esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, electrocardiogram signals were used as a biometric option to fill the security gaps left by both traditional identifying methods and other recently developed biometric technologies. The proposed method demonstrates the utility of wavelet and 1D-CNN-based features in comparison to fiducial features. The proposed methodology was tested on both publicly available datasets with healthy subjects as well as the dataset of ECG with arrhythmia for biometric recognition with promising results. The ECG is particularly useful in biometric identification applications because the heart is located inside the body and moves while a subject is alive. The signal frequency domain analysis has a large impact on noise. Because the ECG is sensitive to noise, various studies have used noise-resistant time-frequency transformations, with the proposed approach combining DWT, LSTM and 1D-CNN providing superior performance for ECG biometric classification. Various transfer learning models, including VGG-19, ResNet-152 and Inception version 3, were used to compare the performance of the proposed approach. The system is best served by the optimal choice of the Coif5 wavelet coefficient with level 4 decomposition, which was analyzed and compared, as was the performance of the different wavelet family functions used for extracting features, and the 1D-CNN + LSTM extracted features were concatenated with the Coif5 wavelet-based feature for evaluation. The combined features technique (DWT+ LSTM + 1D-CNN) gives a better result compared to the transfer learning approach. The evaluation of the combined features technique was performed on a large dataset. The proposed model can also be evaluated for other physiological signal-based biometric modalities and compared with other 1D-CNN models in future work.  
Data curation, M.S.A. and M.H.; Funding acquisition, M.H., H.E. and A.R.; Methodology, M.S.A., M.S.F., M.N.D., M.H., A.R. and M.A.; Project administration, M.H.; Software, M.S.A.; Supervision, M.N.D. and M.A.; Validation, M.S.F., M.N.D. and H.E.; Visualization, H.E. and A.R.; Writing--original draft, M.S.A. and M.N.D.; Writing--review and editing, M.S.F., H.E., A.R. and M.A. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R125), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** Not applicable  
**Acknowledgments:** The authors would like to thank Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R125), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Data and Methodology
### Inversion of Dispersion Curves and HVSR Curves  
The inversion was carried out with the Dinver software (Sesarray package [34]) using the neighborhood algorithm [51]. In order to have a better spatial distribution for the V\({}_{\text{s30}}\) values, some HVSR curves were inverted in El-Abadia (5 sites) and Ain-Defla (5 sites). Only the part around the fundamental frequency peak was considered in the inversion process [52]. For the array data inversion process, the 3 dispersion curves obtained for each array using the 3 different techniques (F-K, SPAC, and ESAC) (Figure 4) were averaged to obtain a better constrained dispersion curve with an optimized frequency range. The input parameters required for the inversion (Vp, Vs, densities, and number of layers) were taken from previous studies [14; 21]. The maximum number of iterations was set to 300 iterations, and 100 models were generated at each iteration. The experimental average dispersion curve was compared to the theoretical one via a misfit value. Then, the Vs model corresponding to the minimum misfit was selected (Figure 5).
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 3 Results and Discussion
### Prediction Model Establishment  
Test Preparation  
To evaluate the performance of the designed rapid-detection sensor, 100 rice grain samples with different moisture content were prepared according to the sample preparation method described in Section 2.1. Among them, the calibration set contained 70 samples, while the prediction set contained 30 samples. Figure 7 displays the detection process of rice grain moisture using the designed rapid-detection sensor. In this paper, polynomial fitting was applied to fit the mathematical relationship between the reflected voltage value and the true moisture content of rice grain samples. The relationship between sensor voltage and obtained rice grain moisture was established as shown in Figure 8, and the formula was as shown below.  
\[p(x)=-0.00005x^{2}+0.018x+26.689 \tag{3}\]  
Figure 6: The flowchart of the software program.  
Figure 7: The detection process for test the sensor performance.  
