esa-sceva/satcom-chunk-collection huggingface dataset:
# Software Defined Wireless Mesh Network Flat Distribution Control Plane
## 5 QoS Monitoring  
Quality of service of network forwarding devices is particularly crucial for real-time applications like video streaming. The current SDN topology discovery service does not monitor or collect the QoS of underlying network elements, in spite of the fact that it is based on the LLDP protocol, which can collect QoS properties [27]. As shown in Figure 6a, additional to the mandatory TLVs (Type/Length/Value, i.e., key-value pair with length information) fields that are used in topology discovery, LLDP has optional Type Length fields that can be customized to discover other features such as QoS properties. In [27] four optional fields were identified to carry bandwidth, delay, jitter and packet loss, with an 8 byte size of each property as shown in Figure 6b. Therefore, a customized LLDP packet for QoS collection is longer than the original LLDP packet by 38 bytes. Since the aggregated mechanism allowed applying LLDP-based topology discovery on FD-SDWMN, it also can monitor and collect QoS.
## 3 Results
### Group 2--Use of CO\({}_{2}\) Laser in Peri-Implantitis Treatment (Table 2)  
[PERSON] presented a case series focused on the treatment of the peri-implant infrabony defects with CO\({}_{2}\) laser irradiation [17]. After a full-thickness mucoperiosteal flap elevation and the emotion of granulomatous tissue to obtain the implant exposure and the access to peri-implant infrabony defect, CO\({}_{2}\) laser irradiation was performed to achieve the implant surface decontamination and to promote blood coagulation in the bone defect. Subsequently guided bone regeneration technique was applied to achieve a complete filling of the infrabony defects. After an observation period of 27.10 \(\pm\) 17.83 months, no peri-implant inflammatory was noted; clinical parameters such as sulcus bleeding index and probing depth presented a statistically significant reduction during the examination period (\(p\) < 0.01). In addition, a complete bone filling was radiologically observed in all defects.  
[PERSON] and his co-worker conducted a study with the purpose to determine whether CO\({}_{2}\)-laser-assisted decontamination is effective in the treatment of ailing implants in humans [18]. Thirty-two patients with 73 ailing implants were considered in this evaluation and subsequently included in four groups on the basis of treatment to be performed (Group 1, conventional decontamination + soft tissue resection; Group 2, conventional decontamination + bone augmentation; Group 3, laser decontamination + soft tissue resection; Group 4, laser decontamination + bone augmentation. The outcomes of the four different treatments were evaluated and compared (Group 2 vs. Group 4; Group 1 vs. Group 3) 4 and 59 months after surgery. Following the results obtained from the present study, laser-assisted implant decontamination + soft tissue resection (Group 3) resulted in significantly more favorable levels of clinical attachment than conventional decontamination + soft tissue resection (Group 1) at 4, as well as 59, months after surgery. A shorter distance from implant shoulder to the first bone contact was observed in Group 3 (6.8 mm \(\pm\) 0.9 mm) versus Group 1 (7.9 mm \(\pm\) 1.3 mm) 59 months after surgery; no statistically significant differences were found between the two groups (6.9 mm \(\pm\) 1.4 mm for Group 3 vs. 7.2 mm \(\pm\) 1.9 mm for Group 1) after 4 months from surgery. A similar result was also found in the groups where bone augmentation was performed: Group 4 showed better results in terms of clinical attachment level (CAL) mean values (1.6 mm \(\pm\) 1.5 mm) with respect to Group 2 (3.5 mm \(\pm\) 1.2 mm), and a shorter distance from implant shoulder to the first bone contact was observed in Group 4 (2.3 mm \(\pm\) 0.9 mm) versus Group 2 (4.1 mm \(\pm\) 0.9 mm) 4 months after surgery. The authors noted that the use of CO\({}_{2}\) laser concomitant with soft tissue resection was effective in the treatment of peri-implantitis.
