocity in the north-south direction at SCML station is 19.4 mm/y, and the change velocity in the east-west direction is 46.3 mm/y, which are higher than the average motion velocity. The other stations had similar horizontal velocities and more stable motion trends. The vertical motion trend of the reference stations is dominated by uplift (accounting for 76% of the total number of stations), mainly distributed in the central and northern parts. The average vertical motion velocity is 0.6 mm/y, and the SCMX station has the most obvious vertical uplift with a rate of 5.0 mm/y. Six stations in the central and southern part of the country have a sinking trend in the vertical direction, among which the SCML has an obvious sinking trend with a sinking rate of 1.9 mm/y. Figure 7 shows the annual rate of change of each baseline in the triangular network calculated using the Sichuan baseline time series. The bottom graph of Figure 7a-c shows the interpolated velocity field of Sichuan using the Kriging interpolation method, which is based on the velocities of 25 Sichuan reference stations in the N, E and U directions. The bottom graph of Figure 7d shows the topographic heights of the Sichuan region interpolated using the same method, which is based on the geodetic heights of 25 Sichuan CORS stations. Table 3 shows the statistics of the annual rate of change of each component of the baseline.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 6 Methods of Reducing Collisions
### Pure Probability Reducing (PPR) Method  
The PPR Method works as follows. For any deterministic neighbor discovery algorithm \(f\), denote the generated discovery schedule for node \(u_{i}\) as \(S_{i}=\{s_{i}(t)|t\geq t_{i}^{s}\}\). For any time \(t\) that \(s_{i}(t)=1\), node \(u_{i}\) turns on the radio with probability \(p_{1}\) (a constant value in \((0,1)\)); that is, to generate a modified sequence \(\tilde{S}_{i}=\{\tilde{s}_{i}(t)|t\geq t_{i}^{s}\}\) as:  
\[\left\{\begin{array}{ll}\text{If }s_{i}(t)=0,&\tilde{s}_{i}(t)=0,\\ \text{If }s_{i}(t)=1,&\tilde{s}_{i}(t)=1\text{ with probability }p_{1}.\end{array}\right.\]  
If two neighbors \(u_{i},u_{j}\) turn on the radio at time \(t\), the expected probabilities of \(|\tilde{N}_{i}|=1\) and \(|\tilde{N}_{j}|=1\) are:  
\[\begin{array}{ll}\text{Pr}(|\tilde{N}_{i}|=1)&=\prod_{u_{k}\in N_{i}k\
eq j }(1-p_{1}\cdot\theta_{k}),\\ \text{Pr}(|\tilde{N}_{j}|=1)&=\prod_{u_{k}\in N_{j}k\
eq i}(1-p_{1}\cdot\theta _{k}).\end{array}\]  
If \(p_{1}=0.5\), \(\theta_{k}=1\%\), the probability of successful discovery is less than \(1/2\) when \(\max\{|N_{i}|,|N_{j}|\}\geq 139\). By choosing different values of \(p_{1}\), the performance could be different. We evaluate the sensitivity of \(p_{1}\) in Section 7.
## 3 Results and Discussion
### Fabrication and Testing  
Building on the exceptional simulation results for S\({}_{11}\), gain, and efficiency, the single-element antenna demonstrated performance typically seen in array antennas. This impressive performance, surpassing many other designs in the literature, validated the decision to proceed with prototype fabrication. The fabrication was carried out using an LPKF laser machine, specially selected to handle the unique properties of the Rogers substrate material. The connector was meticulously integrated into the design once the antenna was fabricated, as shown in Figure 15.  
A detailed comparison of the simulated S\({}_{11}\) values and those measured using a vector network analyzer (VNA) is presented in Figure 16, further confirming the consistency and high performance of the antenna across both analysis methods. The discrepancy observed in the S\({}_{11}\) value at the second resonance can be attributed to several factors related to the antenna fabrication and measurement process. Specifically, the sensitivity of the printed circuit board (PCB) during fabrication can lead to small variations in the material properties, which may affect the resonance characteristics. Additionally, tolerances in the connectors used and variations in the way the connectors are attached to the PCB can introduce slight differences in the impedance matching, further influencing the S\({}_{11}\) values.  
Figure 16: Comparison of simulated S\({}_{11}\) obtained from CST and prototype measurements.  
