esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Apical periodontitis (AP) involves inflammation and destruction of peri-radicular tissues. The condition may be the result of pulp infection or physical or atrogenic trauma to the pulp and extrusion of root canal filling materials [1]. Peri apical inflammation stimulates bone resorption and the formation of granulomas and cysts. In addition, immune responses are elicited in response to bacteria and their products [2] since there is a well-established relationship between periapical inflammation and bacterial infection [3, 4, 5]. The root canal wall is a surface to which bacteria can adhere, and a biofilm can develop when the root canal system is infected [6, 7]. Such a biofilm provides microorganisms with a series of advantages and skills not available to individual cells living in a free-floating (planktonic) state [8] because the microorganisms are protected from chemical and mechanical stresses by the extracellular polymeric substance (EPS) [9, 10].  
Endodontic therapy is designed to cure or prevent apical periodontitis [11], with the goal of preserving the tooth as a functional unit within a functioning dentition. Biochemical preparation of the canal, the use of irrigation solutions, and a void-free, three-dimensional, hermetic root canal filling that minimizes the opportunity for reinfection [12] are three factors used in the repair of apical periodontitis [13].  
Unfortunately, while free-living planktonic bacteria inside the root canal cavity are eradicated early in the process of cleaning and shaping the canal, bacteria in biofilms or less accessible areas are difficult to eradicate and may cause persistent disease [14]. If the root canal is infected, a biofilm may also be present in the lateral canals, fins, isthmuses, and dentinal tubules [15].  
The goal of evidence-based dentistry (EBD) is to integrate the findings of high-quality clinical research into clinical decision making in order to optimize patient outcomes [16; 17]. This study utilized evidence-based dentistry principles to conduct a systematic search and analysis of the available literature regarding bacterial penetration into dentinal tubules. In daily clinical practice, the elimination of bacteria within the root canal system is the most challenging aspect of root canal treatment. Endodontic irrigants play an important role in the elimination of bacteria. Various irrigation protocols have been purposed for this purpose without a necessary standardization. This review identifies the need to develop standardized methods to evaluate bacterial penetration depth after the use of different irrigants.  
The aim of this study was to investigate the depth to which bacteria penetrate human dentinal tubules in vitro, following the use of various irrigation solutions.
## 5 Results and Analysis
### Path Loss  
The non-uniform structure, rough boundary surfaces and volatile environment of a mine intrudes the propagated signal and causes severe propagation loss. The reception power of the signal relies on the frequency and link distance covered by the propagated signal. Therefore, frequency and distance-dependent path loss is simulated. Mathematical expression to compute path loss and its analysis is discussed below. The path loss can be computed by taking the impulse response of a complex channel and the simplified expressions are given below [34]  
\[PL(z)_{dB}=-10 log_{10}\left(\frac{1}{N}\sum_{j=1}^{N}|H(f,z)| \right)^{2} \tag{32}\] \[P(f,z)=PL(f)PL(z) \tag{33}\]  
where \(PL(f)\) is frequency-based path loss and \(PL(z)\) represents the distance-dependent path-loss parameter and the generic function of integrated path loss is given in Equation (32). Equation (33) is extended for specific expressions of frequency-dependent and distance-dependent path loss, as [35]  
\[PL(f)\varpropto ke^{(-\delta_{1}f)} \tag{34}\] \[PL_{dB}=PL_{dB}(d_{o})+10 nlog\left(\frac{z}{d_{o}}\right)+X\sigma \tag{35}\]  
where \(PL_{dB}(d_{o})\), \(n\), and \(X\sigma\) represents loss at reference distance, exponent for propagation loss, and fading parameter with standard deviation \(\sigma\), respectively.  
Graphical results for path loss are given in Figure 5 which shows the relationship between the path loss and link distance for the LOS path and non-line-of-sight (NLOS) channel link. It can be observed from Equation (35) that the value of path-loss exponent \(n\) is slightly higher for the NLOS case where signal power is received through specular reflection which enhances the overall propagation loss. Figure 5a shows that the path-loss difference between LOS and NLOS link is comparatively low in the near region due to the low attenuating effect, but as the distance increases, the response of path loss increases exponentially. The path loss at all frequencies attenuates fast in the beginning because in the near region, higher modes with high attenuation rates exist, while in the far region lower modes dominate. Distance dependency in both the cases attenuates the signal, and this behavior clarifies the direct relationship of propagated signal performance with the link distance.  
