esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Lipid Hydrolysis Development  
In this study, the inclusion of alga flour extract in the gelatin film resulted in an inhibitory effect on the FFA content. FFA formation in fish muscle during refrigerated storage is attributed to both endogenous and microbial enzyme activities [3; 5; 75]. Initially, before the microbial lag phase ends, FFA formation is primarily due to endogenous enzyme activity (e.g., lipases and phospholipipes). Later, microbial extracellular lipases become the dominant mechanism for FFA generation. Given the significant increase in FFA formation observed during extended storage periods (6-9 days), microbial activity appears to be the main contributor to FFA formation. Therefore, the inhibition of FFA formation in the AF-1 and AF-2 batches at the end of the experiment can be attributed to the inhibition of microbial growth. As previously discussed in Section 4.1, the presence of antimicrobial compounds obtained from the aqueous extraction of _Gelidium_ flour in the packaging system led to reduced lipid hydrolysis. This effect can be explained by the creation of less favorable conditions for the interaction between microbial enzymes and lipids in fish muscle [3; 5].  
The determination of FFA values is of great interest because of their significant quality implications. The accumulation of FFAs can lead to detrimental sensory properties and negatively impact consumer acceptability of seafood [3; 4]. These negative changes include texture alterations and the development of off-odors and off-tastes. Additionally, FFA formation has a pronounced effect on lipid oxidation, as FFAs have lower oxidative stability than their corresponding TAGs and PLs do because of reduced steric hindrance to oxidative reactions [76; 77].  
In the present study, the effect of the addition of _Gelidium_ flour powder to a heated fish muscle system on lipid hydrolysis development was investigated [39]. As a result, a preservative effect on the FFA content was inferred, and this effect increased as the flour concentration increased. When red macroalgae and seaweed are considered in general, a wide variety of different and contradictory results have been reported regarding their effects on FFA development. In agreement with the results of the present study, the addition of lyophilized _F. spiralis_ to a gelatin film inhibited FFA formation in refrigerated mackerel (_S. scombrus_) [41]. However, a previous treatment with an aqueous extract from _Polysiphonia_ _fucoides_ led to enhanced lipid hydrolysis development in chilled minced Atlantic mackerel (_S. scombrus_) [78]. A similar increasing effect was observed in chilled hake (_M. merlucius_) by including a water extract of _F. spiralis_ in the icing medium [79]. In contrast, no effect on FFA formation was detected in chilled megrim (_L. wiffiagonis_) [80] or hake (_M. merlucius_) [79] when an ethanol extract of alga _F. spiralis_ powder was included in the icing medium. Similarly, the inclusion of _G. gracilis_ extracts in the icing medium did not lead to a definite effect on FFA formation during refrigerated storage of hake (_M. merlucius_) [64].
# 5G-AKA-FS: A 5G Authentication and Key Agreement Protocol for Forward Secrecy
## 4 A 5G-AKA Protocol for Forward Secrecy
### The Challenge-Response Phase: Step 2
#### 4.2.1 Step 2.1 (HN)  
**Step 2.1 (HN)**  
_Inputs._ Using \(\mathcal{SQN}_{HN}\), \(k\), and \(DHK\), HN generates an Authentication Vector \(SE\)-\(AV=(RAND,AUTN,HXRES^{*})\) as follows:  
_The Protocol:_  
1. Generate an ephemeral private-public key pair \((y,Y)\) such that \(Y=y\cdot G\)
2. Compute a Diffie-Hellman key \(DHK=y\cdot C_{0}\)
3. Set \(RAND\gets Y\) as a challenge (The 128-bit randomness of \(RAND\) is guaranteed since \(y\) is randomly chosen from the 128-bit key space. )
4. Compute \(MAC\leftarrow\mathsf{f}_{1}(k,SQN_{HN},AMR,RAND\oplus DHK)\) and an anonymous key \(AK\leftarrow\mathsf{f}_{5}(k,RAND\oplus DHK)\)
5. Set \(AUTN\leftarrow(AK\oplus SQN_{HN},AMR,MAC)\)
6. Compute \(CK\leftarrow\mathsf{f}_{3}(k,RAND\oplus DHK)\) and \(IK\leftarrow\mathsf{f}_{4}(k,RAND\oplus DHK)\)
7. Compute expected responses \(XRES\leftarrow\mathsf{f}_{2}(k,RAND\oplus DHK)\), \(XRES^{*}\leftarrow\mathsf{KDF}(CK||IK,ID_{SN}||RAND||XRES)\), and \(HXRES^{*}\leftarrow\mathsf{LEFT}(128,\mathsf{HS}_{\mathsf{HA}-256}(RAND||XRES^{*}))\)
8. Derive \(K_{AUSF}\leftarrow\mathsf{KDF}(CK||IK,ID_{SN}||AK\oplus SQN_{HN}||DHK)\) and \(K_{SEAF}\leftarrow\mathsf{KDF}(K_{AUSF},ID_{SN})\)
9. Increase \(SQN_{HN}\) by 1 (i.e., \(SQN_{HN}\gets SQN_{HN}+1\))
10. Set \(HE\)-\(AV\leftarrow(RAND,AUTN,XRES^{*},K_{AUSF})\) and \(SE\)-\(AV\leftarrow(RAND,AUTN,HXRES^{*})\)  
_Outputs._ HN sends \((SE\)-\(AV)\) to SN.
