esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrospective Study of the Association between Peri-Implantitis and Keratinized Mucosa
## 4 Discussion
Limitations were also experienced in the collection of initial data since there was no information on parameters such as gingival biotype, millimeters of KM in the area of implant placement, bone conditions or post treatment recommendations that limited the analysis of the evolution of these implants. Other authors, however, did not reach conclusive results on the protective benefit of KM. In the 1990s it became a very controversial issue. Many authors found no difference in plaque control, degree of inflammation or depth of bone defects when comparing implants with and without adequate KM width [14; 22; 23; 24; 25]. These data were supported by further studies, such as that of [PERSON] et al., who could not find in their review the evidence for the need of keratinized tissues around implants to maintain health [26] or [PERSON] et al. [27], who pointed out that KM did not seem to have an influence on the peri-implant bone level. In their 2019 literature review, [PERSON] et al. [28] point out that the absence of KM is a risk factor for the development of peri-implant mucositis although there is some disagreement on the subject. The authors pointed out that there is further inflammation when the KM band is less than 2 mm and the absence of KM is associated with plaque accumulation, inflammation and bone loss support. The results of the present study also associate the absence of KM with deeper pockets (5.1 mm with no KM and 3.3 mm with KM) and a higher percentage of bone loss (39.2% \(\pm\) 21.5 vs. 5.4% \(\pm\) 6.5). [PERSON] et al. [29] pointed out that mucosal thickness plays a major role in the esthetic outcome and peri-implant health. Peri-implant keratinized mucosa has been linked to lower patient esthetic satisfaction and a thicker mucosa can prevent mucosal recession and promote greater stability of interproximal marginal bone levels. [PERSON] et al. [30] concluded that thin gingival phenotype and inadequate keratinized mucosa width (<2 mm) could be risk indicators for peri-implant disease and pain/discomfort during brushing. The association between peri-implantitis and KM has been considered for decades. Although it has not been free of controversy, the most current studies seem to indicate that the absence of KM is one of the causes that favors the development of peri-implantitis. In conducting the present study, a series of limitations related to the different hygiene habits of the patients were encountered. As this was a retrospective study, each patient had different hygiene habits and techniques. Nevertheless, a scarce plaque control in the absence of KM was observed. The present results indicate that the absence of KM is related to the clinical signs associated with peri-implantitis and, therefore, the data obtained support the initial working hypothesis. The amount of keratinized mucosa should be regarded in implant treatment planning just as bone availability is considered.
# HI-Sky: Hash Index-Based Skyline Query Processing
###### Abstract  
The skyline query has recently attracted a considerable amount of research interest in several fields. The query conducts computations using the domination test, where \"domination\" means that a data point does not have a worse value than others in any dimension, and has a better value in at least one dimension. Therefore, the skyline query can be used to construct efficient queries based on data from a variety of fields. However, when the number of dimensions or the amount of data increases, naive skyline queries lead to a degradation in overall performance owing to the higher cost of comparisons among data. Several methods using index structures have been proposed to solve this problem but have not improved the performance of skyline queries because their indices are heavily influenced by the dimensionality and data amount. Therefore, in this study, we propose HI-Sky, a method that can perform quick skyline computations by using the hash index to overcome the above shortcomings. HI-Sky effectively manages data through the hash index and significantly improves performance by effectively eliminating unnecessary data comparisons when computing the skyline. We provide the theoretical background for HI-Sky and verify its improvement in skyline query performance through comparisons with prevalent methods.  
Keywords:database; query processing; skyline query +
Footnote †: journal: Applied Statistics and Machine Learning
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 3 Methods
### Atl03
#### 3.3.2 Standard Deviation Model  
The standard deviation model uses the same characteristic of the significant wave height as ATL12, that is, \(\text{SWH}=4\sqrt{m_{0}}\), and forms a baseline model for the more complex histogram model that assesses the SWH directly from the individual waves. This method uses the same filtering and spacing of the segments as the histogram model and can therefore be used to assess the histogram approach. While this method uses the same approach as ATL12, the filtering and sequencing process is the same as the filtering and sequencing process of the histogram model; the limit on the number of waves is replaced by a limit of at least five photons per 10 m segment.
## 4 Parametric Analysis
### Effects of Deviator Spacing  
Based on the reference model, the deviator spacing \(S_{\rm d}\) is set to range from 0.4 m to 19.6 m at intervals of 0.4 m. A total of 49 analysis models are built to discuss the effects of deviator spacing on the structural behaviors. The calculated results of tendon stress increments, effective height decrements \(d_{\rm p}\) and flexural capacity are presented in Figure 18. With an increase of deviator spacing, the stress increments are slightly decreased but the decrements of \(d_{\rm p}\) are increased. The results reveal that the second-order effects are significant for cases with large deviator spacing. Along with the variation in \(S_{\rm d}\) from 0.4 m to 19.6 m, the flexural capacity decreases by 6.7% under one-point loading states and 10.8% under uniform loading states. The increasing \(S_{\rm d}\) causes a decrease in flexural capacity.
