esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To segment jaw structures in both open and closed mouth states from cone beam CT images and to preserve the true structures of trabecular bones. Segmentation algorithms of the mandible and maxilla were designed based on their different structures. We detected edges in volume and segment edges of the mandible and maxilla whether the mouth of a patient was open or closed. The internal structures of the mandible and maxilla were preserved by a morphological method with different parameters, respectively. An axial plane corresponding to the bottom of the hard palate was identified so that the bone structures of the maxilla above this plane were removed. Finally, the mandibular surface was smoothed by a simple thresholding method, and the maxillary surface was optimized by a geodesic active contours method. The 3D jaw model was segmented automatically by our proposed procedure with high accuracy. The average dice coefficients of the mandible and maxilla were equal to 0.9709 and 0.9420, respectively. The proposed jaw segmentation algorithm is automatic and knowledge-driven. The segmented jaws truly demonstrate the cortical and trabecular bone structures. It may potentially assist doctors in diagnosis and surgical planning.  
t 2022 2022 January 2022 2022 January 2  
growing methods. The cortical bone of the maxilla consists of thin fibrous bones, and its bone mineral density is lower than that of the mandible [1; 12]. As such, the segmentation of the maxilla is more difficult than that of the mandible. It is better to separate them and design different segmentation algorithms that are based on their specific structures. When the mouth is open, the mandible and maxilla are disconnected, and it is easy to separate them. When the mouth is closed, some teeth of the mandible and maxilla touch each other, and it is challenging to accurately separate them.  
There are two categories of jaw segmentation methods that are based on 2D images and 3D volume respectively [13; 14]. Some papers propose semi-automatic segmentation [15], segmentation that is based on histogram equalization [16; 17], or 2D slice-by-slice segmentation methods [9; 11]. Most of these methods only segment the cortical bone and the principal trabecular bones, ignoring internal non-bony structures of the jaw. A statistical shape model was proposed for the mandible segmentation [10; 18]. This kind of algorithm could generate an accurate mandible with trabecular bones only when the mouth is open, and these algorithms do not work for the maxilla segmentation. In our previous work [14], we segmented the mandible and maxilla with trabecular bone preservation. However, the selection of maxilla ROI was rough, and the algorithm was not adjusted for the different structures of the mandible and maxilla.  
The mandible and maxilla have different structures. In our proposed method, we designed slightly different algorithms that were based on the edges, region growing, and morphological operations in 3D volume. Our 3D jaw model illustrates not only the jaw shape but also the internal trabecular bone structures.
## 5 Overview and Design
### Strategy Selection Module  
The Strategy Selection Module aims to alleviate the problems of inefficient detection and the inability to cover some uncommon header parameters that may be injected. In this module, LLM Agents extract all potential injection points from the target and perform preliminary validation of them using various injection techniques. This comprehensive  
Figure 2: SqliGPT workflow.  
approach ensures that even the most uncommon header parameters are not overlooked, instilling confidence in the thoroughness of your security testing.  
Figure 3 depicts the structure of this module, primarily comprising three LLM Agents and a Replayer: 1 The _Extraction Agent_ is designed to identify all potential SQLI points from HTTP request-response pairs, compiling them into a list. Although every parameter in the request headers could potentially be vulnerable to SQLI, testing each one exhaustively is often impractical and costly. Therefore, the Extraction Agent selectively identifies only those header parameters implicated in the responses and parameters the user can control as potential injection points. 2 The _Pre-validation Agent_'s responsibility is to generate simple payloads to assess these potential injection points individually. It evaluates each parameter's injection likelihood based on the received responses and ranks them according to their successful injection probability.  
Furthermore, this agent gauges the sensitivity of parameters to various injection techniques based on feedback and prioritizes techniques that are likely to be more successful. This strategic prioritization enhances the test effectiveness of the Sqlmap commands generated by LLMs. 3 The _Parameters Generation Agent_ takes the sorted list from the Pre-validation Agent and generates the appropriate parameters of the Sqlmap command for the listed potential injection points and techniques.  
The Strategy Selection Module enhances detection efficiency and broadens the coverage of uncommon header parameters by optimizing the generation of Sqlmap command parameters through a feedback mechanism. This module arranges injection parameters and techniques based on feedback from the targeted web application, enabling more effective and thorough detection.