In this formula, \(x\) is the voltage in mv, y is the moisture content in %. It can be seen from the figure that the sensor voltage has a good correlation with rice moisture. To verify the stability and reliability of the model, the moisture prediction model was transplanted into the software system of detection sensor. 30 rice grain samples (moisture content: 13-30%) were used to test the predictive ability of the model. The rice grain samples were detected by the designed detection sensor and compared with the true moisture content. In addition, the linear fitting relationship between the predicted moisture content and the true moisture content was obtained and shown in Figure 9.  
As a result, by transplanting the prediction model of rice grain moisture and NIR reflection electrical signals into the system software program of the detection sensor, the designed sensor achieved good detection accuracy (\(R^{2}\) = 0.936, SSE = 23.44) with respect to rice grain samples with 13-30% moisture content. Furthermore, no pretreatment method was applied in this experiment, which thus meets the requirements of rapid and accurate detection. Thus, in order to expand the moisture detection gradient of rice grain and enhance the detection accuracy in further studies, the number of rice grain samples can be increased to obtain more spectral information and optimize the moisture prediction model.  
Figure 8: The relationship between sensor voltage and obtained rice moisture.  
Figure 9: The linear fitting result between the detected moisture content and the true content.
# Exploring the Probiotic Potential of Dairy Industrial-Relevant Lactobacillus
## 3 Results
### Immuno-Modulatory Effect of LAB CFS  
Cytotoxicity tests, using a range of CSF concentrations to treat macrophages, allowed us to determine safe amounts of CSF, which differed for each strain (Table 2).  
The maximal CSF percentages that corresponded to the highest cell viability (cell viability > 80%) were used to test the capacity of the lactobacillus to influence the expression of genes encoding TNF-\(\alpha\) and IL-8, i.e., two cytokines with pro-inflammatory and immunotherapy function. Quantitative RT-PCR was used to evaluate the transcriptional levels of TNF-\(\alpha\) and IL-8 genes in LPS-stimulated human macrophages. Pre-treatment of LPS-stimulated macrophages with 5% CFS (_v/v_) from \(L\). _plantarum_ PLA and PLA2, and 10% CFS from \(L\). _rhammosus_ RHM and PAR4 CFS led to a decreased relative expression of genes encoding both the pro-inflammatory cytokines, compared to non-treated LPS-stimulated cells (Figure 4). The anti-inflammatory effect of the tested CFS was particularly evidenced by the consistent repression of TNF-\(\alpha\) mRNA level, relative to that observed in LPS-stimulated control (Figure 4A).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Relative Cell Viability** & **15\% CSF** & **10\% CFS** & **5\% CFS** \\ \hline _L. plantarum_ PLA & \(71.0\pm 5.3\) & \(91.1\pm 23.2\) & \(101.1\pm 22.4\) \\ \hline _L. plantarum_ PLA2 & \(67.0\pm 20.1\) & \(73.9\pm 28.1\) & \(82.3\pm 19.1\) \\ \hline _L. rhamnus_ RHM & \(55.5\pm 14.9\) & \(92.5\pm 18.5\) & \(95.3\pm 23.3\) \\ \hline _L. rhamnus_ PAR4 & \(109.7\pm 23.9\) & \(106.8\pm 19.6\) & \(109.0\pm 10.2\) \\ \hline \end{tabular}
\end{table}
Table 2: Cytotoxicity studies. Toxic effects of different percentage (15%, 10%, 5% (_v/v_)) of CFS on viability of THP-1 macrophages. Relative cell viability (%) = [(OD\({}_{959}\) sample \(-\) OD\({}_{959}\) blank)/(OD\({}_{959}\) control \(-\) OD\({}_{959}\) blank)] \(\times\) 100. Mean and SD from at least 2 experiments performed in triplicates.  
