esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions
### Effectiveness and Advantages of the Objective Evaluation Method  
The results of the paired samples \(t\)-test have been discussed and showed a statistically significant difference between the subjective and objective methods. This indicates a systematic difference in how each method assesses handwriting assistive devices. However, the Pearson correlation analysis revealed a significant positive relationship between subjective scores and objective scores (r = 0.69, \(p=0.0279\)), as depicted in Figure 6. This suggests that participants who scored higher on subjective evaluations also tended to score higher on objective evaluations, and vice versa. In other words, although the two methods have systematic differences in how each method assesses devices, the relationship between the two methods remains strong, meaning that each method reliably distinguishes between higher and lower performers in different manners. This dual finding highlights that the objective evaluation method not only provides a standardized and quantifiable evaluation but also aligns with the subjective perceptions of performance, thereby reinforcing its utility and effectiveness in handwriting evaluation.  
The EWM of the objective method identified five key objective indicators (\(S_{4}\), \(S_{5}\), \(S_{6}\), \(S_{7}\), and \(S_{10}\)), with entropy weights exceeding 0.1, specifically 0.2440, 0.1736, 0.1613, 0.1323, and 0.1394, respectively. These high-weight indicators play a pivotal role in the objective scoring process [39; 40], reflecting their significant impact on the overall evaluation of handwriting performance. The high weights assigned to these indicators enhance the objective method's ability to capture nuanced performance traits that may not be fully addressed by subjective evaluations. This is exemplified by the outliers identified for participant 9 in the box plots of Figure 5. The objective evaluation method, through its emphasis on key indicators, was able to detect specific deficiencies or unique performance traits in participant 9's handwriting that were not fully recognized in the subjective evaluation. Such discrepancies highlight the objective method's strength in providing a more standardized and quantifiable evaluation, minimizing the influence of personal biases or unmeasured factors inherent in subjective evaluations.
## Research Frontier  
Researchers are devoting themselves to improving VANETs during this period.  
Around 2013 and 2014, researchers were looking for technologies that satisfied different demands. For example, a comprehensive survey of VANET is conducted to verify its economic and environmental-friendly characteristics [50]. However, most researchers are concerned about road safety problems. [PERSON] et al. aim to propose an evaluation scheme that could predict the possibility of collisions at intersections according to real-time information received from surrounding vehicles. Their study shows the beacon message to be unsatisfactory, while the timeliness of beacon messages is confirmed [52]. Coincidentally, [PERSON] et al. evaluate the wireless collision avoidance (CA) system in VANET by generating vehicular mobility traces, deriving the probability of rear-end collisions, and quantifying the probability of vehicles not receiving emergency messages. The results confirm the function of CA systems; however, the relationship between collisions and vehicle density is not linear [91]. Some researchers consider the IEEE 802.11 medium access control protocol and its requirements for vehicular safety and propose a reverse back-off mechanism designated for road safety in high-density scenarios [51]. Beyond that, some researchers consider the integration of V2V and V2I. For instance, [PERSON] et al. present a communication platform and a network designed for both the vehicle and infrastructure sides and introduce a service access middleware together with onboard application management [53]. Researchers also considered Y-Comm architecture such as network dwell time (NDT), the time before handover (TBH), and exit time (ET) to overcome handover issues [54]. Furthermore, researchers consider standardized facilities such as the cooperative awareness message (CAM) and the decentralized environmental notification message (DENM). In [PERSON]'s article, the functions of vehicle tracking and dissemination of traffic accidents using the two facilities are validated, as are impacts from vehicle speed, signal quality, or message dissemination rules [92].
# Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding
###### Abstract
[PERSON] and [PERSON] [22] studied the correlation between laser microwelding bonding strength and the feature of audible sound collected during welding by simulating a gap between the layers of metal sheets. In this study, following the study reported by [PERSON] and [PERSON] [22], the correlation between welding quality and the features of AE signals was analyzed. In the previous work [22], the signals analyzed is audible sound collected by a MEMS microphone with frequency range below 10 kHz. In this paper, the signal studied is the Acoustic Emission (AE) signal with a range from 50 kHz to 400 kHz. The AE signal was collected simultaneously with audible sound studied in Reference [22] from the same welding experiments. The experimental setup simulated the condition in the manufacturing of the metal mask in the production line. To verify the capability of applying the AE signal in detecting the low-strength joint in laser lap microwelding, the same hidden Markov model (HMM)-based monitoring system as shown in Reference [22] was employed to evaluate the performance of the selected AE features.