# Implementing Smart City Technologies to Inspire Change in Consumer Energy Behaviour
## 1 Introduction
In parallel, analysis and simulation of communication solutions as the integral building block of the smart city uncovering important characteristics that might be considered was performed. The cooperation between the European Union (EU) and Latin American, Caribbean (LAC) countries has a potential to provide valuable knowledge and experience exchange for all parties involved. The ITCity project brought together researchers and experts from different areas relevant to urban development to cooperatively investigate the energy related aspects of smart city solutions with a focus on the well-being of cities' inhabitants. Partners from the EU (Latvia and Romania) represented energy experts but partners from LAC (Brazil, Chile and Barbados) served as experts in ICT Small and Medium Enterprise (SME) and applications developers. While intermediary results of the work are well explained in respective publications [7; 8; 9], this paper aims to present outcomes of successful intercontinental collaboration and provide the research community insights into tailored approaches in advancing energy efficiency. The paper is structured as follows: Section 2 addresses relevant political and legislative trends in EU and LAC. Section 3 outlines an implementation and results of smart metering pilot in Romania. Section 4 describes the proposed user behaviour assessment tool and Section 5 presents analysis and simulation of communication solutions. Finally, Section 6 presents the conclusions and proposes directions for the future research.
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration
### Noise Figure  
A lower noise figure is generally preferred since it indicates a more sensitive receiver with a lower noise contribution. A typical value for SAR systems is around 1 to 3 dB. Reducing noise figure improves the ability to detect weak signals, which is important for SAR image quality. The antenna beam width affects the azimuth resolution. A narrower beam width provides higher azimuth resolution but may require more complex beamforming and steering mechanisms. The optimal beam width depends on the required resolution and the satellite's operational altitude. Generally, a narrow beam is preferred for higher resolution. Minimizing system losses is important to maximize the radar's sensitivity. This includes losses in the transmit and receive paths, as well as any losses due to the atmosphere. System loss should be minimized to maximize the SNR and, therefore, image quality [36; 52].
# Location-Based Relay Selection in Full-Duplex Random Relay Networks
## 5 Simulation Results
Figure 5: The outage probability of an FDRRN as a function of the density of the candidate relay nodes for the proposed, _max-min_, and optimal relay selection algorithms. Figure 6 presents the outage probability of an FDRRN for different path-loss exponents. Note that the potential relay locations of the proposed algorithm, when \(\alpha=3.5\) and \(\alpha=4.5\), were calculated using MATLAB R2024a functions under the simulation setting. We can see that as the path-loss exponent decreases, the outage probability increases. This is due to the fact that as the path loss decreases, not only does the strength of the desired signal increase but so does the network interference, which significantly impacts the FDRRN's performance. We can also observe a similar performance trend between the proposed and optimal algorithms, with a consistent performance gap. This is because the proposed algorithm can mitigate location-dependent impacts, such as path loss and interference, which are dominant factors in the FDRRN's performance. As a result, it can achieve performance close to that of the optimal solution. Consequently, based on Corollary 1, it is feasible to determine the potential relay location for any general path-loss exponent. Figure 7 illustrates the outage probability of an FDRRN with different SIR thresholds. The figure shows that the outage probability improves as the relay density increases due to the benefits of enhanced spatial diversity. Furthermore, the outage probability decreases with a lower SIR threshold, as this leads to more relaxed conditions for determining outage events. From these results, we can conclude that selecting an appropriate SIR threshold is essential, depending on the system requirements. Figure 8 depicts the achievable spectral efficiency of an FDRRN for the various relay selection schemes. The achievable spectral efficiency is defined as the product of the successful transmission probability and the spectral efficiency of the DF relay protocol [39] as follows:  
\[\varsigma=p_{\text{o}}(\mathbf{x}_{\text{s}})\times\text{min}\{ \log_{2}(1+\gamma_{1}),\log_{2}(1+\gamma_{2})\}. \tag{21}\]  
We can see that the achievable spectral efficiency increases with higher relay density due to enhanced selection diversity. Moreover, similar to the outage probability, the achievable rate of the proposed algorithm shows slightly poorer performance compared to the optimal and _max-min_ algorithms because the proposed algorithm utilizes less information in the relay selection process. Figure 6: The outage probability of an FDRRN as a function of the density of the candidate relay nodes for the proposed, _max-min_, and optimal relay selection algorithms for different path-loss exponents, \(\alpha\).