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 3 Double-Layer CS Framework-Based DOA Estimation
### DOA Estimation by DCS-DOA  
Under the double-layer CS framework, the received data samples from each array sensor can be regarded as a measurement vector, and we have \(H\) measurement vectors in total at the FC. For DCS-DOA, the DOA estimation results are obtained in two stages. In the first stage, the raw sparse vectors are required to be recovered from the received data samples by sparsity-based techniques. In the second stage, DOA estimation is implemented according to the recovered sparse vectors. The details of the two stages are presented respectively in the following two subsections.
## 3 Results and Discussion
### Application in Research and Industry  
The method of pit point detection based on the conjugate lines proposed in this paper can be applied to image-based measurement. In this paper, after the overlapping and adhesion, coal and gangue targets were segmented, the centroid of the targets could be calculated by the segmented binary image, and the location could be completed. As shown in Figure 1, after the target was positioned, the gangue was able to be separated by injection through the nozzle. In addition, the segmentation of overlapping and adhesion target areas was the basis for further material identification and judgment. The algorithm in this paper can be applied not only in coal and gangue separation on the basis of photoelectric technology but also in the product segmentation and positioning of other optical separations, for example, in the field of sorting and recycling crops, ores, garbage, and scrap metals.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 4 Proposed Image Compression and Encryption Algorithm
### Compression and Encryption Based on IWT and SPIHT  
LWT with SPIHT yields the best results in terms of compression ratio and computational complexity with very good image quality for medical and natural images. The plain image is transformed into integer wavelet coefficients through LWT. The wavelet transform has the capability of placing a large percentage of the total signal energy in the low sub-band of the image. Therefore, the nonlinear inverse diffusion operations in \(Z_{p}\) on lower-band wavelet coefficients provide sufficient security and linear diffusion operations on the remaining coefficients to increase the 0 counts during SPIHT encoding. In this case, we only select the coefficient values in the 64 \(\times\) 64 upper-left corner. Among them, we transformed two-byte positive \((0-65535)\) values through nonlinear inverse operations. However, unfortunately, 65536 is not a prime number. Hence, \(Z_{65536}\) is not a field and does not have a multiplicative inverse. Therefore, we choose the next prime, which is \(Z_{65537}\), for taking the inverse. For the two bytes of the wavelet coefficient that is represented by m, the inverse operation can be defined as \(I=m^{-1}mod\left(2^{16}+1\right)\). After the diffusion process, these coefficients were permuted  
Figure 5: Joint image compression and encryption process.  
via a high-dimensional Kd-tree. These permuted and diffused coefficients were subjected to SPIHT for encoding.
# Study of Gravelly Soil Core Material Using a Large-Scale Triaxial Wetting Test
## 1 Introduction
Firstly, accurately measuring volume deformation during experiments can be challenging, especially in large-scale tests. Furthermore, the evolution law of the wetting direction itself is very complex. The behavior of volume deformation during the wetting process may vary in different soil types and experimental conditions [25]. Therefore, based on numerous experimental studies, many wetting models have been proposed on the basis of [PERSON]'s model [26; 27; 28; 29; 30]. [PERSON] et al. [31] (2023) established a P-Z wetting model based on the elastic-plastic theory and the wetting model formula, and the BP artificial neural network was introduced to establish an artificial neural network wetting deformation prediction model. [PERSON] et al. [32] (2023) proposed that the lateral wetting strain of high-liquid-limit red clay can be divided into two types, the lateral wetting strain caused by humidification and the lateral wetting strain caused by the water absorption of expanding minerals, and established a wetting strain peak prediction model of high-liquid-limit red clay. Systematic experimental investigations of the wetting behavior of gravel core wall material have rarely been reported, compared with vast experimental research on rockfill materials. Therefore, it is not clear whether or not the observed behavior of rockfill materials can be extrapolated to gravelly soil core material, and the applicability of wetting models to gravelly soil core material is unknown. This research study investigated the wetting behavior of the gravelly soil core material of a dam using large-scale triaxial wetting tests. Based on the results of the wetting tests, the direction and mechanism of wetting deformation are discussed. To conclude, a new constitutive model of wetting deformation is proposed.