## 4 Discussion
According to Tables 4 and 5, in terms of the entire arterial, the total green wave bandwidths obtained by each traffic mode are shown in Table 8. The calculation method for the improvement in Table 8 is the same as the one shown in Table 6.  
According to Table 8, the objective function values on the test arterial for Models 1 and 2 are 6.7903 and 9.0614, respectively. Compared to Model 1, the total green wave bandwidths generated by Model 2 for all traffic modes on the test arterial increased by 33.4%. Specifically, compared to Model 1, the total green wave bandwidths of cars, buses and electric bicycles generated by Model 2 at the entire arterial level were increased by 36.8%, 47.9%, and 19.3%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & **Cars** & & & **Buses** & & **Electric Bicycles** & \\
**Road** & **Bandwidth** & **Bandwidth** & **Bandwidth** & **Bandwidth** & **Bandwidth** & **Bandwidth** & **Bandwidth** & **Bandwidth** \\
**Segments** & **Generated** & **by Model 1** & **Generated** & **Generated** & **by Model 2** & **Generated** & **by Model 2** & **Conerated** & **by Model 1** & **Generated** \\ \hline Between intersections & 0.482 & 0.3597 & \(-\)25.4\% & 0.707 & 0.966 & 36.6\% & 0.5516 & 0.8911 & 61.5\% \\
1 and 2 Between intersections & 0.362 & 0.7481 & 106.7\% & 0.562 & 0.9183 & 63.4\% & 0.8596 & 0.6043 & \(-\)29.7\% \\
2 and 3 Between intersections & 0.498 & 0.3784 & \(-\)24.0\% & 0.8203 & 0.9572 & 16.7\% & 0.7262 & 0.933 & 28.5\% \\
3 and 4 Between intersections & 0.179 & 0.5938 & 231.7\% & 0.342 & 0.7541 & 120.5\% & 0.7006 & 0.9574 & 36.7\% \\
4 and 5 & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The green wave bandwidth of each traffic mode on each road segment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Road Segments** & **Bandwidth Generated by Model 1** & **Bandwidth Generated Model 2** & **Improvement** \\ \hline Between intersections 1 and 2 & 1.7406 & 2.2168 & 27.4\% \\ Between intersections 2 and 3 & 1.7836 & 2.2707 & 27.3\% \\ Between intersections 3 and 4 & 2.0445 & 2.2686 & 11.0\% \\ Between intersections 4 and 5 & 1.2216 & 2.3053 & 88.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The total green wave bandwidths obtained by the three traffic modes on each road segment. Based on the analysis at the road segment and arterial levels, the presented model demonstrates its capability to fully consider the green wave coordination demands of each traffic mode, thereby significantly enhancing the green wave bandwidths of the three traffic modes.
# The Invisible Threat of Antibiotic Resistance in Food
## 4 Antibiotic Resistance in Drinking Water
### Antibiotic Resistance Gene Migration Between Micradgae and Bacteria
A third area of potential application of microalgae is the development of functional foods, which may include the incorporation of high-value molecules such as essential amino acids, carbohydrates, pigments, and proteins derived from microalgae [326; 332; 333]. Microalgae possess a remarkable aptitude for the elimination of a wide spectrum of pollutants and hazardous materials in wastewater, which are produced by various sources, including domestic agricultural runoffs, effluents, textile, printing, pharmaceutical, and electroplating industries [334]. It wasa recently demonstrated that microalgal-mediated wastewater treatment systems have the capacity to reduce antibiotic resistance genes (ARGs) in wastewater [335]. It is hypothesized that microalgae have the capacity to function as natural barriers, thereby playing a pivotal role in hindering the transfer of antibiotic resistance genes (ARGs) between symbiotic bacteria. This hypothesis suggests that microalgae could contribute to a reduction in the abundance of ARGs during the process of wastewater treatment [336]. Conversely, a cyanobacterial bloom caused by the _Planktotrix_ and _Microsystis_ species at Lake Taihu in China was reported to increase the probability of emergence of antibiotic-resistant bacteria (ARB) [337]. Although a limited number of studies have addressed the issue of antibiotic resistance migration among microalgae and bacteria, [PERSON] [338] and [PERSON] et al. [339] demonstrated that _E. coli_ K12 and _E. coli_ DH5\(\alpha\) are capable of uptake kanamycin resistance genes in co-culture with genetically engineered cyanobacterium _Thermosynechococcus elongatus_ BP1. [PERSON] et al. 's [340] findings indicated that cyanobacteria can obtain and transmit ARGs (tetracycline (_tet_A) and sulfonamide (_su1_) resistant genes) in aquatic environments through HGT. Studies by [PERSON] et al. [341] and [PERSON] et al. [342] showed that a number of factors, including temperature, pH, the availability of nutrients, UV radiation, and dissolved oxygen, may influence the transfer of ARGs  
The aforementioned evidence indicates, despite the beneficial characteristics of microalgae, there is a potential risk of transmission of antibiotic-resistant genes to humans. This is also confirmed by the findings of [PERSON] et al. [343], according to which the transmission of ARGs carried by microalgae to organisms with high nutritional levels within the food web may pose a potential risk to human health. The presence of _Chlorella pyrenoidosis_ in their research work was observed to enhance the abundance of ARGs (tetracycline- and sulfonamide-resistant genes), thereby elevating the probability of ARG transmission along the food chain.
# Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
## 3 Problem Statement and the Analytical Model
### Analytical Model  
We commence by noting the definitions of the well-known ARM concepts of support, confidence, minimum support, minimum confidence, and the Apriori principle first introduced in [28], and as detailed in [29,30].  
* The support of an item \(A\), in a transaction database \(T\), is given by: \[\text{supp}\left(A\right)=\text{P}(A)=\frac{\text{number of transactions in $T$ that contain $A$}}{\text{number of transactions in $T$}};\]
* The probability of the presence of item \(A\) leading to the presence of item \(C\) (commonly referred to as confidence) is given by: \[\text{conf}\left(A\to C\right)=\frac{\text{number of transactions in $T$ that contain both $A$ and $C$}}{\text{number of transactions in $T$ that contain $A$}}.\]  
When \(\text{supp}\left(A\right)\) exceeds some user-defined value for support (commonly referred to as minimum support or minsup) we note that \(A\) is considered to be frequent. Similarly when \(\text{conf}\left(A\to C\right)\) exceeds some user-defined value for confidence (commonly referred to as minimum confidence or \"minconf\") we note that \(A\) and \(C\) are considered to be associated. Note that FIM is defined as the process of finding all itemsets that exceed minsup in a given database [13,16,29].  
The Apriori principle, first detailed in [28], and more recently in [16], states that for a given set of transactions, \(\text{supp}\left(A\right)\geq\text{supp}\left(A,C\right)\). This is consistent with probability theory where \(\text{P}(A)\geq\text{P}(A\cap C)\), as well as in practical terms, e.g., where the number of transactions that contain pupils who are absent on Monday AM is always greater than or equal to the number of transactions that contain absences on both Monday AM and PM.
## 5 Discussion  
The anisotropic properties of the RDFN models have been discussed. It has been accepted that the fracture patterns of underground openings are greatly influenced by the distribution of the structure planes. Figure 11 shows the anisotropic failure patterns of the RDFN model after excavation. In this test, vertical load is applied on top and bottom boundaries, with a confining pressure of 1.0 MPa. The failure modes of the opening are highly affected by the orientation of the fracture sets. Meanwhile, certain joint sets will dominate the failure of the surrounding rock mass. Take the loading direction of 30\({}^{\circ}\) for example, roof failure is the principal failure type. However, rib spalling occurs when the loading direction comes to 90\({}^{\circ}\).  
The failure patterns are affected by both the stresses conditions and the excavation types for the jointed rock masses [41; 42]. Figure 12 shows the failure patterns of surrounding rocks of the RDFN model with rectangular and circular opening. According to Figure 12a, the failure area is relatively smaller than the circular opening (Figure 12b). A larger convergence area could be obtained in the circular opening model due to the effect of the fracture sets. Similar fracture patterns observed around openings with structural planes are shown in Figure 13. According to the comparison, the influence of the distribution and geometry should be taken into account when assessing the failure modes and area of underground openings. The proposed RDFN model could provide a new reasonable model for the simulation of jointed rock mass, especially when estimating the failure patterns of surrounding rock mass of underground openings.  
Figure 10: Anisotropic properties of the jointed rock mass. (**a**) The Stress–strain curves in different directions. (**b**) The peak compressive strength in different directions (unit: MPa).  