## 3 Results and Discussion  
### Highly Volatile Compounds Identified by GC-IMS  
The volatiles in the sample of untreated fresh onion, onion bulbs, onion rings, and onion squares stored at 25 \({}^{\circ}\)C were analyzed by GC-IMS. The ion migration time and the position of the reactive ion peak (RIP) were normalized. The ordinate represents the retention time of the gas chromatography, the abscissa represents the ion migration time, and the vertical line at the abscissa 1.0 is the RIP peak. Each point on the right of RIP represented a volatile compound extracted from the samples. Color represented the signal intensity of the substance. White indicated a lower intensity, and red indicated a higher intensity. A total of 15 volatile compounds, including 6 aldehydes, 2 alcohols, 2 esters, 1 sulfur, 1 ketone, 1 pyrazine, and 1 furan compound, were identified in onion samples by GC-IMS, as shown in Table 1.  
Aldehydes and alcohols were the most abundant compounds. They have a green odor, similar to cut grass, leaves, or wood. Aldehydes in onion included pentanal, hexanal, heptanal, octanal, (_E_)-2-heptanal, and (_E_,_E_)-2,4-heptadieneI. Alcohols included 1-propanol and 2-methyl-1-pentanol. According to previous research [26], aliphatic aldehydes and alcohols are produced upon cutting or chewing plant tissue from the lipoxygenase pathway. Most aldehydes identified in foods are produced by the oxidation of unsaturated fatty acids. For example, (_E_)-2-heptanal and pentanal are derived from w-6 fatty acids, and 2,4-heptadieneI is derived from w-3 fatty acids. In plants, this process can be enzymatic and is called the lipoxygenase (LOX) pathway. The LOX pathway is predominately active in the green organs of plants in response to wounding, and it also gives rise to the formation of volatiles with green odors [26]. Ethyl acetate and propyl acetate have been identified in onion. Esters with fruity aromas have been reported in almost all fruits and are derived from the fatty acid and amino acid pathways [26], respectively. 2,6-Dimethylpyrazine, dimethyl ketone and 2-pentylfuran, and dimethyl disulfide, were also identified by GC-IMS. Among them, dimethyl disulfide has a low odor threshold and a typical cabbage aroma, while 2,6-dimethylpyrazine has a roasty aroma. Furans are the products of the autoxidation of polyunsaturated fatty acids. 2-pentylfuran in onion can be formed from linoleic acid [26].
###### Abstract  
GNSS Precise Point Positioning (PPP) technology has been extensively applied to post-processing international comparisons between UTC/TAI times and real-time time transfer, predominantly in static configurations. However, with the swift advancement of intelligent and unmanned systems, there is an urgent need for research into kinematic time transfer. This paper introduces a kinematic model Galileo/GPS integrated PPP time transfer approach leveraging the Galileo High Accuracy Service (HAS). The study utilized observational data from seven stations spanning 22 days. The findings indicate that under static conditions, GPS, Galileo, and Galileo/GPS PPP, when supported by the Galileo HAS, can achieve time transfer with sub-nanosecond precision. In kinematic scenarios, the accuracy of single-system PPP time transfer is comparatively lower, with frequent re-convergence events leading to significant accuracy degradation (exceeding 1 ns). However, in cases where re-convergence is infrequent due to a limited number of satellites, sub-nanosecond time transfer is still attainable. The Galileo/GPS integrated PPP time transfer effectively mitigates the issue of re-convergence, ensuring sub-nanosecond accuracy across all links (0.48 ns). Consequently, it is recommended to employ a multi-system integration approach for kinematic PPP time transfer, particularly when utilizing the Galileo HAS. In terms of frequency stability, GPS, Galileo, and Galileo/GPS PPP demonstrate short-term stability (over 960 s) of (5.29 \(\times\) 10\({}^{-13}\), 3.34 \(\times\) 10\({}^{-13}\), and 1.60 \(\times\) 10\({}^{-13}\)), respectively, and long-term stability (over 15,360 s) of (1.49 \(\times\) 10\({}^{-13}\), 1.02 \(\times\) 10\({}^{-13}\), and 4.06 \(\times\) 10\({}^{-14}\)), respectively.  
