esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Organoleptic Quality of Chicken Meatballs
The results of our study suggest that the application of various sage preparations to chicken meatballs prepared using MSM from chickens did not cause any deterioration in the organoleptic attributes of the products such as external appearance, color, and aroma, which would result in their lack of acceptance. However, to obtain a product with fully satisfactory organoleptic properties, further research should be performed assessing the taste of the product and determining the appropriate amount of the preparation. The results of other studies [22] show that sage can modify the palatability of meat products. [PERSON] et al. [22] analyzed the influence of various sage preparations on the quality of fresh pork sausage. These authors found that the addition of both sage essential oil and sage extract increased the scores given for the odor and flavor of sausages when the assessment was carried out on the day of production. Based on their study results, the authors reported that the acceptance of \"new\", \"characteristic\" sensory properties resulting from the use of plant preparations should be treated as a factor determining consumer preferences for meat products. This means that individual perceptions of consumers regarding sensory quality may vary. Previous studies have indicated that the sensory characteristics of chicken meatballs may be influenced by preparations obtained not only from sage but also from other plants, such as rosmenary [58]. Similar to our study, it was shown that the appearance and color of meatballs prepared with the addition of dried rosmenary and with ethanol extracts were comparable to that of the control product; however, meatballs containing dried spice were scored the best for aroma and taste. The results of other studies suggest that the overall sensory desirability of poultry meatballs can be improved by adding black cumin to the meat batter [43]. Although lower scores were given for the overall desirability of meatballs over time, based on all sensory results, the authors concluded that black cumin does not significantly deteriorate the sensory attractiveness of these products to consumers. The results of the organoleptic evaluation of chicken meatballs showed that during storage the scores given for appearance and color significantly decreased (\(p<0.05\)) for SEE40, SEE70, and SEO products, while for the SD product the scores for aroma decreased. At this stage of the research, due to the relatively high scores given for each of the organoleptic attributes, the use of any sage preparation should not be excluded in the production of chicken meat products such as meatballs.
# GNSS-Assisted Low-Cost Vision-Based Observation System for Deformation Monitoring
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline
**Floor** & **Point** & **Semi-Major Axis m\({}_{\text{X}}\) (m)** & **Semi-Minor Axis m\({}_{\text{Y}}\) (m)** & **Elevation m\({}_{\text{Z}}\) (m)** \\ \hline \multirow{9}{*}{Ground floor} & 10 & 0.0010 & 0.0010 & 0.0010 \\  & 11 & 0.0010 & 0.0010 & 0.0010 \\  & 12 & 0.0010 & 0.0010 & 0.0010 \\  & 1000 & 0.0030 & 0.0024 & 0.0031 \\  & 1001 & 0.0029 & 0.0017 & 0.0019 \\  & 1002 & 0.0039 & 0.0039 & 0.0044 \\  & 1003 & 0.0041 & 0.0037 & 0.0046 \\  & M01 & 0.0035 & 0.0045 & 0.0060 \\  & M02 & 0.0029 & 0.0028 & 0.0033 \\  & 2000 & 0.0035 & 0.0030 & 0.0046 \\  & 2001 & 0.0040 & 0.0033 & 0.0042 \\  & 2002 & 0.0039 & 0.0037 & 0.0042 \\
1 st block & 2003 & 0.0040 & 0.0035 & 0.0042 \\  & M21 & 0.0030 & 0.0041 & 0.0050 \\  & M22 & 0.0034 & 0.0038 & 0.0049 \\  & 13 & 0.0010 & 0.0010 & 0.0010 \\  & 3000 & 0.0038 & 0.0033 & 0.0056 \\  & 3001 & 0.0046 & 0.0040 & 0.0053 \\
2 nd block & 3002 & 0.0040 & 0.0038 & 0.0044 \\  & 3003 & 0.0040 & 0.0033 & 0.0041 \\  & M31 & 0.0030 & 0.0028 & 0.0032 \\  & M32 & 0.0036 & 0.0041 & 0.0055 \\  & 4000 & 0.0041 & 0.0037 & 0.0065 \\  & 4001 & 0.0051 & 0.0045 & 0.0064  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Floor**} & \multicolumn{1}{c}{**Point**} & \multicolumn{1}{c}{**Semi-Major Axis m\({}_{\text{Y}}\) (m)**} & \multicolumn{1}{c}{**Semi-Minor Axis m\({}_{\text{Y}}\) (m)**} & \multicolumn{1}{c}{**Elevation m\({}_{\text{Z}}\) (m)**} \\ \hline  & 1000 & 0.0030 & 0.0023 & 0.0030  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Floor**} & \multicolumn{1}{c}{**Point**} & \multicolumn{1}{c}{**Semi-Major Axis \(\mathbf{m_{X}}\) (m)**} & \multicolumn{1}{c}{**Eperation**} & \multicolumn{1}{c}{**Point**} & \multicolumn{1}{c}{**Semi-Major Axis \(\mathbf{m_{Y}}\) (m)**} & \multicolumn{1}{c}{**Eperation**} \\ \hline \multirow{4}{*}{Top floor} & \multirow{2}{*}{**To**} & \multirow{2}{*}{**Axis \(\mathbf{m_{X}}\) (m)**} & \multirow{2}{*}{**Semi-Major Axis \(\mathbf{m_{Y}}\) (m)**} & \multirow{2}{*}{**Eperation**} & \multirow{2}{*}{**From**} & \multirow{2}{*}{**To**} & \multirow{2}{*}{**Axis \(\mathbf{m_{X}}\) (m)**} & \multirow{2}{*}{**Axis \(\mathbf{m_{Y}}\) (m)**} & \multirow{2}{*}{**Axis \(\mathbf{m_{Z}}\) (m)**} \\ \cline{1-1} \cline{5-10}  & & & & & & & & \\ \hline
