esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The extraction of mandibular impacted third molars is one of the most common procedures in oral surgery. One of the extraction methods consists of the preparation of a buccal bone lid to obtain access and remove the tooth. This technique aims to reduce bone loss, promote bone healing distal to the second molar, and increase the accessibility to the impacted tooth, particularly in cases of concomitant pathologies or proximity to the mandibular canal. The procedure benefits from the application of 3D printing technologies to produce cutting guides that, combined with piezosurgery, allow deeply impacted third molar extractions with excellent preoperative planning and extreme localisation precision, as well as predictable repositioning and fixation of the bone lid. The objective of this article is to present two clinical cases of the application of the bone lid technique to extract deeply impacted third molars using 3D printed guides with different designs and piezosurgery.  
bone lid; 3D printed guide; piezosurgery; wisdom tooth extraction; CAD-CAM; osteotomy 00000-0000-0000-0000-0000-00000  
cases of the reduced stability of the bone lid, various fixation devices have been proposed, such as plates, screws, osteosynthesis wires, sutures, or acrylic adhesives [19; 20; 21; 22; 23].  
The technique has been demonstrated to be advantageous in terms of bone healing, allowing, in many cases, implant placement in the healed site if implant therapy is required [24; 25]. Indeed, this conservative technique, apparently more complex than a traditional osteotomy-based approach, enables the maintenance of the alveolar bone, thus minimising or eliminating the need for subsequent bone augmentation procedures.  
In the context of personalised dentistry and minimally invasive oral surgery, digital planning and 3D printed surgical guides are becoming essential tools to perform accurate and predictable osteotomies in terms of location, angulation, and cutting depth. The application of computer-assisted osteotomy guides has been associated with favourable outcomes for several indications, including the removal of alveolar bone pathologies [26], orthognathic surgeries [27], corticotomy [28], tooth extraction [29; 30; 31], inferior alveolar nerve lateralisation [32], bone harvesting [33], and apical surgery [34].  
The aim of this report is to describe two cases of impacted mandibular third molar extractions, approached with the computer-guided bone lid technique performed using piezosurgery and two different stabilisation methods for the repositioned bone window.
###### Abstract  
The integration of the Internet of Vehicles (IoV) and low-orbit satellite Internet not only increases the efficiency of traffic management but also contributes to the emergence of new cyberattacks. Spoofing interference occupies a special place among them. To prevent a rogue satellite from imposing unauthorized content on vehicle owners, a zero-knowledge authentication protocol (ZKAP) based on a modular polyalphabetic polynomial code (MPPC) was developed. The use of MPPC allowed for increasing the authentication speed of the satellite performing the role of RSU. As a result, a reduction in the time needed to guess the prover's signal also reduces the probability of granting a rogue satellite the communication session and increases the imitation resistance of the satellite IoV. At the same time, the MPPC allows for improving the noise resistance of the ZKAP. An algorithm for calculating the control residuals for a noise-resistant MPPC was developed for this purpose, as well as an algorithm for correcting errors arising in the communication channel due to interference. Thus, the developed authentication protocol based on a noise-resistant modular code allows for simultaneously reducing the probabilities of the first-order and second-order errors, which leads to the increased cybersecurity of satellite IoV.  
authentication protocol; IoV satellite network; VANET; modular polyalphabetic polynomial code; error detection and correction algorithm; noise resistance +
Footnote †: journal: _Verticle Journal_  
_Article_
## 4 Experiments
### Implementation Details and Evaluation Criteria  
The present mainstream framework PVTv2 was selected as the feature extractor for the experiment. PVTv2 is less computationally complex than other networks such as Swin [52] or Pyramid Vision Transformer (PVTv1) [49]; yet, it provides better performance in computing tasks. The backbone of the proposed HFNet was the PVTv2-b5, which was pretrained in Imagenet-1K [60]. The Adam optimization model with a weight decay of \(5\times 10^{-4}\) was used, the batch size was set to 24, the learning rate was set as \(1\times 10^{-4}\), and there were 16 epochs.  