Gallieo HAS; time transfer; PPP kinematic; GPS; Galileo
## 5 Discussion
### Impact of Forest Type and Age on Carbon Sinks Capacity in Forest Ecosystems  
Numerous studies have shown that NPP in forests tends to decrease with increasing forest age [61; 62]. [PERSON] and [PERSON] [61] noted that forest productivity begins to decline in young forests, with a more pronounced decrease in productivity during the early stages of forest growth. However, other studies have highlighted that mature forests can exhibit strong carbon sequestration capacity [63]. The results of this study support this dual perspective, showing that while the carbon sequestration capacity of forests in the Qinba Mountain area decreases during the early stages of growth, older forests still maintain a significant carbon sequestration capacity. This finding aligns with existing research on the topic.  
[PERSON] et al. [64] found that in the Himalayan region, forest biomass and carbon storage tend to increase with elevation. The results of this study, which show that forest carbon sinks values also increase with elevation, are consistent with these previous findings.  
This study indicates that the carbon sequestration capacity of plantation forests is particularly strong when the forest age ranges from 10 to 70 years, with an overall average ecological carbon sinks value of 25.37 gC m\({}^{-2}\) mon\({}^{-1}\), which is nearly identical to the average ecological carbon sinks value of natural forests, at 24.36 gC m\({}^{-2}\) mon\({}^{-1}\). Although the proportion of carbon sources is lower and the proportion of carbon sinks is higher in natural forests compared to corresponding grid values, this suggests that plantation forests in the Qinba Mountain area also exhibit significant carbon sequestration effects. [PERSON] and [PERSON] [65] found that although plantation forests tend to have lower species diversity, their biomass, productivity, and carbon absorption rates are comparable to or even exceed those of natural forests. In fact, plantation forests aged 0-80 years can already have biomass levels similar to those of natural forests. Similarly, [PERSON] et al. [66] observed that the carbon storage at the ecosystem level in Masson pine plantation forests is comparable to that in natural forests. The results of this study generally align with these earlier findings.  
This study finds that the carbon sinks capacity of plantation forests in the Qinba Mountain region peaks when forest age ranges between 10 and 30 years. This highlights the critical role of young plantation forests in carbon sequestration, underscoring their importance in achieving China's dual carbon goals. Therefore, future efforts should focus on optimizing forest management strategies to enhance the carbon sequestration potential of young plantation forests.
## 3 Results
### Antioxidant Activity  
Antioxidant activities of _Chlamydomonas_ sp. MA\({}_{\text{a1}}\) and _Navicula_ sp. MA\({}_{\text{n1}}\) isolates methanol extracts were performed by two spectrophotometric methods: the 2,2-di(4-tert-octylphenyl)-1-picrylhydrazyl (DPPH) assay and the 2,2\({}^{\prime}\)-azinobis-(3-ethylbenzothazoline-6-sulfonic acid (ABTS) assay (Figure 1a,b).  
The results obtained showed that the green microalgae isolate _Chlamydomonas_ sp. MA\({}_{\text{a1}}\) exhibited a higher antioxidant activity compared to _Navicula_ sp. MA\({}_{\text{n1}}\) strain using both tests.  
The enzymatic antioxidants from _Chlamydomonas_ sp. MA\({}_{\text{a1}}\) and _Navicula_ sp. MA\({}_{\text{n1}}\) isolates were investigated (Figure 2).  
Figure 1: Non enzymatic antioxidant activities of _Chlamydomonas_ sp. MA\({}_{\text{a1}}\) and _Navicula_ sp. MA\({}_{\text{n1}}\) methanol extracts. (**a**) DPPH scavenging activities; (**b**) ABTS scavenging activities. Data show means \(\pm\) SD (\(n=3\)). Statistical significance was determined by a Student’s \(t\) test; significant difference (\(p<0.05\)) is indicated by different lowercase letters.  