Figure 15: Fabricated antenna: (**a**) front view, (**b**) back view.
# Nanostructured Biosilica of Diatoms: From Water World to Biomedical Applications
These groups are suitable to bind different biomolecules to diatoms (i.e., enzymes, proteins, antibodies, peptides, DNA, aptamers) [7; 27]. Therefore, diatom frustules have been increasingly recognized as a promising material in biomedical applications. The large surface-area-to-volume-ratio and the nanoporous structure, together with the tunable surface, enables drug loading levels exceeding those of other common drug delivery carriers [28]. Indeed, although drugs are generally physisorbed or chemisorbed onto surfaces, the porous structure allows sustained drug release overcoming the constraints of conventional delivery and, according to the surface modification, _stimuli_-responsive therapy [29]. Moreover, the optical properties of diatom nanostructured biosilica have made a significant contribution to the fabrication of cost-effective surface-enhanced Raman scattering (SERS) platforms for label-free diagnosis. The ability of frustule pore patterns to enhance the Raman scattering of biomolecules attached to the surface promoted the fabrication of diatom-based SERS platforms for label-free diagnosis. Throughout this review, different applications of diatom biosilica are summarized to outline the potentiality of this natural material. The enormous versatility of natural biosilica is highlighted from many observation points: chemical, by pointing out the numerous modifications on the biosilica surface; therapeutic, by investigating the advantages of diatoms as drug carriers; diagnostics, as SERS platforms for label-free sensing; biological, by illustrating the efficiency of diatoms as a scaffold for cell growth and proliferation. In vivo application of the material is also presented to endorse the employment of diatoms in biomedicine.
## 2 Materials and Methods
### Feature Selection and Machine Learning Methods
#### 2.6.2 Model Development  
Following feature selection, we trained and compared four classifiers, each offering distinct advantages for handling high-dimensional, small-sample biomedical data:  
* Logistic regression (LR) with L2 regularization, selected as an interpretable baseline with well-studied coefficient estimates in clinical predictions [42];
* Random forest (RF), included for its robustness to noise and multicollinearity, native support for \(p\gg n\) scenarios, and built-in feature importance measures [43];
* The support vector machine (SVM-RBF), chosen for its strong performance with non-linear boundaries in high-dimensional, low-sample-size settings [44];
* Extreme gradient boosting (XGBoost), evaluated for its state-of-the-art accuracy on tabular biomedical data and built-in regularization to reduce overfitting [45].  
To address the class imbalance, we applied inverse class frequency weighting to improve the minority-class recognition [46]. Hyperparameters were tuned through nested cross-validation (outer 5-fold for the generalization error; inner 5-fold for the model/parameter selection), using the AUC as the optimization objective, thus avoiding optimistic bias in the performance estimation [47; 48].  
Models were trained on both the full feature set and on predefined feature subgroups to assess the independent and combined predictive value of different handwriting domains and task types.
# Internet of Things and Its Applications: A Comprehensive Survey
## 8 IoT and Next Generation Protocol  
The IPv6 suite primary protocol is neighbor discovery protocol (NDP), and is considered a replacement for the address resolution protocol (ARP) function in IPv4 [165]. The IPv6 protocol considers an extremely auspicious protocol for complicated and dispersed network applications in the era of IoT and Industry 4.0. However, its industrial implementation is slowly increasing in smart manufacturing methods [166]. As the number of devices in the network grows, the received data becomes complex and complicated, which requires more efficient approaches to be collected, sorted, and processed to achieve higher QoS values [167]. This has led researchers and developers to focus on designing various smart network protocols with self-organizing, self-management, and self-configure features, which can able full 3 GPP standards and establish an uninterrupted network [168].  
Moreover, the IoT6, which is the research project of the future IoT, is progressing positively, yet the unification of IPv6 and IoT is struggling with some challenges. The aim is to exploit the potential of IPv6 and related standards to overcome current shortcomings and fragmentation of the IoT [169]. Currently, the prime issue is the need to integrate the IPv6 and corresponding protocol with IoT, which can help to offer various applications such as automation, smart homes, and smart cities. However, due to wish to design an efficient protocol, some of the significant issues, such as the integration, complexity, scalability, security, reliability, flexibility, and homogeneity, need to be investigated for more IoT applications.