# 108 m Underwater Wireless Optical Communication Using a 490 nm Blue VECSEL and an AOM
## 5 Conclusions  
In conclusion, we demonstrated a 108 m UWOC system utilizing a 490 nm blue VECSEL and an AOM. In this system, the blue VECSEL can provide high-quality laser beams underwater, and the AOM can support the higher optical power of the transmitter. By combining 64 PPM, the anti-interference ability of the UWOC system was improved. Therefore, even using an ordinary APD as the detector, we still achieved a transmission distance of over 100 m and a good BER of 2.7 \(\times\) 10\({}^{-5}\) for the communication at the signal time-slot frequency of 50 MHz. This type of UWOC system still presents considerable opportunities to improve communication distances, and acts as a good model for future long-distance UWOC.  
Figure 11: Laser beam size at 24 m (**a**), 48 m (**b**) and 108 m (**c**). Signal eye diagram at 24 m (**d**), 48 m (**e**) and 108 m (**f**).  
**Author Contributions:** [PERSON] wrote the manuscript; [PERSON] and [PERSON] performed the experiment; [PERSON] and [PERSON] contributed significantly to analysis and manuscript preparation; [PERSON], [PERSON] and [PERSON] helped with the analysis; [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Cooperation Project between Chongqing local universities and the institutions of the Chinese Academy of Sciences, grant number HZ2021007; the Science and Technology Research Program of Chongqing Municipal Education Commission, grant numbers KJZD-M201900502, KJQN202200557, and KJQN202300525; the National Natural Science Foundation of China, grant numbers 61975003, 61790584, and 6202506; and the Chongqing Normal University (Talent Introduction/Doctoral Initiation) Fund Project, grant number 23 XLB003.  
**Institutional Review Board Statement:** This study did not require ethical approval.  
**Informed Consent Statement:** This study did not involve humans.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication. [PERSON] would like to declare on behalf of co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All authors listed have approved the manuscript that is enclosed.
## 2 Related Work
### Adaptive Thresholding  
An adaptive method usually assigns different threshold values to different regions or pixels based on local pixel statistics, which is useful for extracting small and sparse regions. Common methods include local-statistics-based, local-variance-based, and local-mean-based methods, but they require more computational resources. Classical methods include the Niblack method [46], which calculates the local threshold using the pixel mean and variance in a neighborhood. This method has good robustness against noise, but the choice of processing template window size has a significant impact on the results. The Sauvola method [47] uses the local grayscale mean of the template window as a reference, divides the image into different categories based on the standard deviation, and calculates multiple thresholds based on image categories. These methods adaptively select thresholds based on local image characteristics, leading to more accurate segmentation results. [PERSON] [48] proposed an algorithm for local adaptive thresholds that maintains segmentation result continuity. [PERSON] [49] introduced a local method based on intuitionistic fuzzy set theory, achieving good segmentation results in low-contrast samples. [PERSON] [50] introduced a Differential Evolution-based algorithm that balances local and global search for improved efficiency. Recently, [PERSON] [51] employed a grid search method to segment Regions of Interest (ROIs) in ultrasound medical images, improving segmentation results by calculating overlapping regions between automatically and manually extracted ROIs. [PERSON] [52] developed an adaptive thresholding technique with boundary curvature and tortuosity control. [PERSON] [53] introduced a center point selection method to address foreground deviation and used continuity detection to improve segmentation accuracy and the method's capacity for generalization. [PERSON] [54] proposed an enhanced version of the African Vulture Optimization Algorithm (AVOA), which integrates three binarization thresholding algorithms--Kapur entropy, Tsallis entropy, and the Otsu method--for multi-threshold image segmentation. [PERSON] [55] combined the Arithmetic Optimization Algorithm (AOA) with the Harris Hawks Optimizer (HHO) to improve the AOA's exploitation phase, thereby achieving an optimal threshold vector for multilevel thresholding image segmentation.