## 2 Methodology and Model Assessment
### Imperialist Competition Algorithm (ICA)  
The method of imperialist competition algorithm (ICA) was firstly developed by [47] and later expanded in different engineering subjects by other researchers (e.g., [PERSON] and [PERSON] [48]). Its process is very similar to many other evolutionary algorithms such as PSO and genetic algorithm (GA). The first step in ICA is candidate solutions (or initial population). This step in ICA gets started with a number of countries. The number of countries will be separated into two main groups, namely imperialists and colonies. In these groups, the imperialists are some of the strongest countries where the colonies are the remaining countries (Figure 4). Note that colonies are distributed between the imperialists to make the empires. The distribution of the colonies is based on their own relative strength. As in the ICA, each one of the emeries competes with others to govern more colonies and therefore expand their territory. At the end of this imperialist competition looping, stronger empires will take possession of weaker colonies located in weaker empires. The process will stop after being satisfied by pre-defined termination criteria. Description of the ICA algorithm including its designed steps is well described in other studies. The algorithm of ICA is presented in Figure 5.  
In the literature, for enhancing the efficiency of the ANN-based algorithm by the use of novel optimization algorithms, many works have been performed. In the case of solving engineering issues, algorithms like genetic, ICA, and PSO algorithms can be selected as the new optimization approaches. Moreover, in order to improve the defect of the ANNs, many optimization algorithms have been tested. The optimized finding method of ANN algorithm can cause undesirable solution because the backpropagation approach is a local searching method, for example, searching via the training algorithm. For finding an appropriate weight along with bias of the network and for enhancing the efficiency, different optimization approaches can be selected. Because optimization algorithms can discover a global minimum in comparison with backpropagation-based neural networks, one can find a higher probability of finding more appropriate convergence in these methods. Hence, by taking advantage of hybrid methods like PSO-ANN and ICA-ANN, the ANN weakness in the case of finding the global minimum is removed for example enhancing its searching characteristics by fitness functions and also cost functions.  
Figure 4: Imperialist competition algorithm main procedure to take possession of a weaker colony.
## 5 Numerical Simulation
### Stress Ranges of Suspenders Resulting from Individual Wind Load  
Considering the main focus of the present study is to predict the fatigue life of the suspenders, only the stress responses of the suspenders are presented and the discussion on dynamic responses of the bridge structure are not included herein. In this section, the stress responses of the representative suspenders resulting from individual wind load were first investigated, as shown in Figure 8. For better illustration, the stress responses for a duration of 400 s under the wind speeds of 2.0 m/s, 10.5 m/s, and 20 m/s are presented. For all the four representative suspenders, the variation of stress responses under lower wind speeds is much lower than that under higher wind speeds. Taking the suspender S1 as an example, the standard deviations of the stress responses under wind speeds of 2.0 m/s, 10.5 m/s, and 20 m/s were 0.07 MPa, 2.10 MPa, and 4.45 MPa, respectively. It is obvious that as the wind speed increases, the variation of the induced stress responses increases remarkably. Since the variation of the stress response relates to the stress range that directly determines the fatigue life, high winds are expected to be much more destructive in terms of fatigue reliability than the low winds.  
As the prerequisite for estimating the fatigue life, the stress range values and number of cycles of all four representative suspenders were obtained through the rain-flow counting method, as listed in Table 6. It is worth noting that the stress range cutoff levels have a strong effect on the number of cycles. In the present study, 3.45 MPa was chosen as the cutoff level for stress range to calculate the number of cycles, as the contribution of stress ranges less than 3.45 MPa can be neglected [35]. The results in Table 6 show that both the stress ranges and the number of stress cycles for all the selected suspenders under wind speed of 2.0 m/s were equal to 0, indicating that the contribution of \(U=2.0\) m/s to the fatigue damage of suspenders can be neglected. In addition, both the stress range and the number of cycles increase quickly with the increase of wind speed in general, showing that high wind speeds could cause much more significant fatigue damage to the suspenders than low wind speeds. This finding is consistent with the finding drawn from Figure 8.  
Figure 8: Segment of stress response time histories of representative suspenders under various wind speeds.
# Performance Analysis in Heterogeneous Networks with Spatiotemporal Traffic and Scheduling
## 2 System Model
After the user receives the signal from the BS, if the instantaneous SIR exceeds a predefined threshold, the user sends an acknowledge character(ACK) through the error-free feedback channel, and the BS deletes the data packet from its buffer; otherwise, the BS receives negative acknowledge(NACK). The data packet remains at the head of the queue and waits for the next transmission opportunity. Figure 1 depicts a two-tier heterogeneous network with spatiotemporal model.The disc is used to indicate the coverage of the base station wireless signal. Among various scheduling schemes, RS and RR are the two popular policies from which operators may choose for resource allocation among users as their implementation costs are low [9]. We assume that \(K_{1}\) tiers use RS and \(K_{2}\) tiers use RR \((K_{1},K_{2}\in\{0,1,\cdots,K\},K_{1}+K_{2}=K)\). * _Random Scheduling (RS)_: BS randomly selects a user from its cell to serve during each time slot. * _Round-Robin (RR)_: The BS schedule users in a sequential order in a cell.