[PERSON]'s rank order correlation coefficient (SRCC) and [PERSON]'s linear correlation coefficient (PLCC) were adopted to measure the prediction monotonicity and the prediction accuracy. The SRCC is computed as:  
\[SRCC=1-\frac{6\sum_{i=1}^{N}di^{2}}{N(N^{2}-1)}, \tag{10}\]  
where \(N\) represents the image number in the testing database, and \(d_{i}\) is the difference between the \(i\)-th image's ranks in the objective and subjective evaluations. The PLCC calculates the accuracy between the predicted scores and the ground truths, which is defined as:  
\[PLCC=\frac{\sum_{i}(q_{i}-\overline{q})(s_{i}-\overline{s})}{\sqrt{\sum_{i}(q_ {i}-\overline{q})^{2}}\sqrt{\sum_{i}(s_{i}-\overline{s})^{2}}}, \tag{11}\]  
where \(s_{i}\) and \(\overline{s_{i}}\) are the \(i\)-th image's subjective rating and the mean of the overall \(s_{i}\), respectively; \(q_{i}\) and \(\overline{q}\) are the \(i\)-th image's converted objective score after nonlinear regression and their mean value, respectively. The higher the two indices, the better the performance of the quality metric.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database** & **RIN** & **DIN** & **DTN** & **SST** & **SSR** & **Year** \\ \hline CSIQ [55] & 30 & 866 & 6 & DMOS & [0,1] & 2010 \\ TID2013 [56] & 25 & 3000 & 24 & MOS & [0,9] & 2013 \\ BID [57] & N/A & 585 & authentic & MOS & [0,5] & 2011 \\ LIVEC [58] & N/A & 1162 & authentic & MOS & [0,100] & 2016 \\ KONIQ-10k [59] & N/A & 10,073 & authentic & MOS & [0,100] & 2020 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information of five distorted image databases. RIN: number of reference images; DIN: number of distorted images; DTN: number of distortion types; SST: subjective score’s type; SSR: subjective score’s range.
## 3 Basic Theory  
### Image Segmentation Method  
A convolutional neural network (CNN) is a deeplearning algorithm established to simulate the nervous system. It is a typical case is shown in Figure 1[27]. A CNN generally includes an input layer, convolution layers, descending sampling layers (also known as pooling layers), connection layers, and an output layer.  
The input of the network is every pixel value within an image. A good network model is obtained through the iterative adjustment of its parameters. The convolution calculation of multiple convolutional layers is shown in Equations (1) and (2):  
\[H_{i}=f(H_{i-1}\otimes W_{i}+b_{i}) \tag{1}\]  
\[H_{i}=downsampling(H) \tag{2}\]  
where \(W_{i}\) is the parameters of the \(i\)th layer convolution kernel, \(B_{i}\) represents the offset vector, \(\bigotimes\) represents the convolution calculation and \(H_{i}\) represents the feature map. \(f()\) is the excitation function. \(downsampling()\) is represented by the sampling function below. \(W\) and \(B\) are optimized with each successive iteration. The main frameworks of subsequent networks are primarily formed by the convolution, downsampling and connection layers. The basic networks used in this paper (LinkNet, ResNet, U-Net, and DeepLab) can also be characterized in this manner.
## 3 Results
### DNJ Enhances AKT Activation to Stimulate NRF2  
Protein kinase AKT was reported to be activated by various natural products to protect cells against exterior insults [27]. Herein, we examined whether DNJ treatment enhances AKT activation. As shown in Figure 4A, 50 mmol/L glucose decreased the level of phosphorylated AKT (Ser 473) compared to 11 mmol/L glucose. And DNJ treatment increased the phosphorylation of AKT at Ser473 in cells cultured in media containing 11 mmol/L and 50 mmol/L glucose. These results showed that high glucose inhibited AKT activation. However, DNJ stimulated AKT activation, especially in cells cultured in 50 mmol/L glucose. Next, we checked whether AKT activation contributes to the enhancement of the NRF2 anti-oxidative response due to DNJ treatment. AKT inhibitor MK2206 (final concentration 10 umol/L) treatment suppressed AKT phosphorylation (Figure 4B). In addition, the induced expression of NRF2 and HO-1 by DNJ was significantly inhibited by MK2206, indicating that DNJ stimulates NRF2 signaling through enhancing the activation of AKT. Furthermore, the application of MK2206 inhibited the expression of OGG1 in cells treated with DNJ. Altogether, these results show that DNJ treatment promotes the activation of AKT, and leads to the stimulation of NRF2 signaling, which upregulates the expression of OGG1 to mitigate oxidative DNA damage caused by high glucose.  