## 2 Experimental Methodology
### Laboratory-Scale Linear Cutting Machine and Cutting Tool  
Figure 3 shows the laboratory-scale LCM employed in this experiment. This LCM operates electronically and includes a rock box that can hold the rock specimens. The rock box is positioned using two servo motors. The x-axis servo motor moves the rock box to adjust the line spacing with a stroke of 500 mm, while the y-axis servo motor shifts the rock box to execute the cutting operation with a stroke of 700 mm. In addition, the z-axis servo motor moves the saddle vertically to adjust the cutting depth. Tool forces acting on the conical pick during cutting are measured by three load cells installed along three orthogonal axes. The x-axis load cell measures the side force (\(FS\)), while the y-axis and z-axis load cells measure the cutting force (\(FC\)) and normal force (\(FN\)), respectively, with data collected at a rate of 10 Hz. Cutting parameters, including cutting depth and line spacing, are set using the control box. Tool forces are displayed in real time on a monitor and concurrently recorded in CSV format.  
In the experiment, the cutting tool employed was the conical pick manufactured by Kennametal Inc. (Pittsburgh, PA, USA), model SM06. This pick has a tip angle of 90\({}^{\circ}\), and the pick gauge and shank lengths are 45.3 mm and 41.9 mm, respectively. The detailed specifications of the conical pick are shown in Figure 4a. The attack angle was consistently set at 45\({}^{\circ}\) for all tests, as shown in Figure 4b, with both the skew and tilt angles set at 0\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \(\rho\) **(g/cm\({}^{3}\))** & \(E\) **(GPa)** & \(\
u\) & \(\sigma_{c}\) **(MPa)** & \(\sigma_{t}\) **(MPa)** \\ \hline Finike limestone & 2.22 & 21.1 & 0.13 & 49.0 & 5.00 \\ Concrete & 2.38 & 38.9 & 0.30 & 42.0 & 2.51 \\ Cement mortar & 2.11 & 8.7 & 0.20 & 6.0 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of rock specimen materials.  
Figure 2: Schematic diagram of jointed rock mass specimen: (**a**) cross-sectional view; (**b**) full view.
## 2 The Algorithm
### Expansion  
#### 2.2.1 Homogeneous Direct Spatial Expansion  
When the initial sparse seed points are extracted, they will be expanded simultaneously with a predefined small step \(s\) to nearby 3D points along two perpendicular directions, as illustrated in Figure 2. Each seed, taking the red point \(p\) in Figure 2a as an example, is expanded directly in 3D space along its tangent plane to four (black) points for later optimization. After the first iteration of expansion, the newly expanded (black) points are used as new seed points and expanded in a similar way to nearby (green) points in Figure 2b. This process repeats until the density of the 3D points is sufficient. To avoid overlap, any point that appears close to an existing point will be deleted either before or after optimization, which ensures that the final points are distributed evenly and with proper density.  
The initial normal of a newly-expanded point is set the same as that of the seed point, whereas its initial world coordinate is calculated as follows. As shown in Figure 2a, first a square with a side length of 2s is drawn on the XY plane of the world coordinate system, with the centers of the four sides (blue points) placed symmetrically on the \(X\)- and \(Y\)-axis, respectively. The world coordinates of the four blue points are denoted as \(B\). Next, the square is moved until its center coincides with point \(p\) and its normal (\(Z\)-axis positive direction) coincides with the normal of point \(p\). The corresponding rotation matrix \(R\) can then be computed using the method in [30]. Then the new positions of the centers of the four sides (now marked black) of the square represent the initial positions of the four expanding points. Their world coordinates \(E\) can be computed by  
\[E=\left[\begin{array}{cccc}1&0&0&p_{x}\\ 0&1&0&p_{y}\\ 0&0&1&p_{z}\end{array}\right]\times\left[\mathbb{R}\times B\right] \tag{4}\]  
where \(p_{x}\), \(p_{y}\), \(p_{z}\) are the world coordinates of point \(p\).