Figure 11: Anisotropic properties of RDFN models after excavation under different loading directions. (**a**) The failure patterns. (**b**) The bond failure of different rock masses.  
Figure 12: Failure patterns of the RDFN models with varied excavation shapes. (**a**) A rectangular shape (size: 40 m \(\times\) 40 m). (**b**) A circular shape (diameter = 40 m).
## 5 Examples
### Example 2  
Solve the second-order linear VFIDE  
\[u^{\prime\prime}(x)+a_{1}u^{\prime}(x)+a_{2}u(x)-\int_{0}^{x} \cos(x-t)u^{\prime}(t)dt-\int_{0}^{x}\sin(x-t)u(t)dt\] \[\qquad-\int_{0}^{1}(x^{2}+xt)u(t)dt=f(x),\quad x\in[0,1], \tag{41}\]  
subject to homogeneous boundary conditions  
\[u(0)=0,\quad u(1)=0. \tag{42}\]  
First, we note that \(n=2,\ m=2\) and \(\ell=2\) and define \[Au = u^{\prime\prime}(x)+a_{1}u^{\prime}(x)+a_{2}u(x),\] \[Ku = \int_{0}^{x}k_{1}(x-t)u^{\prime}(t)dt+\int_{0}^{x}k_{2}(x-t)u(t)dt, \quad k_{1}(x)=\cos x,\quad k_{2}(x)=\sin x,\] \[\bar{K}u = x^{2}\int_{0}^{1}u(t)dt+x\int_{0}^{1}tu(t)dt,\] \[\Phi(u) = \bigg{(}\begin{array}{c}u(0)\\ u^{\prime}(0)\end{array}\bigg{)},\quad\mathbf{N}=\bigg{(}\begin{array}{cc}0 &0\\ 1&-1\end{array}\bigg{)},\quad\Theta(u)=\bigg{(}\begin{array}{c}u^{\prime}(0) \\ u(1)\end{array}\bigg{)}.\]  
Next, we set the vectors  
\[g = \big{(}\begin{array}{cc}g_{1}(x)&g_{2}(x)\end{array}\big{)}= \big{(}\begin{array}{cc}x^{2}&x\end{array}\big{)},\] \[\Psi(u) = \left(\begin{array}{c}\int_{0}^{1}h_{1}(t)u(t)dt\\ \int_{0}^{1}h_{2}(t)u(t)dt\end{array}\right)=\left(\begin{array}{c}\int_{0} ^{1}u(t)dt\\ \int_{0}^{1}tu(t)dt\end{array}\right),\]  
and define the linear operators  
\[Vu = Au-Ku,\quad\mathcal{D}(V)=\Big{\{}u:u\in C^{2}[0,1],\;\Phi(u)- \mathbf{N}\Theta(u)=0\Big{\}},\] \[Su = Vu-g\Psi(u),\quad\mathcal{D}(S)=\mathcal{D}(V). \tag{43}\]  
We observe that \(k_{1}\), \(k_{2}\), \(g_{1}\) and \(g_{2}\) are continuous functions of exponential order. Therefore, we use Theorems 2 and 3 to construct the unique solution of the boundary value problem (41), (42).  
Specifically, for \(a_{1}=-1,\;a_{2}=1\) and \(f(x)=-x^{2}+x\), we obtain the unique solution  
\[u(x)=\frac{12}{745e-4611}\Big{(}238e^{x}-(25e+5)x^{3}-(71e-81)x^{2}-(142e-162) x-238\Big{)}.\]  
For \(f(x)=e^{x}\), we obtain  
\[u(x) = \frac{1}{745e-4611}\big{\{}\big{[}(745e-4611)x+2111e-836\,\big{]}e ^{x}\] \[+\Big{(}-300e^{2}+340e+1180\Big{)}x^{3}+\Big{(}-852e^{2}+2406e-672 \Big{)}x^{2}\] \[+\Big{(}-1704e^{2}+4812e-1344\Big{)}x-2111e+836\Big{\}}.\]
## Experimental Results
### Comparative Study of Ground-Level and Above-Ground Deployment
#### 5.1.2 Temporal Properties  
As observed, the RSSI or the PRR values were better at height compared to a ground-level deployment. Figure 5 also shows that the temporal properties of links were different in the two deployments, with an opposite behavior when considering PRR and RSSI values. At ground level, while we had a stable radio link, but poor quality, in terms of RSSI values, we observed a relatively high fluctuation of the measured PRR values. A different behavior was noticed for the deployment at height, where we had stable PRR values and a high fluctuation of RSSI values.  