Figure 4: (**A**) Western blotting detection of the expression levels of phosphorylated AKT (ser473) in cells cultured in media containing 11 mmol/L and 50 mmol/L glucose, with or without DNJ treatment. (**B**) Western blotting detection of the expression levels of phosphorylated AKT (ser473), NRF2, HO-1, and OGG1 in HUVEC. The cells were cultured in media containing 11 mmol/L and 50 mmol/L glucose. DNJ and MK2206 treatments are indicated in the figure. Three replicates were set for each sample, and each experiment was performed three times. # means the \(p\) value \(<\) 0.05, #\(\#\) means the \(p\) value \(<\) 0.01. One-way ANOVA with Tukey test was used to analyze the significance.
# Fault Classification and Localization Scheme for Power Distribution Network
## 5 Power System Simulation under Study  
To validate our scheme, we used an Eskom power distribution line as a case study. Eskom is a South African power utility responsible for supplying more than 95% of electricity for industrial and household applications. A reduced power system segment of the Eskom distribution line is shown in Figure 6. The network is modeled as a two-substation system. The voltage at the sending end and receiving end of the line is rated at 132 kV voltage supply. The distribution network is modeled as a pi-type system with a total length of 155 km. The modeling of the network is subdivided into small sections to improve model design and efficiency. The substation parameters are depicted in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Apparent Power (MVA)** & **Fault Level (kA)** \\ \hline Substation A & 20 & 55.5 \\ Substation B & 15 & 42.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Substation parameters.  
Figure 5: Fault location taxonomy.  
The parameter value of the resistor \(R\)\((\Omega)\), inductor \(L\)\((\mathrm{H})\), and capacitor \(C(\mathrm{F})\) of the distribution line per kilometer are given in Equations (14)-(16), respectively.  
\[R=\begin{bmatrix}0.15&0.12&0.12\\ 0.12&0.15&0.12\\ 0.12&0.12&0.15\end{bmatrix}\Omega/\mathrm{km} \tag{14}\]  
\[L=\begin{bmatrix}1.12e^{-3}&1.90e^{-3}&6.30e^{-3}\\ 1.90e^{-3}&1.12e^{-3}&1.90e^{-3}\\ 6.30e^{-3}&1.90e^{-3}&1.12e^{-3}\end{bmatrix}\mathrm{H}/\mathrm{km} \tag{15}\]  
\[C=\begin{bmatrix}1.10e^{-8}&-2.85e^{-9}&-6.40e^{-10}\\ -2.85e^{-9}&1.10e^{-8}&-2.85e^{-9}\\ -6.40e^{-10}&-2.85e^{-9}&1.10e^{-8}\end{bmatrix}\mathrm{F}/\mathrm{km} \tag{16}\]  
The power distribution system used in the present work for analysis is modeled in the MATLAB/Simulink platform. The power system could have been modeled in any other power system platform, such as (DigSilent, PLSCAD, and ATPDraw) without any significant difference in the results. Eleven (11) types of faults are initiated at different locations of the distribution line to develop a fault data matrix. The proposed fault classification and location schemes use half a cycle of the post-fault signal. The (LG), (LL), (LLG), and (LLL) fault signals are shown in Figure 7. It is observed from Figure 7 that the current magnitude increases drastically when a fault occurs.  
Figure 6: Power system.  
Figure 7: _Cont._  
Figure 7: Cont.
## 3 Experiment and Application
### Engineering Application: Multi-UAVs Cooperative Path Planning
#### 3.2.3 Multi-Population Track Coding  
The idea of multi-population is combined in this work to construct the multi-UAVs track set. When the algorithm is adopted to perform a search, the number of populations is determined in accordance with the number of UAVs, and the process of UAV searching path is mapped to the updating process of the population. The position of the anas platyrhynchos in each sub-population corresponds to a flying path of the mapped UAV. The optimal solution in the sub-population corresponds to the optimal path of this UAV. The optimal solution of all sub-populations corresponds to the cooperative path of UAVs.  