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 4 Diagnostic Approaches of Foodborne Pathogens
### Methods Based on Nucleic Acids
#### 4.2.4 Mass Spectrometry Technology  
An unbiased and accurate method to detect pathogenic bacteria is mass spectrometry (MS). Since the advent of mass spectrometry, this method has steadily evolved into a new way to detect various types of pathogenic bacteria [22; 51; 102; 183; 184]. A non-biochemical instrumental analytical technique used to examine the characteristics of bacteria or a portion of their proteins as the subject matter of research, MS refers to the technique of analyzing the ions produced after ionization in order to identify and detect the target bacteria. A new method has been developed by [PERSON] et al. for detecting antimicrobial resistance in bacteria. For relevant foodborne pathogens such as _Campylobacter coli_ and _Campylobacter jejuni_, a combination of protein mass spectra from matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and a prediction method was developed [185]. Furthermore, [PERSON] et al. [186] have developed a unique method for MALDI-TOF MS simultaneously detecting numerous bacteria by using mass tags induced surface engineering in conjunction with a MALDI-TOF MS procedure in the same study.  
As a result of this method, mass tags are no longer only used to identify pathogens, but can also be used to identify multiple bacteria at once without the need to collect microbial mass spectrum libraries before analysis. According to [PERSON] et al. [187], they examinedthe antimicrobial properties of three essential oils and their necessary ingredients against foodborne pathogens and spoiled food. They indicated that the gas chromatography-mass spectrometry and gas chromatography-flame ionization detector methods were used. MS is currently the most effective detection method for foodborne pathogenic bacteria, providing quick detection accuracy and ease of operation, but there have been many issues with the actual identification process [188]. It is necessary to continuously troubleshoot spray voltage, flow rate, capillary temperature, and other difficulties during the detection process as part of the process to increase the sensitivity and reliability of foodborne pathogenic bacteria detection technology. MALDI-TOF MS has several limitations, such as an inability to distinguish between related species due to their inherent similarity. In addition, misidentification may occur when some members of a species complex are listed in the database, but others are not [189].
## 6 Case Study  
The IEEE 33-bus test system [32] is used to validate the performance of the proposed method (as shown in Figure 5). The test system is composed of 32 branch lines and 5 tie lines, and each line has a switch as well. The system voltage is 12.66 kV, the maximum capacity is 10 MVA, and the maximum load is 5084.26 + \(j\)2547.32 kVA. In order to fit the testing of the proposed method, three MGs, named as _MG1_, _MG2_ and _MG3_, are respectively connected to node 15, 31 and 28, which are tied by two separated DC-links. In the three MGs, the DERs include microturbines, PVs and WTs, which provide electricity services for residential, industrial and commercial users.  
The capacity of DERs are shown in Table 1 and the power limit on DC-link converters are set as 200 kVA. The parameters of the GA are: \(\rho_{m}=0.001\), \(\rho_{c}=0.6\), generation = 20, population = 50. As shown in Figure 6, 24 h of actual data of PV, WT and load in a day are collected to test the method. For the sake of tractability, the original scenario set is adequately trimmed down to 5 scenarios via scenario-reduction techniques [33].  
Figure 4: Example of operation cost of distributed energy resources (DERs) in an MG.  
Figure 5: IEEE 33-node test system containing multi-microgrid connected via DC-link.
## 3 Results and Discussion  
### Evaluation of Saffron Volatile Compounds  
Chromatograms at extraction temperatures 45, 50 and 60 \({}^{\circ}\)C are presented at Figure 1. The percentage area of safranal depending on the SPME extraction conditions was very high and ranged from 92.0 to 96.2% (Table 1), while the content of safranal was 17.4 \(\pm\) 25.6 mg:g\({}^{-1}\). Literature reports suggest that the extraction method and the geographical origin of safron may have a high impact on safranal content. [PERSON] et al. (2016) reported an average content of safranal at 69.3% of the total volatile fraction in Italian safron samples from Cascia, extracted by SPME. [PERSON] et al. (2012) used the same technique and reported a safranal percentage between 41.13 and 83.97% in Italian samples from Salerno and Sardinia. The significant differences in safranal content observed among these literature reports and the current study can be attributed to the different geographical origin of the samples as well as in the differences of the conditions applied for the SPME extraction.  