## 3 Database-Building and Exploratory Analysis  
### Database-Building  
Underground engineering problems are complicated and diverse. In this section, this paper concentrates on the factors influencing the uplift of the underlying tunnel caused by the excavation. Figure 3 shows the relative position between the excavation and the existing tunnel. A database with 170 samples was built by investigating the existing engineering projects, and it was divided into a training set and a test set, with the ratio of 7:3.  
The relationship between overlying the excavation-induced tunnel uplift and the influencing factors is particularly important for predicting the tunnel uplift. Geological  
Figure 3: Relative position of the excavation and the tunnel: (**a**) bird’s eye view, and (**b**) sectional view.  
Figure 2: The overall analysis process of the XGBoost model based on Bayesian optimization.  
properties are usually essential for determining a construction program. The samples in the database were categorized into four classes according to the properties of the soils in which the existing tunnels were located, namely, muddy silty clay (Mud), soft clay (Sof), silty clay (Sil), and gravelly clay (Gra). The size of an excavation, including its length (L), width (B), and depth (H\({}_{\rm e}\)), directly affects the unloading force exerted on the soil above the tunnel, which, in turn, affects the maximum uplift of the tunnel (\(S_{\rm max}\)). Other factors include the radius of the shield tunnel (D), the distance between the tunnel vault and the base (H\({}_{\rm t}\)), and the actual underpass length of the tunnel underpass (L\({}_{\rm c}\)). Additionally, the control measures adopted in a project that control the disturbance of the soil by the excavation cannot be overlooked. They are, respectively, the excavation enclosure structure (SWM, DW, and BP), the internal support structure (OC and NC), and other excavation control measures (SM\(\_\)1, SM\(\_\)2, SM\(\_\)3, and SM\(\_\)4), respectively. It is worth noting that some of these factors are non-numerical variables, such as the soil mixing wall (SWM) in the excavation enclosure structure, which are quantified using binary values (0 and 1), with \"1\" indicating that the effect of the variable is considered and \"0\" indicating otherwise. Table 1 presents the definitions, ranges, and detailed descriptions of each variable.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 3 Methodology
### Local Obstacle Avoidance Optimization Based on the Improved Artificial Potential Field Method  
Considering the complex and diverse environment of the map with numerous obstacles, when the robot encounters a situation where the target point is too close to the obstacle, the problem of the target being unreachable may occur. In this paper, the repulsive field function is further optimized and improved by introducing a repulsive force correction term \(A_{t}\) to solve the unreachable target problem.  
\[A_{t}=\gamma\frac{X_{i}-X_{p}}{\left|\left|X_{i}-X_{p}\right|\right|^{\sigma}} \tag{16}\]  
Among them, \(\gamma\) is the correction coefficient, and \(\sigma\) is the exponential parameter used to adjust the sensitivity of the target guidance to the distance change. The closer the distance to the obstacle, the stronger the correction effect. In the path planning of a multi-mobile robot system, it is necessary to consider the influence of obstacles on the robots and avoid collisions with other mobile robots during the movement. Therefore, this paper further constructs a local repulsive field \(F_{rrob-local}\) between robots.  
\[F_{rrob-local}=\sum_{j,z}^{m}\rho(t)\cdot(\frac{1}{\left|\left|q_{i}-q_{j} \right|}-\frac{1}{d_{s}})\cdot\frac{q_{i}-q_{j}}{\left|\left|q_{i}-q_{j}\right| \right|^{3}} \tag{17}\]  
Among them, \(q_{i}\) and \(q_{j}\) are the current positions of a robot \(i\) and robot \(j\), respectively. \(d_{s}\) is the safety distance threshold. The repulsive force is triggered when the distance between the two robots exceeds this threshold. The dynamic repulsive gain coefficient \(\rho(t)\) changes with time. When the robot moves too fast, or the environment is complex, the value of \(\rho(t)\) will be increased to enhance the repulsive effect. In a stable environment, the value of \(\rho(t)\) will be decreased to avoid the interference of the repulsive force on the normal movement of the robot and improve the operation efficiency.  
The formula for the total local repulsive force after fusion is:  
\[F_{rep-local}=F_{rrob-local}+A_{t}+F_{rep-local} \tag{18}\]  
The resultant force \(F_{APF-local}\) of the local potential field is:  
\[F_{APF-local}=F_{rep-local}+F_{att-local} \tag{19}\]