10 &  
Appendix BFigure 17: The relative error ellipses of coordinate determination (**a**) with additional GNSS observations; (**b**) without additional GNSS observations.
## 4 Data Analysis Framework Application
### Irregular Waves and Data Processing
Comparing the features among three wave scenarios, specifically 2, 5, and 6, as outlined in Table 3, reveals not only existing disparities but also hints at additional features that may need to be incorporated into the dataset. Figure 14 indicates the scalograms for scenarios 2, 5, and 6. Evidently and as discussed in [29], the main differences exhibited are in the height of white pixels appearance. Figure 14b illustrates the linear relationship between features in scenarios 2 and 5 and 5 and 6, where closer spectral range pixels (normalized wavelet coefficient) show higher linear dependency. Figure 13: Indication of features variance for raw data (**a**) and projection on black plane (**b**). The bar chart in Figure 14c not only illustrates the variation in the undercurve area between different scenarios but also quantifies the bin where the highest intensity of the coefficient is observed. While the correlation between vectors may reflect certain aspects, it necessitates another metric to translate the correlation value into a meaningful feature. After test and trial, the undercurve area and the summation of a minimum feature value in each segment can be incorporated as additional features to the existing features in vector K\({}^{\prime}_{(2,b)}\), resulting in 10 attributes in vector form instead of 8, as depicted in Figure 12. To achieve higher resolution, these feature arrays must be appropriately scaled up. For instance, two additional features for resolution 8 need to be scaled up to eight arrays, with each array repeated four times. So, for vector C\({}_{ij}\) as the size of (1,10) to be the feature with the size of i = 1:8 for original attributes and j = 9:10 for two extra attributes with a max pooling size of 32, the extra features must be extended with factor of \(\alpha\), for example, \(\alpha=4\) for max pooling 8, so the resulting feature vector will be C\({}_{ij}\) with i = 1:32, and j = 33:40. Figure 14: (**a**) The scalograms of response to scenarios 2, 5, and 6 from left to right. (**b**) Linear relationship between features in scenarios 2 and 5 (**left**) and 5 and 6 (**right**). (**c**) Comparison of features between far (**left**) and close (**right**) frequencies. To explore whether additional attributes can potentially enhance feature variance, we analyzed the cumulative response data to the sphere over a 40-min period. For simplicity, the analysis was conducted using eight features. The ANOVA representation for additional features is shown in Figure 15, with feature 9 denoting the undercurve area and feature 10 assigned to the summation of minimum intensity value. These new features have significantly boosted the variance and uniqueness regarding the spectral contribution of each segment for classification, as they fall into two unique ranges. Consequently, the new setup has been employed to train the classification models presented in the next section.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 4 Case Study
### Step 1: Collect System Information
#### 4.1.2 Step 1.2: Collect Solar, Critical Load, and, Current Microgrid Data  
Critical load energy consumption data is obtained from the DOE using hourly load data referencing 70% of the buildings within the United States used for building energy studies [56]. Historical solar incidence data is collected from 1991 to 2010 for climate conditions in Monterey, California [57]. We specifically use solar data from the year 2000 for illustrative purposes. Load data analysis indicates that the highest amount of electricity use occurs from January to March while simultaneously having the worst PV power output. This worst case scenario of high power demand and low solar output power is the time of year used for the rest of this Case Study. This ensures that energy resilience is evaluated in the worst conditions when adversaries are more likely to attempt hostile actions. Table 1 shows the summary of the critical loads within the existing microgrid. For the purposes of the case study, these values are derived from only taking into consideration the critical electricity needed to maintain mission operations at a military base such as Naval Support Activity Monterey which is assumed to be 50% of interior lighting and 67% of interior equipment [32]. This eliminates the use of auxiliary services of water heaters, fans, and electric cooling and heating systems among others that provide comfort service rather than mission-critical needs [56]. This critical load assumption is then used to calculate the hourly load (kW). The hourly load data is compiled to during the selected time of year to determine the maximum and average critical load values for each type of facility.