We also note that the relatively high RSSI values obtained at height resulted in better PRR values than the stable RSSI values measured at ground level. As hinted by Figure 5b and further discussed in Section 5.1.4, the ground-level deployment also resulted in a significant number of unidirectional links. Indeed, despite very similar RSSI values, link 3-4 showed highly different PRR values on the two directions, node 4 receiving many more messages than node 3.  
An explanation for the low RSSI values in the ground-level deployment might come from the increased number of signal reflections, hence resulting in multi-paths [30]. As both the transmitter and the receiver were close to the ground, this phenomenon was to be expected. On the other hand, in the case of a deployment at height, the propagation environment was a more open space (compared to a deployment at ground level), but also more sensitive to different sources of reflexions (e.g., a person moving around the deployment area). We hypothesized that the fluctuations of the RSSI values in the case of a deployment at height was due to this property of the environment.
# Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging
## 3 Results and Discussion  
### Reflectance Spectra of Pears  
Because the pear is kind of a ball-shaped object, the surface of the pear is bright in the middle but dark at the edge. In order to minimize the effect of illumination on the experimental results, this study chose the normal skin region of interest (ROI) near the equator line of the pears. The spectra were extracted from the ROI region of 60 normal samples and 60 bruised samples, and the average spectra of all bruised samples and normal samples were calculated separately. Considering that noises exist at the beginning and end of the spectrum, a 420 to 1000 nm spectral range was selected for the final analysis.  
It can be seen from Figure 3 that the average reflectance of the damaged area was lower than that of the normal region in the 420 to 1000 nm spectral range. In addition, there were two obvious absorption peaks in the graph. The absorption peak at 680 nm is mainly caused by the absorption of chlorophyll on the surface of the fruit [21] which reflects the color information of the fruit surface, while the absorption peak near 960 nm is mainly caused by the water absorption [22].
# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 7 Conclusions  
This study's findings mainly centered on detecting DDoS attacks and allocating bandwidth in the VANET, based on a machine learning-hybrid optimization approach. DDoS attack detection was performed using the SVM-HHO technique, i.e., a modified SVM. Then, the impact of DDoS attacks in the VANET was mitigated using a hybrid whale dragonfly optimization technique (H-WDFOA-VANET). Using a unique optimization method called the hybrid whale dragonfly optimization approach (H-WDOA-VANET), which hybridizes the notion of rider integrated cuckoo search, this study found the best route selection with minimal routing costs (RI-CS). To utilize the predicted information after validation, fast and reliable communication was achieved for stability in vehicular networks. Energy consumption, throughput, latency, and packet delivery ratioswere a few performance indicators used to gauge performance of the suggested approach. To increase the effectiveness of the routing process in VANETs, a new protocol, H-WDFOA-VANET, was introduced.  
The DCM and SBACO pairing at H-WDFOA-VANET sped up packet processing, decreased ant search time, and eliminated blind broadcasting packets while preventing stagnation issues. The packet delivery ratio and throughput benefit from H-WDFOA-VANET allowed for the use of the shortest channel without stagnation, due to its rapid processing of packets and fast convergence speed. Finally, the suggested hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was evaluated in comparison with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). The H-WDFOA-VANET system had values of 2.00000 mJ for energy consumption, 15.61668 s for latency, and 0.15759 for drop at node 60. A higher throughput was achieved using the new approach as well. With the suggested method, the energy consumption target, delay value, and drop value at node 60 may be attained. At node 80, the suggested approach minimized the drop value to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These results demonstrate the efficacy of the proposed H-WDFOA-VANETapproach. Therefore, the efficiency of the proposed system was greater than that of existing systems.  
Conceptualization: G.P.K.M., S.T. and A.J.; writing--original draft preparation: P.K.M., A.A. and C.O.S.; methodology: M.S., R.S., C.O.S. and T.C.M.; writing--review and editing: [PERSON], R.S. and G.P.K.M.; investigation: C.O.S., A.J. and S.T.; supervision: [PERSON], M.S. and S.T.; visualization: P.K.M. and T.C.M.; software: T.C.M., R.S. and S.T. All authors have read and agreed to the published version of the manuscript.  
National Research Development Projects to finance excellence (PFE)-14/2022-2024 granted by the Romanian Ministry of Research and Innovation.  
The authors declare no conflict of interest.