###### Abstract  
Aimed at the problems of low search efficiency of the A* algorithm in global path planning, not considering the size of AGV and to many turns, and the DWA algorithm easily falling into local optimization, an AGV path planning algorithm based on improved A* and DWA fusion is proposed. To begin, the obstacle rate coefficient is added to the A* algorithm's evaluation function to build an adaptive cost function; the search efficiency and path safety are increased by improving the search mode; by extracting key nodes, a global path containing only the starting point, key nodes, and endpoints is obtained. The DWA algorithm's evaluation function is then optimized and the starting azimuth is optimized based on information from the first key node. The experimental results show that in a static environment, compared with the traditional A* algorithm and the improved A* algorithm, the path length is reduced by 1.3% and 5.6%, respectively, and the turning times are reduced by 62.5% and 70%, respectively; compared with the improved ant colony algorithm in the literature, the turning angle is reduced by 29%. In the dynamic environment, the running time of this fusion algorithm is reduced by 12.6% compared with the other hybrid algorithms.  
AGV path planning; DWA algorithm; fusion algorithm; improved A* algorithm
## 4 Discussion and Conclusions
### Discussion
However, for applications requiring narrower margins of error (i.e., a surgical guide), the precision of specimen renderings should be studied. With regard to study limitations, PLA output is clearly of low quality, lacking sophistication [15]. In addition, the printing time of mesh-type 3D models may take longer than the output of the same volume of solid models with the same FDM printer; even half-size printing of a large organ (such as a lung) required nearly an entire day. However, reducing mesh density and stereolithography (STL) file reduction (decimation) could save the printing time. Stringing may also develop as a technical issue, although this is avoidable by controlling printer settings or resorting to non-FDM 3D printing techniques. We could not pursue internally filled models (delineating tumors) to compare costs and printing times, because no transparent or translucent PLA filament was compatible with our printer. Overall, our model depicts actual organ size and tumor location at low levels of resolution. This study remained focused on modeling technique and its clinical import, but a change in parameter default settings might serve to improve the resolution of our models. Optimal settings may even be organ specific. These are lingering issues to be resolved going forward. To truly establish its merit, prospective research is needed.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 5 Discussion
### Optimization of Traffic Signal Control  
One of the key contributions of this study is the exploration of signal timing optimization in conjunction with AV penetration rates. Our results show that optimized traffic signal control, particularly at cycle times between 60 and 100 s, can substantially improve both traffic flow and environmental outcomes. This observation is in line with the work of [5], who demonstrated that coupling AV technology with signal control optimization can enhance traffic performance by reducing vehicle delays and improving throughput at intersections.  
This study used [PERSON]'s method and PTV VISSIM simulation software to optimize signal timings, and the results suggest that coordinated AV movements, particularly under platooning conditions, can further enhance the benefits of optimized signal control strategies. These findings complement the research of [26], who found that integrating AV control algorithms with signal optimization can minimize delays, CO emissions, and fuel consumption in urban traffic networks.  
Despite these benefits, our findings also suggest that traffic signal optimization alone may not be sufficient to mitigate the inefficiencies introduced by cautious AVs, particularly at high penetration rates. This underscores the importance of developing more adaptive and flexible signal control strategies that can dynamically adjust to different AV behaviors and penetration levels, as noted by [37] in their study of adaptive signal control systems.
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 5 Verify the Feasibility of the Model and Analyze the Relevant Results
### University of Maryland Dataset Validation  
The above description uses four battery datasets provided by NASA to verify the CNN-LSTM-Attention-FVIM model in this paper, which reflects the strong predictive ability of the model proposed in this paper. In order to further verify the generalization ability and robustness of the model, we used the CS2-35 and CS2-37 battery datasets provided by the University of Maryland for prediction and verification. We did not modify any parameters, we used the same health features as the NASA dataset, and we provided 70% of the data as the training set for predicting and estimating. As shown in Figure 11, the CNN-LSTM-Attention-FVIM model proposed in this paper still has a high predictive ability, and the predicted value is very consistent with the true value. This shows that the model proposed in this paper has a strong generalization ability, and it can have a  
Figure 8: Fitting diagram and relative error diagram of B0018 battery.  