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 3 Proposed Method  
The proposed approach uses the methodology represented by the flowchart in Figure 1, which consists of six stages: acquisition of the data (video), pre-processing of the images captured from the videos, creation of the dataset, training of the CNN architectures, testing of the trained models and subsequent prediction of driver drowsiness. Firstly, the acquisition of videos showing driver drowsiness is performed. After obtaining the data, pre-processing is conducted to extract the frames where the 468 facial points are detected by MediaPipe. For a better selection of the ROI, a methodology is proposed that uses 4 points around the eyes and--with the help of an intermediate point between the eyes--calculates the distances from the extreme point of the right eye to the extreme point of the left eye and the upper and lower extreme points of the right and left eyes, comparing them and thus selecting the most significant distance to create the ROI. This method guarantees the ROI of the eyes without losing information when the driver makes head movements looking up, down, right and left. This proposed method is described in detail in Section 3.2.3 ROI selection. After having the ROI selected, the frames are extracted to create the dataset. All the images of the dataset go through a processing that resizes them to an image of \(112\times 112\) pixels and then normalizes them by dividing each pixel by 255. At this stage, data augmentation is also applied to the training set in order to avoid overfitting. Then, the processed images are used for the training of the 3 CNN architectures generating their respective accuracy and loss graphs. To run and evaluate the performance of the networks, a test is performed with the set of test images resulting in the selection of the best performing model. Finally, with the selected model, the driver drowsiness prediction test is performed.
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Neural (Machine Learning) Methods
#### 5.5.5 Attention  
A recent technique called _attention_ in networks emphasizes specific portions of data and is flexible to exploit non-uniform distributions of data information. Attention is usually determined as a _correlation_ in data sequences using matrix operations that have _queries_ (inputs), _keys_ (features), and _values_ (desired outputs). Attention can be applied over time, frequency, and network layers [52, 53].  
Neural systems with attention are often termed _Transformers_. These recent _end-to-end_ (E2E) methods do not use recurrence and rely only on attention. In such recurrent networks (e.g., LSTM), input is processed sequentially. A Transformer, on the other hand, does not exploit data sequences, but instead uses temporal positional encodings in a separate embedding. Transformers typically require more computation than other ANNs. E2E modelling methods (taking data input directly without analysis, and using a unified, simple architecture) for SD have demonstrated superiority in handling overlapped speech compared to conventional pipelines based on speaker embedding clustering. However, E2E systems usually require much carefully annotated training data, which may require up to 10 min to manually describe each minute of training data.
# A Review: Using Multiple Templates for Molecular Imprinted Polymer: Is It Good?
## 2 Multi-Template Strategy
### Influencing Factors
#### 2.3.3 Crosslinker  
The crosslinker has the same function in ST-MIP and MT-MIP synthesis, namely binding the functional monomer complex and the template to form a rigid structure [29]. If the MIP formed has a rigid structure, the monomer structure around the template will not change even if the template is removed. Therefore, the use of a crosslinker greatly affects the stability of the MIP that is formed [77]. The amount of crosslinker usage should be carefully considered. Insufficient use of crosslinkers can lead to poor MIP stability and template leakage during synthesis [83]. Meanwhile, the use of too many crosslinkers results in impaired cavity banding between functional monomers and templates so that the sensitivity of the binding site is reduced [29; 84].  
