esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Most patients with tooth loss opt for dental implants to restore full dentition. A key step before implant placement is the recovery of resorbed bone and filling the empty socket, which has led to increased interest in guided bone regeneration (GBR) procedures. Both biodegradable and permanent membranes are used for this purpose, with fourth-generation biomaterials standing out due to their dual role in providing mechanical barriers and stimulating biological processes like osteoblast proliferation and biomineralization. The goal of this study was to develop GBR membranes through electrospinning that not only meet the basic requirements for dental materials but also surpass current resorbable and non-resorbable options. Theoretical studies were conducted to determine optimal process parameters for nanofibrous PLA-based materials containing periclase nanoparticles and BMP-2 growth factors. Our study revealed that the most favorable samples in terms of nanofibers alignment, additives distribution, and membrane integrity are obtained using 25 kV and a distance between the needle and collector of 10 cm. FT-IR spectroscopy confirmed their composition, and swelling tests in various media, along with antibacterial testing against two bacterial strains, were successful. Furthermore, cytotoxicity studies on the MG-63 cell line demonstrated good biocompatibility and enhanced proliferation activity. These promising results suggest that the developed biomaterials exhibit reproducible, desirable properties and warrant further investigation for GBR applications.  
Conceptualization, A.S.-B. and J.R.-P.; methodology A.S.-B., J.R.-P., N.R.-P. and L.J.; validation, A.S.-B., J.R.-P. and L.J.; investigation, A.S.-B., J.R.-P., L.J., N.R.-P., K.L., T.G. and P.R.; data curation, A.S.-B. and J.R.-P.; writing--original draft preparation, A.S.-B. and J.R.-P.; resources: L.J., [PERSON]-P., M.T. and [PERSON]-P. supervision, M.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by This research was funded by Ministry of Education and Science, TRL 4.0 Grant number MNiSW/2020/344/DIR.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
Figure 14: Microscopic images of (a)—sample 5 and (b)—sample 6.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 4 The Proposed Protocol
### A Protocol Overview  
Our proposed protocol consists of two major components: efficient traffic data representation and delay-controlled data dissemination. For efficient data representation, network edges are proposed to monitor the traffic for indicating events and conditions. These conditions are shared among vehicles through a delay-controlled data dissemination component. Network edges are involved in the dissemination of safety and convenience messages in a vehicular environment for an improved performance, connectivity and trust. By integrating edges, data can be disseminated with high delivery ratio and low latency to relatively faraway distances. Such a performance enables the edge to provide an ideal platform to fulfill the requirements of different types of vehicular networking applications. In addition, vehicles with no internet access can be warned of a potential crash by connecting to the nearest edge, even if VANET is sparsely connected. To overcome the limited computation power of the edge, decisions can be made with the aid of the vehicular cloud when necessary.  
In the remaining of this section, we describe the problem which we are proposing a solution for. Then, we explain the role of edges in traffic condition detection and representation. After that, we provide a description of how vehicles control their broadcasting delays according to the traffic condition for efficient data dissemination. Lastly, we explain how our protocol can be integrated into delay-tolerant applications.
# Factors Influencing Appliance Wearing Time during Orthodontic Treatments: A Literature Review
## 4 Results and Discussion
### \"Appliance-Related\" Factors
The improvements introduced over the past few years regarding clear aligner therapy have made this approach comparable with fixed conventional treatments in selected cases (such as the intrusion and rotation of incisors, lingual crown tip, the distalization of upper molars, dental cross-bite, etc.) [47; 48; 49]. It has been recently reported that complex and even surgical treatments can be successfully completed with clear aligners, but more studies are needed to retrieve solid conclusions [50]. A very good compliance is necessary to obtain successful results with clear aligner therapy [36; 41], as the appliances should be worn 22 h per day and changed sequentially every 7, 10, 14 days [51]. An insufficient compliance may increase the treatment time and therefore may compromise the treatment outcome [35]. In order to improve the efficiency of the treatment, it is important for the clinician to understand the optimal aligner wear protocol: [PERSON] et al. [51], in 2021, evaluated the efficacy of tooth movement with aligner wear protocols of 7, 10, and 14 days. They found out that a 7-day protocol reduces the treatment duration and is generally enough, as there is not a clinically significant difference compared with a 10-day or 14-day protocol. A 14-day protocol showed a statistically greater accuracy for complex posterior movements such as maxillary intrusion, distal-crown tip, buccal-crown torque, and mandibular intrusion and extrusion [51]. The compliance issue is critical to obtain results and to select accelerated wearing schedules. It should be carefully evaluated by the clinician and thoroughly discussed with the patient and/or with the parents when talking about kids' treatment. If the patient shows a good approach and the adherence to the treatment is foreseen, the clear aligners are an excellent treatment option with effective results, even in difficult cases. A fair compliance could be sufficient in mild malocclusions, but certainly not in complex cases. A poor compliance may cause the failure of any treatment.
# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 4 Experimental Results
### A Sweeping Path Accuracy Method Based on Situation Analysis and BF-CPP + SA
#### 4.3.3 A Comparative Study of Scenarios with Different Sparsities  
Considering the existence of real scenarios with different garbage heap sparsity, we set up a set of experiments to verify the performance of the BF-CPP and SA algorithms. Among them:  
(1) Ten situations where pavement garbage is clustered but overall sparse.  
Figure 15: Performance of BF-CPP and SA algorithms combined with obstacle avoidance algorithms. where the white irregular box indicates the area to be cleared, the yellow irregular box indicates the complex garbage pile conforming to the clearing rules, the blue irregular box indicates the complex garbage pile whose height exceeds the threshold, the black rectangular box indicates the static obstacles, and the gray rectangular box indicates the prediction of the motion trend of the dynamic obstacles.  
2. Ten scenarios with a balanced distribution of pavement garbage.  
The scene schematic is shown in Figure 16, where the mean \(s\) and variance \(\sigma\) of the distance between obstacles are used to denote the scene sparsity. We set up simulation experiments to verify the performance of the A star algorithm, CPP algorithm and BF-CPP + SA algorithm under two scenarios, respectively. The experimental results are shown in Tables 3 and 4. According to the experimental results, we found that the A star algorithm can also work well in the case of roadway trash aggregation but overall sparseness, but it is not satisfactory in the case of balanced distribution of roadway trash. The CPP algorithm has better coverage, but the energy loss is serious. After a comprehensive analysis, BF-CPP + SA is found to be more suitable for all kinds of complex situations.
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## Case Study
### Analysis of Influencing Factors of Travel Time
#### 4.3.1 Effect of Traffic Accidents on Travel Time  
In order to reveal the effect of accidents on travel time, we used the BP, SVM, and the optimized SVM model to predict travel time from Changsha toll station to Guanshan toll station on G5513. One traffic accident took place at 15:40 and ended at 16:34 on 7 th February 2018. The accident site located between these two toll stations. Travel time is listed in Figure 6, which includes the actual value, BP prediction value, SVM, and optimized SVM prediction value.  
Figure 6: Travel time during traffic accident.  
From Figure 6, travel time increased by 70% at 16:00 (from 50 min to 85 min), and increased by 230% at 17:00 (up to 165 min). The figure indicates that accident can influence travel time greatly.  
The prediction results also indicate that the optimized SVM model is superior to the BP neural network and SVM model in terms of the CP. In the prediction of Changsha West-Guanshan, the CP of the optimized SVM model is higher than those of BP model and SVM model by 21.40% and 7.28% respectively. In the prediction of Changsha West-Ningxiang, the CP of the optimized SVM model is higher than those of BP model and SVM model by 10.9% and 6.53%, respectively. The results proved that the optimized SVM model has better inclusiveness and stability when unexpected factors such as traffic accidents, thereby avoiding the need for repeated trial and error to address network problems.
## 4 Experimental Results and Discussion
### Analysis of Specimen Failure Progress Caused by Transverse Deformations  
To analyze the entire process of longitudinal crack development, frame by frame images were used (Figure 10). According to [6], this process is divided into three stages. In the first stage, the concrete is exposed to tension until the initiation of longitudinal cracks, when \(\varepsilon_{trans}\leq\varepsilon_{ct\;ul}\) (Figure 10a), where, according to the results presented in Table 3, the average value of \(\varepsilon_{ct\;ul}\) for three the specimens is equal to 0.095%. In the second stage, when \(\varepsilon_{ct\;ul}<\varepsilon_{trans}\leq 2\varepsilon_{ct\;ul}\) (that is, transverse deformations develop until they reach a value of about 0.2 % (Figure 5)), there is the process of longitudinal crack initiation and development (as shown in Figure 10b). At the selected loading rate, this stage continues for about 6 seconds. The crack propagation process ends with specimen failure, when \(\varepsilon_{trans}>2\varepsilon_{ct\;ul}\) (Figure 10c). The obtained longitudinal crack development pattern corresponds to the deformation process and failure in the columns. For example, a similar behavior in the reinforced concrete columns of residential building was observed during the Mexico earthquake in 2017 [6].  
Crack propagation in the specimens occurs parallel to the compressive force direction that proves deformation development in the transverse direction, which is caused only by pure tension due to the cylindrical form of the specimens. As a result, the longitudinal crack development process in specimens leads to their separation into several vertical wedge-shaped segments and an unbroken circular inner core, which was theoretically predicted earlier [6].  