Figure 3: Ability to produce biofilms on plastic surface. Cultures of the tested lactobacillus were inoculated in fresh MRS broth and incubated for five days at 30 \({}^{\circ}\)C. Optical density was measured at 570 nm to quantify the plastic-adhering biofilm. Statistical analyses were carried out by ANOVA test (_p_ < 0.05), followed by Fisher’s Least Significant Difference (LSD) test. Values represent mean \(\pm\) SD of 3 different experiments.  
Indeed, the transcriptional induction of TNF-\(\alpha\) gene was from 51- to 209-fold lower in LPS-stimulated macrophages with CSF-pre-treatment, compared to LPS-stimulated control cells. The same effect was observed for IL-8 gene transcriptional level, which was induced by the pro-inflammatory stimulation with a 2.6- to 5.4-fold reduction in CSF pre-treated macrophages relative to control.
## 2 Background  
The human brain, with its remarkable plasticity, possesses the extraordinary ability to rewire itself and forge new neural connections even after injury or disease. This ability, called neuroplasticity, makes BCIs a promising tool for brain rehabilitation [14; 15; 16; 17; 18; 19; 20].  
There is a kind of teamwork between BCIs and machine learning that could change the method of brain rehabilitation for people with stroke, spinal cord injuries, brain injuries, and other neurological conditions. It could help people regain lost skills and improve their quality of life. That is why scientists are keen on exploring new things in this technology for the following reasons:  
1. Unlocking the potential of brain-computer interfaces (BCIs) for brain rehabilitation goes hand in hand with the remarkable power of machine learning. These intricate algorithms act as digital guides, navigating the complex landscape of brain signals to reveal the hidden pathways to recovery. By learning to recognize the unique patterns within each person's neural code, machine learning techniques like support vector machines, deep neural networks, and random forests become adept at deciphering intentions and translating them into tangible actions. This remarkable synergy opens a world of possibilities for individualized rehabilitation, allowing us to harness the power of our own minds to regain control and rebuild skills [21; 22; 23; 24].
2. BCIs are able to adapt to variations in a user's brain impulses over time because of machine learning. Increasing accuracy and resilience, adaptive models continuously modify their parameters in response to fresh data [20; 25; 26; 27].
3. Tailored rehabilitation interventions are made possible by machine learning. Rehabilitative techniques can be adjusted as necessary by training models to identify each user's unique brain patterns [7; 11; 19; 28; 29; 30].
4. Instant feedback: Machine learning can process brain signals very quickly, so BCIs can react in real time. This is like having a virtual coach that gives you feedback right away, helping you learn faster [14; 17].
5. Predicting success: Machine learning can look at brain activity and predict how well someone will do in rehab. This helps doctors make better treatment plans and obtain better results [20].
6. Seeing the whole picture: Machine learning can combine different types of brain data, like EEGs and fMRIs, to obtain a more complete understanding of how the brain is working during rehab [17; 20].
# The Novel Role of Solvents in Non-Surgical Endodontic Retreatment
## 2 Evolution of Endodontic Solvent Compounds  
Traditional gutta-percha solvents are chemical substances, usually organic, whose primary objective is the dissolution or softening of filling materials (particularly gutta-percha). Studies on the advantages of their use are not consensual [15]. Some authors have stated that solvents should only be used when the working length is hard to reach [16]. Eventual disadvantages have also been reported, such as slowing of the retreatment process due to a higher accumulation of filling material remnants [16; 17]. On the other hand, the isolated use of mechanical instruments has been associated with several problems, including perforating roots and straightening canals, preventing their original shape from being preserved [3].  