[PERSON] et al. (2012) applied a micro simultaneous hydro distillation-extraction method in stigmas of _Crocus sativus_ L. of Greek origin dried by the traditional method and reported a relative percentage of safranal between 50.0 and 70.8%, while after ultrasound-assisted  
Figure 1: Chromatograms at extraction temperatures 45, 50 and 60 \({}^{\circ}\)C. (1) hexanal; (2) 3,5,5-trimethylcyclohex-3-en-1-one; (3) 2,6,6-trimethylcyclohex-1,4-diene-1-carbaldehyde; (4) 3,5,5-trimethylcyclohex-2-en-1-one (isophorone); (5) 2,6,6-trimethylcyclohex-2-ene-1,4-dione (4-ketoisophorone); (6) 2,6,6-trimethylcyclohex-1,3-diene-1-carbaldehyde (safranal); (7) 3,5,5-trimethyl4-methylencyclohex-2-en-1-one; (8) 7,7-dimethylbicyclo[4.1.0]hept-3-ene-2,5-dione; (9) megastigma-4,6,(E),8(E)-triene; (10) 4-(2,6,6-trimethylcyclohex-1-en-1-yl)butan-2-one; (11) (E)-4-(2,6,6-trimethylcyclohex-1-yl)but-3-en-2-one (\(\beta\)-ionone).  
extraction, the content of safranal ranged between 8.7 and 41.2%. Safranal content in Iranian safron samples after ultrasound assisted extraction was found at 26.29% [8].  
Safranal, isophorone, and 4-ketoisophorone are the most common constituents of safron aroma regardless the extraction method and geographical origin [5, 8, 26, 28, 32, 33, 34]. 2,6,6-trimethylcyclohex-1,4-diene-1-carbaldehyde, isophorone, and 4-ketoisophorone have been previously detected in safron samples from Greece, Iran, Italy, and Spain by ultrasound extraction with diethyl ether [34].  
The above compounds along with 3,5,5-trimethylcyclohex-3-en-1-one, 7,7-dimethylbicyclo [4.1.0]hept-3-ene-2,5-dione, 4-(2,6,6-trimethylcyclohex-1-en-1-yl)butan-2-one and \(\beta\)-ionone have been isolated by SPME-GC-MS with a DVB/CAR/PDMS fiber in Italian saffron samples [28]. Hexanal has been detected by the same technique in Greek and Spanish saffron samples, while megastigma-4,6(_E_),8(_E_)-triene has been detected in Greek, Iranian, and Spanish saffron samples [27].
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 3 Proposed Search Algorithm, a Set of Behaviors: A1
### Keep Distance Behavior
The behavior so posed depends only on three variables, the adimensionalized distance at which the forces are at equilibrium, \(\tilde{d}_{0}\), the adimensionalized distance at which the attractive force is maximum, \(\tilde{d}_{max}\), and the characteristic distance, \(d_{c}\). Once these distances are set, \(m_{r}\) and \(m_{a}\) are calculated with Equations (40a) and (38), respectively. \(A_{a}\) is calculated with Equation (40b), and finally, with Equation (39), we get \(A_{r}\). In Figure 5(a), the resultant forces have been represented for \(\tilde{d}_{0}=1/3\) and \(\tilde{d}_{max}=0.5\). Having \(N_{a}\) the number of agents, the total force on agent \(i\) will be:  
\[\mathbf{F}_{T}^{i}=\sum_{ \begin{subarray}{c}j=1\\ \forall j\
eq i\end{subarray} }^{N_{a}}\mathbf{F}_{ij}^{i} \tag{41}\]Once the total force vector \(\mathbf{F}_{T}\) over an agent has been calculated, an income value must be assigned to each surrounding cell. Given the direction \(\psi_{F}\) of \(\mathbf{F}_{T}\) and its modulus \(F_{T}\), a normal distribution is used along the different headings of the surrounding cells:  
\[I_{D_{g}}=\frac{F_{T}}{\sqrt{2\pi\sigma^{2}}}e^{-\left(\frac{\alpha F_{T}^{i}} {2\sigma^{2}}\right)^{2}} \tag{42}\]  
where \(I_{D_{g}}\) is the income corresponding to the surrounding cell \(g\), \(\sigma\) is the variance of the normal distribution, and it has been chosen as \(\sigma^{2}=0.5\); \(\Delta\psi_{F}^{g}\) is the difference of the heading of \(\mathbf{F}_{T}\) and the heading of each surrounding cell \(g\). In Figure 5c, an example of the keep distance behavior has been represented. Agents 2 and 3 apply two repulsive forces \(\mathbf{F}_{1_{2}}^{1}\) and \(\mathbf{F}_{1_{3}}^{1}\) on Agent 1. The total force in this example is \(F_{T}^{i}=1\). The incomes \(I_{D_{g}}\) for each surrounding cell have been calculated with Equation (42). In this case, since the heading of the total force is \(\pi/4\), we have:  
\[\Delta\psi_{1-8}^{F_{T}}=[-\frac{\pi}{4},0,\frac{\pi}{4},\frac{\pi}{2},\frac{3 \pi}{4},\pi_{,}-\frac{3\pi}{4},-\frac{\pi}{2}] \tag{43}\]