The following important investigation step is to analyze the specimen failure type after unloading (Figure 11). Following the structural phenomenon [13], it can be theoretically predicted that the cross-section area of the destroyed specimens is approximately half that of the initial one. Experimental studies confirmed this prediction for all of the tested specimens. The area of the inner core A and of the outer segments B were formed on the upper surface of all of specimens at failure (see Figure 11).  
Figure 10: Typical crack propagation in the investigated specimens: (**a**) End of stage 1–crack development initiation; (**b**) Stage 2–crack development process; (**c**) Stage 3–specimen failure.  
The ratios between the entire cross-section area of the specimens (A + B) and that of the inner core A are presented in Table 6. The obtained data show that the average ratio is close to 2, which confirms the structural phenomenon [13].  
Thus, the typical failure pattern of a compressed concrete specimen is presented in Figure 12.
# Open Loop Position Control of Soft Hydraulic Actuators for Minimally Invasive Surgery
## 1 Introduction
As part of our work to use soft robotic methods with the _Cyclops_ robot, we have developed a soft hydraulic actuator inspired by the pneumatically actuated 'Pouch Motors' described in [19; 20]. One advantage of our approach is that it is possible to control the actuator length by controlling the volume of the incompressible hydraulic working fluid. In [21], it is stated that the minimum volume of this form of actuator is zero, which they note is less than other pneumatic actuators such as McKibben actuators [22], Baldwin type [23], and pleated pneumatic artificial muscles (PAMs) [24]. Our soft hydraulic actuators are a good choice for MIS and other potential applications because the actuators have a low-profile form factor, they avoid the friction effects of Bowden tubes, and open loop length control can be achieved with a simple method. Furthermore, the actuators can be manufactured very rapidly and economically. In [25], a pneumatic pouch motor style actuator with extra internal and external constraints was designed that increased the theoretical maximum contraction ratio from 0.363 of [19] to 0.553. However, the construction of these actuators is more complex, and in this work, we use a pulley to double the stroke of our actuator. Pneumatic actuation causes actuators of this type to act as non-linear springs whose equilibrium position when pressurised is the actuator length at maximum contraction. Changing the air pressure in the actuator changes the stiffness. A difference with hydraulic actuation is that the actuator cannot be extended like a spring if a tension force is exerted on it, assuming that water is incompressible and the membrane material is inextensible. Instead, the pressure will increase as the external force acts to reduce the cross-sectional area of the chamber. Electrohydraulic actuators are described in [26; 27] that use dielectric elastomer actuators (DEAs) to pump liquid dielectric within their pouches to contract. These actuators achieve rapid actuation and the capacitance of the DEAs can be measured to sense their deformation. However, because there are no hydraulic supply tubes, the pressure cannot be monitored as the actuator contracts. Furthermore, biocompatibility is a problem because they are filled with liquid dielectric oil and actuated by voltages between 6 and 13 kV. We have developed soft actuators with the aim of approaching these disadvantages and of building on the work in [9] to produce an entirely deployable cable-driven robot. The actuators have accurate and highly repeatable performance despite their simple manufacture and control. This has been shown by using the actuators in a variety of configurations: from simple one degree of freedom (DOF) tests to planar parallel mechanisms, a three DOF hybrid parallel mechanism, and finally a proof-of-concept deployable hybrid parallel robot with inflatable structure.
## 4 Experiments
### Comparative Experiments
The heatmaps of RT-DETR, on the other hand, display high-intensity responses in localized areas, reflecting its advanced ability to detect defects with strong cues. However, RT-DETR's focus may exhibit slight fragmentation in certain complex scenarios, potentially leading to incomplete detection of diffuse defect patterns. Despite this, its heatmaps still showcase a robust capacity for highlighting defect areas, albeit at a cost to inference speed. Consequently, PerMSCA-YOLO stands out as a model that not only maintains balanced focus across diverse defect scales and shapes but also preserves real-time detection capability, thereby offering a comprehensive performance advantage for track defect detection. To comprehensively evaluate model performance, Figure 13 also shows the Precision-Recall (PR) curves of all models across varying recall ranges, along with comparisons of [EMAIL_ADDRESS] during training. The PR curves indicate that PerMSCA-YOLO maintains a high precision even at large recall values, demonstrating superior robustness in handling small targets and complex background noise. Furthermore, in high-recall regions, it effectively reduces false positives, ensuring comprehensive detection of defects. From the training curves, PerMSCA-YOLO's mAP continues to improve and becomes stable over more extensive iterations, whereas YOLOv9t, YOLOv8n, and other models show limited accuracy gains at later training stages. Although RT-DETR can also achieve competitive detection accuracy, its computational cost during training and inference is relatively high. If the model is deployed in environments requiring high real-time performance, its low inference speed may pose a bottleneck. In object detection tasks, particularly in track defect detection, both precision and recall are critical. Although YOLOv10n has an advantage in FPS, its lower [EMAIL_ADDRESS] and F1-score reveal its inadequacies when handling small cracks and irregular surface textures on tracks. In contrast, PerMSCA-YOLO not only optimizes detection accuracy but also achieves efficient detection with reasonable inference speed, making it especially suitable for real-world scenarios requiring both real-time performance and precision. Across different confidence thresholds, PerMSCA-YOLO demonstrated a superior balance between precision and recall. This advantage is particularly important because track defect detection  
Figure 12: Heatmap comparison between different models. typically involves various defect types, with significant variability in defect size and form. Through a comparative analysis of [EMAIL_ADDRESS] and F1-score, PerMSCA-YOLO consistently maintains high detection accuracy while ensuring a solid recall rate, thus sustaining stable detection performance across diverse and complex track environments.