If all UAVs on a mission are set as \(U=\{U_{i},i=1,2,\ldots,N_{u}\}\), then the corresponding number of anas platyrhynchos population is \(N_{u}\). The number of anas platyrhynchos individuals in each sub-population is set as \(m\), and the anas platyrhynchos individuals in the sub-population can be represented as \(X=\{X_{i},i=1,2,\ldots,m\}\). The position of anas platyrhynchos \(i\) in the search space is \(X_{i}=\{X_{i1},X_{i2},\ldots,X_{in}\}\), which represents the midway point of a path in addition to the starting and the target points. The coordinate of each path point is \(X_{n}=\{x_{in},y_{in},z_{in}\}\). The fitness value of the anas platyrhynchos individual corresponds to the cost function value of a certain path. If the fitness is favorable, then the path is optimal. In the process of searching and comparing the fitness values of individuals in the population, the anas platyrhynchos individuals at the favorable position are obtained as the leading duck. The algorithm is guided to search continuously in the direction with small fitness values, thereby resulting in many cooperative paths.  
When the APO algorithm is initialized, a group of anas platyrhynchos is randomly generated in the region of search. When an individual's three-dimensional position coordinates are randomly generated, the computational load is large, and the search efficiency is low. Therefore, in this work, an equal division was performed for the coordinates of individuals in the direction of the \(x\)-axis based on the number of the waypoints set at initialization. In the searching process, the positions in the \(y\)- and \(z\)-axes were updated. Boundary control was performed when the position was updated. The planning problem was transformed into a search optimization problem in a two-dimensional space.  
Based on the APO algorithm in this work, the specific flow of the multi-UAVs cooperative path planning is exhibited in Figure 7.
# Phase Center Corrections for BDS IGSO and MEO Satellites in IGb14 and IGSR3 Frame
## 4 Results and Validation
### Phase Center Variations  
As mentioned above, PCVs and PCOs were estimated in two steps. For the estimation of PCV values, we first obtained the daily raw PCVs with the PCOs fixed. Afterwards, the daily raw PCVs were then converted to daily smooth PCVs. With comprehensive quality control as well as an averaging algorithm, the satellite-specific and satellite-block-specific PCV values were obtained and plotted in Figure 5 for BDS-2 IGSO, BDS-2 MEO, and BDS-3 MEO from CAST and SECM, respectively.  
For BDS-2 IGSO satellites, the consistency between individual PCV curves were relatively lower than that of MEO satellites, due to higher correlation between PCVs and clock parameters caused by higher orbit height. Disregarding C10, the variations of nadir-dependent phase center were similar with maximum differences up to 1.3 mm between C09 and C16 at a 9\({}^{\circ}\) nadir. Hence, the block-specific mean was derived by excluding C10. Moreover, we found that the majority of BDS-2 MEO antennas show a very similar nadir-dependent phase center characteristic with variations from \(-\)4.6 to +4.1 mm. The repeatability of the three satellites was within 1.0 mm at a nadir angle above 4\({}^{\circ}\), and below 2.0 mm at a low nadir angle, as less observations were available at a low nadir. Similar performance was observed for BDS-3 MEO from CAST and SECM. However, we found that C35 and C36/C37 show anomalous patterns at a nadir angle of 5\({}^{\circ}\) compared to their counterparts from the CAST and SECM group. The reason is not clear, but is possibly due to the number of stations. About 60 stations available could track the three satellites until DOY 200, 2019, and it was about two times less than others. Hence, they have been excluded for deriving the satellite-block-specific PCVs. In general, the differences between the satellite-block-specific mean and the individual PCV curves were below \(\pm\) 1.0 mm. Using a satellite-block-specific pattern for these antennas instead of satellite-specific ones should therefore be possible without a significant loss of accuracy. The derived satellite-block-specific PCVs are also shown in Figure 5, and their values are listed in Table 4.  