In selecting the crosslinker used, there are several things that must be considered, namely the functional monomer, the type of bond between the functional monomer and templates, the type of polymerisation, and the solvent [6; 84; 85; 86]. The types of functional monomers and crosslinkers must have the same reactivity so that when random copolymerisation is carried out, the functional groups of monomers can be evenly distributed [6; 85]. The bond between functional monomers and templates is also a matter that must be considered because the improper selection of crosslinkers can disrupt the bonds that are formed [84; 86]. The type of polymerisation is also a consideration because the presence or absence of free radicals in the polymerisation process will affect the MIP formed [6; 85]. The crosslinkers commonly used for free radical polymerisation are EgDMA, divinylbenzene (DVB), and trimethylploprane-trimethacrylate (TRIM) for non-covalent bonds, and triallyl isocyanurate (TAIC), dicumyl peroxide (DCP), and bis-(1-_tert_-butylperoxy)-1-methylethyl)-benzene (BIPB) for covalent bonds [6; 87; 6]. In addition, the solvent must also be considered: the crosslinker used must be insoluble in the solvent used. Commonly used crosslinkers for inorganic solvents are \(N\),\(N\)-methylenebisacrylamide and the usual crosslinkers for organic solvents are DVB and EgDMA [6; 85].
## 4 Effect of the Injection Rate Shape
### Mixture Properties
The TKE can reflect the intensity of the turbulent motion in the cylinder, with a large TKE indicating that the agitation is more intense in the cylinder. Examination of the turbulence demonstrates that a high TKE flow provides a clear driving force for mixing and evolution. In addition, strong turbulent mixing leads to the highest chance of saturation, which results in better mixing opportunities. Therefore, the higher the initial injection rate, the greater the variability of the cutting force field in the spray area. This phenomenon is related to the fact that the shape of the injection rate has a significant impact on the potential of spray behavior and TKE production. The mixing efficiency is very potent when the TKE distribution has high potential due to a sufficiently large increase in rail pressure. The results of this simulation study show that high initial injections will produce high turbulence energy. The GIQD injection rate is highly efficient in atomization and creating good fuel-air mixing, thus resulting in good combustion. The duration of the combustion at each injection rate can be arranged, from long to short, as follows: GIQD, GIGD, RECT, and QIGD. Since the QIGD injection rate has the highest initial injection rate, combustion may start earlier, while the GIQD injection rate has the lowest initial injection rate, which will result in less atomization and poorer combustion. In addition, when analyzing the mixing behavior of sprays (which can affect emissions), CO emissions increase when the initial injection rate is low, which will shorten the flame lift-off length [6]. This means that the fuel-injection does not have enough time for good air entrainment before the start of combustion, resulting in higher CO emissions levels. A high initial injection will result in high NO because a high initial injection will lead to an earlier start of combustion and a higher peak heat-release. We expect that the QIGD injection rate will have the highest amount of heat released because the temperature contours and the QIGD injection rate case will provide the fastest mixing. This mixing will result in the shortest ignition delay duration with the highest observed heat-release. The strategy for creating high initial injection rates often results in NOx emissions and a higher engine noise level. In our numerical simulation on the influence of injection rates on spray behavior affecting the mixing and combustion processes, we chose the QIGD injection rate as the optimal rate for the injection strategy, which requires the injection time and combustion to be short, because this injection rate's mixing efficiency is greater than that of the other injection rate shapes. The RECT injection rate was used for reducing NOx and engine noise due to lower pressure in the combustion chamber. Future studies will be developed on the efficiency of injection strategies that can simultaneously reduce NOx emissions, engine noise levels, and soot emissions.
## 2 Materials and Methods  
A sampling of the JKB, JOF and JHC was performed based on observations of the diversity of deposited material based on grain size, the colour of deposited material, different mechanical properties and the spatial distribution of material on the flotation tailings. A total of 10 probes were collected from the entire tailings: three probes were taken from the crest of the dam (JKB), two probes were conducted on the perimeter of the dam (JHC) and five probes were taken from the tailings plateau (JOF), Figure 1. All samples were dried in an oven at 105 \({}^{\circ}\)C. For every sample, probes were mixed and homogenized, and one representative sample (marked as JKB, JOF and JHC) was made and used for investigations.  
XRD was performed by using an Ultima IV Rigaku diffractometer (XRPD, Rigaku Ultima IV, Tokyo, Japan), equipped with CuK\(\alpha_{1,2}\) radiations, using a generator voltage of 40.0 kV and a generator current of 40.0 mA. The range of 5-90\({}^{\circ}\) 20 was used for all powders in a continuous scan mode with a scanning step size of 0.02\({}^{\circ}\) and at a scan rate of 10\({}^{\circ}\)/min, using a D/TeX Ultra-high-speed detector. a glass sample carrier was used for sample preparation. The unit cell parameters, volume of the unit cell and fractions of the phases were calculated by using the software package Powder Cell [13; 14]. All the structural models for refinement, i.e., cif files of quartz, clinochlore, albite, biotite, pyrite and calcite were taken from the ICSD database [15].  