## 3 Results and Discussion  
### The PTML-MLP Model  
By following the procedure described in the previous subsection, the most appropriate PTML-MLP model found by us was the one with the notation MLP 15-73-2. This means that our PTML-MLP model used 15 input nodes (equal to 15 \(D[GBI]ej\) descriptors present within it). The 15 \(D[GBI]ej\) descriptors present in the PTML-MLP model are depicted in Table 1. In the PTML-MLP model, there are also 73 hidden neurons. At the same time, the number two refers to the two output neurons used by the PTML-MLP model; each of them predicted each of the categorical values of activity [_ALC(e)_ = 1 and _ALC(e)_ = \(-\)1]. It is important to highlight that both the hidden and output layers used hyperbolic tangent as the activation function. The aforementioned explanation regarding the notation of the PTML-MLP model also means, that, according to Equation (3), \(I=15\), \(H=73\), and \(O=2\). Also, the number of training cases (\(T\)) has a value of 5535 (see Supporting Information S1). That yields a value of \(\rho=4.21\). This \(\rho\) value is larger than the cutoff \(\rho>3\) reported in the previous subsection. Consequently, our PTML-MLP model is not overfitting the data.
## 6 The Evaluation of the Network Availability for WDM-PON Protection Schemes  
Using the PON Network Availability Evaluator, the total network availability for considered scenarios of WDM-PON protection schemes can be evaluated and compared. Resultant network availabilities (Figure 6 and Table 4) are calculated for specific parameters' values from the data resource [7] and for selected restoration times. Possible changes of network availabilities are depending on a type of the architecture, on a specific protection scenario and on the restoration time.  
Figure 5: The main screen’s part for the protected RING metro-access WDM-PON network.  
Figure 6: The total WDM-PON network availability for selected restoration times.
## 3 Results
### Observed Time (OT) Analysis  
In the OT analysis, there were no significant interactions between condition and time for distance (F(10, 90) = 0.947, \(p\) = 0.449), speed (F(10, 90) = 1.766, \(p\) = 0.078), HR (F(10, 90) = 1.613, \(p\) = 0.199), or VO2 (F(10, 90) = 2.542, \(p\) = 0.067) (see Figure 2A, Figure 3A, Figure 4A,C and Figure 5A). However, there was a main effect of time for all variables (VO2, F(5, 45) = 18.391, \(p\) < 0.001; distance, F(5, 45) = 14.878, \(p\) < 0.001; speed, F(5, 45) = 15.683, \(p\) < 0.001; HR, F(5, 45) = 98.880, \(p\) < 0.001). In addition, there was a main effect of condition for distance (F(2, 18) = 134.821, \(p\) < 0.001).  
Figure 5: RPE (Borg scale 6 to 20) in the (**A**) observed time (OT) and (**B**) real time (RT) analysis. Mean (\(\pm\)SD) data for RPE for each condition with respect to (**A**) OT (every 10-min period of the observed time) and (**B**) RT (every 10-min period of the real time). Notes: * Significant differences between FC (18) and both NC (17) (\(p\) = 0.030) and SC (17), \(p\) = 0.015.
## 5 Experimental Setup
### Evaluation Metrics  
In the experiments, we utilize the mean reciprocal rank (MRR) and Hits@n [16] as metrics to evaluate the results. Specifically, the reasoning results of the proposed method are multiple entities sorted in descending order of probabilities, so the metric Hits@n means the proportion of gold standard entities in the top n of the predicted results. MRR measures the average of the reciprocal ranks of all the testing data. These methods are widely used in various knowledge graph reasoning studies [42, 45].