Figure 4: Distribution of observations with respect to the satellite nadir angle for representative IGSO C06 and MEO C19 satellites.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 3 Preliminaries  
### Assumptions and Notations  
This proposed protocol, PDTR, was designed under the following conditions; the non-mobility of the sink and the sensors, and the homogeneity of all the network sensors (the same initial energy and the same communication range), except for the sink, which is charged with high energy, (see Figure 1).  
Figure 1 shows the PDTR working network. The sink (node 0) is static and located at the center, near to the base station. The sensors are homogeneous and static. The sensors near the sink (within the communication range) communicate directly with the sink. The sensors out of the sink communication range should maintain a multi-hop path for the communication with the sink. The main objective of PDTR is to prolong the network lifetime under the conditions depicted and explained in Figure 1. Table 1 shows the notations that used in this study.  
Figure 1: Static wireless sensor network (WSN) with 99 homogeneous sensors and the centralized sink node (node 0).
## 3 R-SIN Architecture Model Based on DaaC
### Definitions and Principles
#### 3.1.2 Main Principles  
The nature of a reconfigurable SIN refers to the situation of SIN architecture in which it can be transformed from one form to another for a task. The architectures of the SIN before and after conversion are not only different in composition, but also have different functions in implementation. Meanwhile, the architectures before and after conversion are not a simple arrangement combination, but rather embody the overall ability. Combining the reconfigurable characteristics and the development trend of the SIN in the future, we think that we should ensure the advantages of flexibility, scalability, and applicability. Meanwhile, for realizing the establishment of its architecture model, we should also observe these principles as follows.  
**Principle 1: Loose-coupling principle**  
The recombinant network in an R-SIN is a reorganization that is based on a specific function and task. It is based on the business needs of specific users to provide the corresponding network services. Therefore, its service and demand are one by one corresponding, and the kind of network is a loosely coupled model.  
**Principle 2: Compatibility principle**  
The components of an R-SIN consist of a variety of heterogeneous heterogeneity entities, and it also spans across different levels. Therefore, to carry out real-time information support, it is necessaryto realize real-time data information sharing. In addition, achieving good compatibility and integration is the prerequisite and foundation for ensuring its overall functionality.  
**Principle 3: Isolation principle**  
For an R-SIN, the networks that are built according to different needs of their clients are also different. In order to ensure that the network that is built can provide services efficiently, it is necessary to isolate the network that is built from other networks, and avoid interference and influence by other networks. That is, decoupling is to be done from the implementation.  
**Principle 4: Deconstruction principle**  
An R-SIN provides a specific service, and it is implemented over a specific period and within a specific local-area. When the demand for a task is over, the built network has no value in existence. Therefore, in order to avoid the waste of resources, it needs to be deconstructed; that is, the physical resources contained in the R-SIN need to be released. After deconstruction, these entity resources are restored to their natural state, and are always ready to prepare for the reorganization of the next specific task.
## 2 Results and Discussion
### Shelf-Life Study  
Like all fatty products, EVOO is prone to oxidation during storage, leading to rancidity [40]. Furthermore, in unfiltered oils, the simultaneous presence of microorganisms/enzymes, water, and organic matter can cause microbial degradation phenomena, resulting in a sensory defect known as \"fusty/muddy sediment\" [41]. These defects compromise the sensory quality of the oil, leading to commercial category downgrading and reducing consumer appreciation [2; 3].  
To minimize microbiological degradation, immediate filtration is a proven and widely recommended strategy [41], and therefore, it was applied to all oils produced within the MontEspertOlio project. For oxidation, reducing exposure to light, temperatures, oxygen, pigments, and metal ions significantly slows the processes, though it does not completely prevent it [42]. Thus, it is still necessary to develop storage strategies that further reduce the impact of oxidation on oil quality.  
This shelf-life study investigated the impact of bottle size on the quality of two EVOOs during 12-month storage (Section 3.3). The hypothesis, supported by analogous observations in wine [43; 44], was that larger bottles provide better protection against oxidation, due to (i) smaller surface-to-volume ratio, reducing light exposure; (ii) the same oxygen amount acting on a larger oil mass; (iii) in cases of sudden temperature changes, slower heat transfer across full oil mass; (iv) lower metal ion concentration leached from container surfaces.