Figure 9: Comparison of prediction effects of different models.  
strong predictive estimation ability in the face of datasets from different sources, showing adaptability to the downward trend of different datasets.  
In order to more intuitively show the prediction ability of the model for the University of Maryland dataset, we still use RMSE and MAE as evaluation indicators. The specific values are shown in Table 6. As can be seen from the table, both error indicators are no more than 0.87%, which is within the acceptable error range. It can be seen that the CNN-LSTM-Attention-FVIM model proposed in this paper has a high generalization prediction ability for both the NASA and University of Maryland datasets. So far, the robustness and applicability of the model proposed in this paper can be fully verified.
## 2 HSI Procedure
### Hyperspectral Wavelength Selection
#### 2.3.5 Variable Importance in Projection (VIP)  
The VIP method answers which variables are important for the predictive capability of the model and ranks them in order of importance. This method, included in PLS regression, calculates scores for each variable. The higher the score, the more important the variable. The VIP method identifies the critical variables with a minimum loss of information that reduces the dimensionality of the data while enhancing the robustness of the model. Normally, a threshold is fixed by the user in order to filter out less important variables and focus the analysis on those that contribute most to model accuracy [93]. This is evident in the application of VIP in machine learning models for reducing data dimensionality during the prediction of beef rancidity while it is cooling. Machine learning models optimized by VIP learned to effectively forecast beef rancidity. The results showed how useful and efficient this method was for a non-destructive quality assessment of foods [94]. The rationale behind the trend in the recent application of variable importance in projection and improving the accuracy of postharvest handling operations comes from how the method manages to reduce dimensionality in spectral data, producing faster results with less computational cost but retaining high accuracy.  
[PERSON] et al. (2024) applied VIP to predict freshness indicators such as pH, TVB-N, and K values of chub mackerel during seafood quality evaluation. With partial least squares regression and Support Vector Regression methods trained by the VIP-PLSR model, high accuracy in its prediction was reached. Such facility of variable selection on the part of VIP enhanced the general efficiency in freshness evaluations and demonstrated wide applicability to various food matrices [95]. In addition, during nutritional analysis, where VIP has proved useful, the model, upon improvement using VIP, showed high accuracy in flavonoid content prediction and could predict total flavonoid content in fruits of Cerasus Humilis during storage [96].  
In general, it is believed that the choice of optimal wavelength maximizes the performance of hyperspectral imaging systems. Well-chosen wavelengths enhance the models' accuracy, reduce computational burdens, and bring better interpretability. CARS, PCA, SPA, UVE, and VIP are some of the various methods used for the refinement in analyzing the spectral data. Each of the techniques identified has strengths and applications in different areas of material analysis and quality control for which it is superior. Table 2 provides an overview of many of the currently available techniques for wavelength selection and applications.
## 2 Materials and Methods
### Transmission Phase  
In the PECR algorithm, the transmission phase involves communication from the sensor nodes to the BS on different levels and using different amounts of energy according to the proposed energy consumption model given below in detail.  
The energy consumption model is revised from [19] which shows (1) the transmission energy of a node for forwarding energy information and location to the BS and (2) for the reception of feedback messages from the BS in the initialization phase. Equation (3) shows the calculation of the threshold distance.  
\[E_{Tx}(l,d)=\left\{\begin{array}{ll}l_{engh}\times(t_{i}+e_{fs}\times d^{2} ),if\ d\leq d_{th}\\ l_{engh}\times(t_{i}+e_{amp}\times d^{4}),if\ d>d_{th}\end{array}\right. \tag{1}\]  
\[E_{Rx}(l)=l_{engh}*e_{r} \tag{2}\]  
where,  
\(l=\) length of the data packet.  
\(d=\) the distance of the respective node.  
\(e_{t}=\) the transmit energy.  
\(e_{r}=\) the receiving energy.  