Chloroform is one of the most popular gold-standard gutta-percha solvents, with a long history in endodontics. Although it is recognized as being one of the most effective for both gutta-percha and sealers [18; 19], its use has been questioned due to its cytotoxicity and carcinogenic potential [20; 21; 22]. Although, in general, the cytotoxicity of gutta-percha solvents depends on their exposure time and dose, chloroform also has a considerable storage risk, as it is highly flammable. In turn, halothane, also associated with a high level of toxicity, has been discontinued [23]. Other solvents such as xylene and eucalypt, which have been proposed as being alternatives to chloroform, although quite effective [24; 25], have been shown to address similar concerns, namely regarding biocompatibility [20; 26; 27]. TCE was reported as having a strong dissolution effect, particularly over gutta-percha [28]. Although it has been pointed out as also promoting the dissolution of endodontic sealers, it was clearly less effective than chloroform [12; 29]. Essential oils, such as Ooil, which have recently been stressed as \"green compounds\", were considered as being quite safer but less effective, particularly for sealer dissolution [24; 27; 28].
## 5 Solution Scattering
### Multidomain Proteins  
Single-domain proteins such as myoglobin, albumin, and lysozyme consist of just one polypeptide chain. A multidomain protein is made of multiple independent structural units. Hemoglobin, which transports oxygen in the blood, is an example of a multidomain protein containing four globin units. Other examples include laminin, essential for the formation and functioning of muscle, nerve, kidney, lung, skin, vasculature, and other tissues, pyruvate kinase, RNA-binding proteins, and proteins, such as fibronectin, involved in blood clotting and cell adhesion. We use a multidomain protein, monoclonal antibody (mAb), to illustrate the application of SAXS for this class of proteins. mAbs bind specifically to antigens on the surface of target cells, enabling precise immune responses or direct cell destruction. A detailed study is useful in engineering molecules to mimic or enhance the immune system's ability to target and destroy harmful cells, such as cancer cells. Small-angle neutron scattering (SANS) using contrast-matching techniques with deuterated solvents has been used to study the subdomains, fragment antigen binding (Fab), and crystallizable fragment (F\({}_{\text{c}}\)) (Figure 10d). These curves are used to generate PDF plots (Figure 10e). The figure shows the SAXS data from three of the four human IgG subclasses, namely IgG1, IgG2, IgG3, and IgG4 [124]. The \(R_{g}\) for the three are 49.4 (IG1) and 47.6 A (IG2 and IG4). The differences in \(R_{g}\) are reflected in the PDFs. Unlike in IDPS, the PDFs are most useful in understanding the small differences in the raw SAXS data.  
SAXS is also used to investigate protein-protein interactions in concentrated mAb solutions that result in self-association, reversible dimer formation, aggregation and appearance of larger clusters that are correlated with viscosity [127; 128; 129]. Insights into colloidal stability obtained by integrating light scattering and coarse-grained modeling with SAXS to study aggregation can lead to formulation of higher-concentration and thermally stable mAb solutions [130]. [PERSON] et al. analyzed the crystalline mAb suspensions under various conditions to optimize batch processes for vaccination [131]. In contrast, SAXS gave structural features (\(R_{g}\) and aggregation). SAXS gave insight into colloidal stability and aggregation, whilst SANS gave insight into molecular and domain-level dynamics [132].
## 3 Results
### Identification of Deer Antler-Fermenting LAB  
Morphological and molecular identification of LAB with antler-fermenting activity revealed that the colonies had an appearance typical of LAB. Microscopic evaluation confirmed that culture HY7602 formed disc-shaped colonies, and that individual cells were rod shaped (Figure 1a,b). These results showed that the cultured bacteria belonged to the genus _Lactobacillus_. Four types of primer were used for accurate species classification. The gene sequences of the cultured _Lactobacillus_ spp. strains were more than 99% identical to those of _Lactobacillus curvatus_ strains in the NCBI database, a finding confirmed by phylogenetic analysis (Figure 1c). Based on these results, the deer antler-fermenting LAB isolate was named _Lactobacillus curvatus_ HY7602.  
Effects of FA on Expression of Genes Related to Muscle Strength and Mitochondrial Energy Metabolism in C2C12 Cells  
Expression of _Igf-1_, _Pi3k_, _Akt_, _Mtor_, and _P7086k_ genes was evaluated by RT-PCR. The results showed that expression of _Igf-1_, _Pi3k_, _Akt_, _Mtor_, and _P7086k_ in cells treated with FA was greater than that in the no treatment (NT) control, creatine, or NFA groups (Figure 2).  