Figure 2: Enzymatic antioxidant activities of _Chlamydomonas_ sp. MA\({}_{\text{a1}}\) and _Navicula_ sp. MA\({}_{\text{a1}}\) methanol extracts. (**a**) Peroxidase activities; (**b**) catalase activities. Data show means \(\pm\) SD (\(n=3\)). Statistical significance was determined by a Student’s \(t\) test; significant difference (\(p<0.05\)) is indicated by different lowercase letters.  
The obtained peroxidase activities were 1.0625 U/mg of proteins and 0.8 U/mg of proteins for _Chlamydomonas_ sp. MA\({}_{\text{a1}}\) and _Navicula_ sp. MA\({}_{\text{n1}}\) isolates, respectively. Also, 4101.27 U/mg of protein of catalase activity was recorded for _Chlamydomonas_ sp. MA\({}_{\text{a1}}\) and 3281.01 U/mg of protein for _Navicula_ sp. MA\({}_{\text{n1}}\).
## 2 Theory Based on Resonance Method
### Thickness Shear Extension (TS)  
In this mode, we have a thin square plate in which the polarization is oriented along the length, and shear vibrations occur along the depth. In other words, the orientation of polarization is perpendicular to the direction of the electric field, causing the piezoelectric component to display shear vibrations. Table 1 presents the geometric shape and material constants. When the aspect ratios of the component  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**Specification**} & \multicolumn{4}{c}{**Obtained Material Constant**} \\ \cline{3-6}  & & \(k\) & \(d\) & \(S^{E}\) & \(\varepsilon^{T}\) \\ \hline Thickness Extension (TE) & & & & & \\ \(l>10t\), \(w>10t\) & & & & & \\ \hline Thickness Shear Extension (TS) & & & & & \\ \(l>20t\), \(w>10t\) & & & & & \\ \hline Length Extension (LE) & & & & & \\ \(l>5w_{1}\), \(w>5w_{2}\) & & & & \\ \hline Length Thickness Extension (LTE) & & & & & \\ \(l>10t\), \(l>3w\), \(3t>w\) & & & & \\ \hline Radial Extension (RAD) & & & & & \\ \(l\) & & & & \\ \(l>10t\), \(l>3w\), \(3t>w\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification on vibration modes of standard piezoelectric elements and their correspondent measured material constants [14].  
satisfy \(l>20t\) and \(w>10t\), we use five specimens with dimensions \(l=9\pm 0.05\) mm, \(w=8\pm 0.05\) mm, and \(t=0.52\) mm, and the following equations can be used to calculate their material constants:  
\[k_{15}^{2}=\frac{\pi}{2}\frac{f_{r}}{f_{a}}\cot\left(\frac{\pi}{2}\frac{f_{r}}{f _{a}}\right) \tag{12}\]  
\[c_{44}^{D}=4\rho(tf_{a})^{2} \tag{13}\]  
\[c_{44}^{E}=c_{44}^{D}\left(1-k_{15}^{2}\right) \tag{14}\]  
For PZT material, the following relations can be used to derive \(S_{44}^{D}\), the elastic compliance constant with constant electric displacement, and \(S_{44}^{E}\), the elastic compliance constant in a constant electric field:  
\[S_{44}^{D}=1/c_{44}^{D} \tag{15}\]  
\[S_{44}^{E}=1/c_{44}^{E} \tag{16}\]  
where \(k_{15}\) is the electromechanical coupling coefficient of the thickness shear mode shape.  
For the dielectric constant, the capacitance measured using an impedance analyzer at a specific frequency (1 kHz) can be substituted into the following equations, which will produce \(c_{11}^{T}\), the dielectric constant in constant stress, and \(c_{11}^{S}\), the dielectric constant in constant strain:  
\[\varepsilon_{11}^{T}=\frac{C_{L}\cdot t}{A} \tag{17}\]  
\[\varepsilon_{11}^{S}=\frac{C_{H}\cdot t}{A} \tag{18}\]  
\(C_{L}\) is the capacitance measured at 1 kHz, and \(C_{H}\) is the capacitance measured at twice the anti-resonant frequency (\(2f_{a}\)).