Neglecting the negative sign of frequency-dependent path loss in Figure 5b, the behavior of path loss is very clear regarding the frequency response of signals. The range of frequency is considered from 2 GHz to 6 GHz, but the optimal use of frequency for this slot is 2.4 GHz and 5 GHz, hence path-loss response at these two particular frequencies is investigated by classifying LOS links for different segments of the mine. It can be seen that at the initial values of slot 2.4 GHz, the obtained path loss is low as compared to the higher frequency of this band. The comparative difference between both links is low at 2.4 GHz, but as the operating frequency is increased to 5 GHz, the path-loss difference is highly increased.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 3 Data and Method
### Data Preprocessing  
The collected data were preprocessed so as to improve the data quality and carry out data mining. Air quality monitoring equipment and meteorological monitoring equipment will cause leakage in data collection due to machine failure, regular inspection and maintenance, unstable transmission, bad weather, and other uncontrollable factors. The existence of such missing values will have some impact on data mining. The missing values are normally required to be removed or filled to ensure the performance of modeling [18]. On the one hand, when there are several missing values in a data record, we directly remove them; on the other hand, a linear interpolation [19] is implemented to fill empty values when there is only one missing value in a data record. After that, features in the data that are described by text, such as weather (sunny, cloudy, foggy, snowy, rainy, etc.) and wind direction (north, west, east, south, northwest, northeast, etc.), are quantified.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stations** & **Code** & **Monitoring Environment** & **Coordinates** \\ \hline JianCaoPing & S1 & Urban: residential area & N 37.887, E 112.522 \\ \hline JianHe & S2 & Urban: residential area & N 37.910, E 112.573 \\ \hline ShangLan & S3 & Rural area & N 38.010, E 112.434 \\ \hline JinYuan & S4 & Suburban: residential area & N 37.712, E 112.469 \\ \hline XiaoDian & S5 & Urban: residential area & N 37.739, E 112.558 \\ \hline TaoYuan & S6 & Urban: residential area & N 37.869, E 112.536 \\ \hline WuCheng & S7 & Urban: commercial area & N 37.819, E 112.570 \\ \hline NanZhai & S8 & Suburban: industrial park & N 37.985, E 112.549 \\ \hline JinSheng & S9 & Suburban: industrial area & N 37.780, E 112.488 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of Taiyuan air quality monitoring stations. N—north; E—east.  
Figure 3: **(a)** The location map of Taiyuan City; **(b)** distribution of air quality monitoring stations in Taiyuan City.  
Furthermore, to accelerate the convergence of the model and reduce the impact of outliers, the features in the data records are normalized as follows:  
\[f^{*}=\frac{f-f_{\text{min}}}{f_{\text{max}}-f_{\text{min}}}, \tag{1}\]  
where \(f_{\text{min}}\) represents the minimum value, and \(f_{\text{max}}\) represents the maximum value.
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 7 Conclusions  
Recently, machine learning techniques have gained popularity as artificial intelligence develops. To improve navigation performance, artificial neural networks have been the subject of substantial research. The entire results of artificial neural network research are explored for both the integration of the INS with GNSS and the usage of ANNs in the GNSS in navigation systems.  
Furthermore, it is possible to employ an artificial neural network as a \"mixed race\" modeling technique without the necessity for a rigorous system physical model. It makes more sense to utilize the input and output data to describe the system behavior. The GNSS can sometimes provide dependable and consistent navigating solutions. The integration of GNSS with INS has been a significant development in modern navigation. Recently, several techniques have been suggested to enhance the GNSS/INS performance during GNSS failures. To improve navigational accuracy and overcome the signal blockage problem in urban metropolitans, various technologies such as jamming, GNSS-restricted environments, the integration of GPS/INS or other GNSS/INS using Kalman filter, and more based on artificial intelligence are also used. The output of the underwater integrated navigation system is subject to degradation in aquatic environments. In order to increase the error checking of the integrated navigation system, both the value obtained of the DVL and the real underwater working environments of autonomous underwater vehicles (AUV) are prone to anomalies or even dropouts. A highly credible adaptive filter built on the VB theory is recommended for managing against the outliers. The review's main goals are an overview, a probe into, observation of, and an assessment of how well the current integrated navigation systems perform. To deal with GNSS disruptions, a neural network-based fusion strategy is recommended in this study.  
Moreover, for an accurate and sustainable navigation solution, the reviews of different studies have been studied to explore, conclude, and combine navigation systems with neural network technology. The application of neural networks to GPS is detailed in this review. It introduces state estimation algorithms for GNSS/INS integration. A review of the development of neural network enhanced GNSS/INS integration study is explored. Future difficulties are also mentioned for the demonstration of deep learning technique in navigation system designs.  