\(e_{fs}=\) the free space energy (energy consumption amplification factor of the free space model).  
Figure 1: Optimal route-finding in the PECR algorithm.  
\(e_{amp}=\) the transmit amplifier energy (energy consumption amplification factor of the multipath fading model).  
\(d_{th}=\) the threshold distance.  
The threshold distance \(d_{th}\) is calculated as,  
\[d_{th}=\sqrt{\frac{e_{fs}}{e_{amp}}} \tag{3}\]  
In the transmission phase, the data communication and energy utilization at each level is determined independently and is given underneath.
## 6 Conclusions
Hence, the MPPT is suitable for applications such as photo-voltaic [53] and wind power generators with consistent sunlight and wind speed [54], respectively. However, it is hard to design the real-time MPPT controller for the wave energy converter due to the strong irregularity. On the other hand, the i-MET controller has the turbine efficiency curves as the reference, which have the optimal point. Hence, it is possible to build a real-time control algorithm based on the Lyapunov method. The Lyapunov method provides the real-time control law when the system is dynamic and the reference signal with the optimal point exists. The CAV controller uses the same control algorithm in order to track the set angular velocity; however, the reference angular velocity is constant. Therefore, the CAV controller shares the same states (air flow speed and turbine angular velocity) and assumptions as needed for the implementation of the i-MET controller. When the CAV controller is applied, the angular velocity converges to the desired value as the control gain increases. Simulations reveal that the overall power extraction is influenced mainly by the reference angular velocity, which is somewhat proportional to the wave height. Compared to the i-MET controller, the CAV controller needs to adjust the reference angular velocity depending on the wave state. Nonetheless, the CAV controller seems to be the more realistic control strategy when considering the optimal performance of the inverter, since it employs a generator that rotates at a constant speed providing a constant voltage. This allows a more realistic performance analysis of OWC-WEC. In order to implement the i-MET and CAV controllers practically, more realistic modeling with the physical limitations of the hydrodynamics of the chamber and the aerodynamics of the turbine should be considered. Furthermore, unlike the ideal DC generator, electric machine modeling should have the nonlinear torque-rpm curve with an actual efficiency curve. In addition, the inverter modeling that produces the control input for the terminal load should be developed under the consideration of substantial hardware specifications. Details on that will be covered in future studies. **Acknowledgments:** The presented work was supported by INGINE Inc.  
**Author Contributions:** [PERSON] conceived and designed the simulations; [PERSON] performed the simulations; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics are reported as mean \(\pm\) SD. The normality of the residuals was assessed visually using residual plots and the Shapiro-Wilk test. Normality was met for RD, HSR, and wellness variables. The data were analyzed using linear mixed models (LMM) and generalized linear mixed models (GLMM) to account for missing data and repeated measures. For the primary analysis, separate models were used to assess the effects of playing Saturday (first game) vs. Sunday (second game) (fixed effect) on the game average and the 60 s PD for RD, HSR, and wellness data for players competing in both games. For secondary analyses, separate models assessed the effects of playing two games versus one game (fixed effect) on the game average, the 60 s PD for RD and HSR, and wellness parameters. Specifically, the physical demands of players who played only on Saturday vs those who played both Saturday and Sunday for the Saturday game were compared. Likewise, the demands for the Sunday game between players who played both days and those who only played on Sunday were also compared. Individuals were entered as random effects in all the models met for RD, HSR, and wellness variables. The GLMM was used for ACC and DEC since the data were counted. The \(F\) scores (LMM) and \(\chi\) chi-squared test (\(\chi^{2}\), GLMM) were used to report the magnitude of the fixed effect in the model. Contrasts were performed using [PERSON]'s correction for multiple comparisons. Estimates of 95% confidence intervals (CIs) were calculated. The t statistics (LMM) were converted into [PERSON]'s d effect size to report the magnitude of the difference and were interpreted as follows: \(<\)0.20 = trivial; 0.20-0.59 = small; 0.60-1.19 = moderate; 1.20-1.99 = large; and \(>\)2.00 = very large [24]. Significance was set at \(p<0.05\) for all the tests. Analyses were conducted using the JASP package (Version 0.16.4, JASP Team (2022) [Computer software], Amsterdam, The Netherlands).