In addition, expression of _Ampk_, _Sirt1_, and _Ppargc1a_ in the FA-treated group was higher than that in the NT-, creatine-, and NFA-treated groups (Figure 3).  
Figure 1: Morphological characteristics and phylogenetic tree depicting the taxonomic position of _Lactobacillus curvatus_ strains based on 168 rRNA sequences. The lactic acid bacteria HY7602 were inoculated onto MRS plates, incubated at 37 \({}^{\circ}\)C for 36 h, and examined under a microscope. (**a**) HY7602 colonies. (**b**) HY7602 cells (4000X). (**c**) The 16S rRNA gene sequence of HY7602 was used to identify its respective phylogenetic position. A phylogenetic tree was constructed using a neighbor-joining method. The “T” after the collection number indicates the strain type of the species.  
Figure 2: Effects of fermented antler (FA) on expression of IGF-1/AKT pathway-related genes (_lgf-1_, _Pi3k_, _Akt_, _Mtor_, _P706k_) in mouse skeletal muscle myotubes. FA induced expression of genes related to the IGF-1/AKT/mTOR pathway. To examine expression of _lgf-1_, _Pi3k_, _Akt_, _Mtor_, and _P706k_, total RNA was prepared from C2C12 cells incubated for 24 h with NFA (1 mg/mL), HY7602-FA (1 mg/mL), or creatine (1 mg/mL). _lgf-1_, _Pi3k_, _Akt_, _Mtor_, and _P706k_ were detected by using the RT-PCR program. Data are expressed as the mean \(\pm\) SD of three determinations. _Asterisks_ indicate significant differences: *_p_ < 0.05 and **_p_ < 0.01 vs. NT. _Akt_, protein kinase B; _lgf-1_, insulin-like growth factor 1; _Mtor_, mammalian target of rapamycin; NT, no treatment; _P706k_, phosphorylated 70-kDa ribosomal S6 kinase; _Pi3k_, phosphoinositide 3-kinases.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Specific Characteristics of Sensor Networks
#### 2.5.1 Sensor-Node Features  
Individually, sensor nodes have their own characteristics that usual sensors do not have. Indeed, sensor nodes have energy autonomy, and this resource is essential to the survival of a WSN because, in most cases, the sensor's battery is irreplaceable. In addition, sensors have limited capacity, such as with regard to storage and data processing, as well as narrow sensor transmission range. Here, we detail the main features. More information is available in the literature [22].  
* _Energy consumption._ Sensors have energy autonomy, and they usually use tiny batteries as energy resources. In most cases, WSNs are deployed in hard-to-reach areas. This makes it difficult or almost impossible to recharge or replace the batteries. This difficulty leads us to deduce that the life of a sensor is essentially dependent on the life of the battery. Therefore, energy-consumption management is a major constraint in this type of network.
* _Transmission scope._ This is an important criterion for the deployment of a WSN. It is limited by the radiation capacity of the antennas used and the signal strength involved. For example, the communication between two sensor nodes can only take place if the distance between them is not too great, i.e., a few tens of meters in practice. The longer the distance is, the higher the energy cost. This is especially true if there are physical obstacles between the two nodes that may prevent communication.
* _Storage and processing power._ An intrinsic characteristic of these sensors is their low storage capacity. Although they also have a processor, the sensors cannot perform very large operations due to their relatively low processing power. For example, \"mote\"-type sensor nodes are composed of an 8-bit 4 MHz microcontroller, 40 KB of memory, and a radio with a bit rate of about 10 Kbps. This remains true even for midrange nodes, such as \"UCLA/ROCKWELL'S WINS\", which have a strong ARM 1100 processor with 1 MB flash memory, 128 KB of RAM, and a 100 Kbps radio.