Conceptualization, D.-J.J.; methodology, D.-J.J.; data curation, D.-J.J.; writing--original draft preparation, D.-J.J., A.B. and I.A.M.; writing--review and editing, D.-J.J. and A.B.; supervision, D.-J.J. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support of the National Science and Technology Council, Taiwan under grant number NSTC 111-2221-E-019-047.  
Not applicable.  
The authors declare no conflict of interest.
# A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks
## 6 Conclusions  
We built up a situation of correspondence in a two-tier heterogeneous network with one primary motivation: to anticipate the decision-making of handover before UE remoteness from the serving AP caused the degradation of the QoS. Utilizing MIH framework gives the user the possibility to gather pertinent data that will assume a critical part in triggering the handover process. With respect to this information, we are concerned with RSS, which is wrapped in the beacons sent occasionally by the AP. To apply the concept of velocity supervision, we use the RSS AVAR, bearing in mind the ultimate goal of identifying the change in velocity. Thus, we recalculate \((\Delta t)_{lifetime}\) for the present velocity. Once the choice is made, MIH passes the handover to PMIPv6, which is then in charge of administering the mobile IP functionality. Simulation results demonstrated that the amount of handover failure and packet loss can be diminished.  
**Author Contributions:** [PERSON]: main writing, design of the overall system, analysis, and improvement of the proposed system; [PERSON] and [PERSON]: overall supervision of the paperwork, review, and comments, etc. All authors have read and approved the final manuscript.  
**Conflicts of Interest:** Authors declare no conflict of interest.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 5 Experimental Results  
### Comparison with Other Methods  
This section evaluates the performance of the proposed YOLO-MEDet model against several state-of-the-art multispectral object detection methods across multiple datasets. We report the results using standard metrics and have conducted the comparisons under consistent conditions to ensure fairness and reproducibility.
## 2 Materials and Analytical Methods
### Analytical Methods
#### 2.4.4 High-Performance Liquid Chromatographic (HPLC) Analysis of Caffeine and Chlorogenic Acids  
The determination of the caffeine and chlorogenic acids content in the SCG extracts was performed according to the analytical procedure reported by [PERSON] et al. [33]. Chromatographic analysis was carried out using a HPLC apparatus with a Dionex (Germering, Germany) pump P580, a DG 1210 degasser, an automatic injector ASI-100, a spectrophotometric detector UVD 170S and a column oven. The analytes separation was performed using a Supelco Discovery C18 analytical column (4.6 mm i.d. \(\times\) 250 mm, 5-\(\mu\)m particle size) at ambient temperature. As the mobile phase, 0.3% (\(v/v\)) aqueous acetic acid solution (eluent A) and methanol of HPLC purity grade (eluent B) were used. The solvent gradient was as follows: 0 min, 20% B; 15-24 min, 50% B; and 27-29 min, 20% B. The flow rate of the mobile phase was 0.8 mL/min. The peaks of tested analytes present in the SCG extracts were identified based on their retention time by comparison with those of the analyte standards. Caffeine and chlorogenic acids were detected at 276 nm and 325 nm, respectively. The external standard curves were plotted for caffeine and chlorogenic acid (3-CQA). Chromeleon v.6.11 software (Dionex, Germering, Germany) was used for the data acquisition and processing.
## 4 Network Layer: The Main Building Block of Fixed Networks
### F1G PONs
#### 4.1.10 Outlook into Other Approaches for Access
Using Nyquist-WDM-PON, the network is migrating from point-to-point towards point-to-multipoint architecture, decreasing the overall number of optical interfaces from 2N to N + 1, where N is the number of access nodes. This solution provides significant advantages compared with legacy PON systems. The most important are (a) decreased number of transceivers due to the use of hub transceivers; (b) better routing efficiency, density, and simplicity, as the overall number of ports is significantly reduced; (c) better alignment of CapEx with actual bandwidth requirements, quick adaptation on changing bandwidth demands as well as traffic patterns; and (d) lower OpEx in terms of power consumption, footprint, number of aggregation sites and support. The concept of Nyquist-WDM-PON has been lately commercialized in a version with the name \"XR-Optics\" [46, 47]. Figure 12: Architecture of a UD-WDM-PON [44].
## 3 The Applied Algorithms
### The Framework of Applying Meta-Heuristic Algorithms for Solving Optimization Problems  
As presented in Sections 3.1 and 3.2, it is easy to recognize that the main difference between the two applied algorithms, OOA and WOA, is their update mechanism for the new solutions. However, while these algorithms are applied to solve a given optimziation problem such the considerd problem in this study, both OOA and WOA share the same structure, which consists of certain steps as follows:  
* Step 1: Set up the initial control parameters, including the population size (_Nps_) and maximum number of iterations (_MI_).
* Step 2: Randomly generate a set of populations based on the lowest and the highest limits characterized by the given optimization problem.