# Role of 4-Hexylresorcinol in the Field of Tissue Engineering
## 2 Biological Activity of 4-HR and Effect on Tissue Healing
### Inhibition of Foreign-Body Reaction and Acceleration of Biodegradation  
Any biomaterial induces a foreign-body reaction when it is implanted in the body. Macrophages emerge and form giant cells around the biomaterial, inducing a foreign-body reaction and causing giant-cell granuloma formation [27]. 4-HR inhibits the foreign-body reaction and giant cell formation around silk, and accelerates the degradation of silk while decreasing the inflammatory reaction [13; 14]. 4-HR inhibits the expression of diacylglycerol kinase (DAGK) and decreases the giant cell formation in the RAW264.7 cell [14]. DAGK activation is related to phagocytosis and multinuclear cell formation. Moreover, the deficiency of DAGK is related to cell apoptosis and suppression of proliferation [28]. Inhibition of DAGK by 4-HR decreases the foreign-body giant-cell formation, induces macrophage apoptosis, and contributes to graft degradation [14].  
Before forming a giant cell, the macrophage undergoes apoptosis in which new macrophage area is recruited, and this may contribute to biodegradation of the grafted material. A 4-HR-treated silk graft shows increased graft degradation and less giant cells [14]. 4-HR increases the expression of the matrix metalloproteinase (MMP), which is the enzyme for proteolysis [29]. MMPs are expressed in the acute inflammatory phase and late remodeling phase and contribute to wound healing [30]. MMPs are mainly produced from macrophages, and have proteolytic enzymes for extracellular matrix protein [31; 32]. 4-HR increases the expression of MMP-2, 3, and 9 in the macrophage and contributes to the biodegradation of the silk suture (Figure 1a). 4-HR treated silk suture material is grafted in the subcutaneous pocket of rat back skin. A 4-HR-treated silk suture group showed higher expression of MMP-2, 3, and 9, and degradation of graft rather than only-silk suture group (Figure 1b) [13]. Further, 4-HR incorporated into bovine bone shows a more rapid bone degradation than untreated bovine bone [15].
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Software Solutions
#### 3.2.2 Prediction  
Prediction systems have also been taken into consideration thanks to the collection of information by sensors. These systems can be improved by integrating a prediction of availability of parking spaces and improving routes according to the possible flow of vehicles. This will lead to decrease parking time and pollution generated during the search process.  
Whether there are free spaces or routes to them, there are prediction systems such as [15] that use a system to find the best slot for each user depending on the distance from the destination and the cost that is willing to tolerate, repeating the cycle and updating the variables in case the user does not accept the allocated slot.  
There are also reservation systems, as in [79], that focus on current traffic statistics to decide a route and a parking space to be reserved according to the user's current needs. It also takes into account the spaces that are available or are likely to be available at the time of user's arrival.
## 4 Simulation Results
### Error and the Number of Deployed Mobile Nodes  
One hundred mobile nodes whose locations are unknown are randomly deployed in the three dimensional localization region. As we can see, in the process of simulation experiments, the five algorithms could localize all the deployed mobile nodes. Because the angles between task anchors and mobile nodes are calculated using the AOA method, the estimated distances between them have low accuracy and the localization error rises obviously with the number of the deployed mobile nodes increasing. The localization error of PCFL is smaller than that of the other four methods in Figure 13.  
Figure 12: The speed of mobile nodes.  
Figure 13: The number of deployed mobile nodes.  
The average localization error of AUV-aid is bigger than the other four methods. The error variation of the Anchor-based method fluctuates clearly and reaches the maximum value. Compared with \(\mathrm{ACFL}\), PCFL is an effective self-localization algorithm, its localization error changes stable and keeps the smallest in the localization process.