The chemical compositions of the samples were determined by the classical chemical analysis of the aluminosilicates [16]. The concentrations of heavy metals were determined using a Perkin Elmer Analyst 300 (Perkin Elmer, Waltham, MA, USA) instrument for atomic absorption spectrometry (AAS).  
Thermogravimetric (TGA), differential thermal analysis (DTA) and differential thermogravimetric analysis (DTG) of samples were performed on a Netzsch STA 409 EP (Netzsch, Selb, Germany). Samples were heated from 25 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C in an air atmosphere at the heating rate of 10 \({}^{\circ}\)C/min. Before analysis, samples were kept in a desiccator at a relative humidity of 23%.  
The morphology and microstructure of all samples were studied by field emission scanning electron microscopy (FESEM) with a TESCAN Mira3 XMU (Tescan, Kohoutovice, Czech Republic) at 20 kV. The samples were precoated with a 2-3-nanometre-thick layer of gold before observation. The images were recorded at a magnification of 10,000\(\times\) with an accelerating voltage of 20 kV.  
In order to determine which mineral is dominant in which fraction, JKB, JOF and JHC samples were sifted through different sieves. Fractions of tailings were separated with the following particle size: \(-63+0\)\(\upmu\)m, \(-100+63\)\(\upmu\)m, \(-200+100\)\(\upmu\)m, \(-300+200\)\(\upmu\)m, \(-400+300\)\(\upmu\)m and \(+400\)\(\upmu\)m, and all fractions were analyzed by the XRD method.
## 3 Results and Discussion
### Determination of the Limiting Current Density  
In order to preserve the efficiency of the electromembrane process and to prevent the dissociation of water molecules on LCM-solution interfaces, the imposed current density is required to be lower than the limiting current density (LCD). This LCD, which refers to the total depletion of the solute in the membrane adjacent layer, can be determined using current-voltage curves. Figure 2 presents the current density-voltage curve for 0.05 M NaCl + 0.05 M LiCl solution and LCM (A = 3.14 cm2) using Guillou's cell and a setup as described in [49]. The obtained current density-voltage curve does not follow the typical trend of classical monopolar IEMs due to the unconventional/unusual nature and composition of the used LCMs.  
The value of this LCD was found to be around 16.9 mA-cm2. Thus, we chose the tested values of the imposed current belo  
Figure 2: Current density–voltage curve for 0.05 M NaCl + 0.05 M LiCl solution and the LCM membrane.
## 4 Experiment and Security Analysis
### Embedding Capacity Analysis
We can see from the statistics that the most time-consuming step is to determine the optimal weights which takes several seconds, which greatly depends on the size of the processing image. The time spent in all other processes is fast, less than one second. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**No.** & **Source** & **EC\({}_{1}\)(SB)** & **EC\({}_{1}\) (WM)** & **EC\({}_{1}\)** & **ER\({}_{1}\)** & **EC\({}_{2}\)(WMRDH)** & **ER\({}_{2}\)** & **EC (Total)** & **ER (Total)** \\ \hline
1 & Baboon & 1,246,465 & 735,931 & 1,982,396 & 2.521 & 1,826,039 & 1.161 & 3,808,435 & 3.682 \\
2 & Lena & 1,246,465 & 735,931 & 1,982,396 & 2.521 & 1,826,039 & 1.161 & 3,808,435 & 3.682 \\
3 & Pepper & 1,246,465 & 735,931 & 1,982,396 & 2.521 & 1,826,039 & 1.161 & 3,808,435 & 3.682 \\
4 & Kodim07 & 1,869,697 & 1,103,896 & 2,973,993 & 2.521 & 2,739,058 & 1.161 & 5,712,651 & 3.682 \\
5 & Kodim20 & 1,869,697 & 1,103,896 & 2,973,593 & 2.521 & 2,739,058 & 1.161 & 5,712,651 & 3.682 \\
6 & Kodim23 & 1,869,697 & 1,103,896 & 2,973,593 & 2.521 & 2,739,058 & 1.161 & 5,712,651 & 3.682 \\ \hline \end{tabular}
\end{table} 
Table 7: The embedding capacities and embedding rates in Phase-1 (SB+WM) and Phase-2 (WMRDH).