* Step 3: Set the current iteration (_CI_) by 0.
* Step 4: Calculate the fitness value for each individual of the population.
* Step 5: Update the new solutions using the update mechanism featured by each applied algorithm. For instance, OOA updates the new solutions by using Equations (11) and (12), while WOA applies Equations (13)-(15) to complete its update process.
* Step 6: All of the new solutions will be checked for their violations of the boundaries characterized by the given problem.
* Step 7: Calculate the new fitness value for each individual in the population and determine the best solution.
* Step 8: Check the terminated condition by evaluating the value of _CI_ compared to _MI_ as follows: If _CI_ is slower than _MI_, increase _CI_ by one and then return to Step 5; otherwise, stop the searching process and report the optimal solution.
## 4 Experimental Results
### Evaluation of Proposed Method
#### 4.4.1 Qualitative Comparison on Synthetic and Real-world Hazy Images  
For qualitative comparison analysis, we present several examples of dehazing results for methods mentioned above [14, 26, 28, 35, 40]. Figures 13-15 show some results on both indoor and outdoor hazy synthetic images and real-world outdoor hazy images, respectively.  
* **Comparison on synthetic images**: As shown in Figures 13g and 14g, the proposed method has a good performance on hazy synthetic images, where it is not easy to discern our results from the truth images. It achieved good dehazing results, considering haze-removing, color-preserving, visibility-enhancing, and edge-preserving. By contrast, we observe that [PERSON] et al.'s [14] and [PERSON] et al.'s [26] results still contain a thin haze layer in most synthetic images, especially for thick haze. Thus, these methods are sensitive to the thickness of haze. [PERSON] et al.'s [24] method could lessen haze on images, but it left some haze trace on distant scene objects (e.g., room, teddy, and mountain2 images). On the other hand, [PERSON] et al.'s [40] and [PERSON] et al.'s [35] methods could remove haze from synthetic images successfully. However, these two methods have a color-shift problem in some regions (e.g., the background of cable image and sky color in park image).
* **Comparison on real-world images**: In this part, we performed comparisons on the real-world hazy images. The real-world hazy images were collected from the Internet because of the  
Figure 15: Samples of visual comparison of proposed system’s results with the comparative methods on real-world hazy images: (**a**) hazy image; (**b**) [PERSON]’s method [14]; (**c**) [PERSON]’s method [28]; (**d**) [PERSON]’s method [26]; (**e**) [PERSON]’s method [40]; (**f**) [PERSON]’s method [35]; and (**g**) our method.  
unavailability of a hazy images dataset. Figure 15 demonstrates examples of comparisons on real-world images with comparative methods [14; 26; 28; 35; 40].  
At first glance, we can say that most methods removed haze from images effectively. However, if inspecting carefully, we can discover that [PERSON] et al.'s [14] method still has some residual haze in specific regions (e.g., tree and forest images). Although the other comparative methods [26; 28; 35; 40] could eliminate haze particles from images efficiently, the over-enhancement problem invades most of the results (e.g., forest, mountain1, and tree images); in addition, these methods generate artificial colors on sky region (mountain1 image). Besides, [PERSON] et al.'s [28] and [PERSON] et al.'s [40] results contain halo artifacts caused by edge-deformation. By contrast, our proposed method yielded promising results on real-world images, where it could eliminate haze, produce real vivid colors, and preserve edges of recovered images.
# A Full-Duplex LED-to-LED Visible Light Communication System
## 3 Receiver Characteristics of LED
In addition, the channel capacities calculated by Equation (7) with the results for the rise time and SNR are shown in Table 3. Note that the three best performing cases in each experimental result are underlined in Tables 2 and 3. Additionally, the transmitter LED to pc-white LED case was not considered to avoid confusion, since pc-white LEDs are not appropriate receivers. In [15], a half-duplex LED-to-LED VLC performance study was conducted using a blue-to-green set, since the blue-to-green set showed the best received signal amplitude value, which is the same as our SNR results in Table 2.  
However, the blue-to-green set is not the best case because its rise time is slow. The estimated channel capacity of blue-to-green set is 38.5 kbps, as shown in Table 3. According to Table 3, rather than the blue-to-green, instead the green-to-yellow set shows the best capacity of 59.1 kbps, which is 53% better than the blue-to-green set. Thus, the green-to-yellow set was chosen as the best color set for LED-to-LED VLC systems in our experiment. Figure 2: Experimental setup for measuring the rise time and signal-to-noise (SNR) of each LED color set (transmitter and receiver LEDs). Note that R, Y, G, B, and W stand for red, yellow, green, blue, and white, respectively.