esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
### Discussion  
This study employs the Optimal Parameter Geodetector method to optimize the parameters of individual factors, uncover the interactions among them, and provide an in-depth analysis of the driving mechanisms behind spatial conflicts. Compared to the traditional Geodetector approach, the Optimal Parameter Geodetector offers significant advantages, including the ability to better address complex nonlinear relationships among multiple factors, enhance the model's explanatory power, and clarify the relative contributions and interaction effects of each factor. This method ensures more a rational and precise parameter selection for spatial factors, enabling a clearer representation of the spatial differentiation characteristics of PLES conflicts. However, this study has limitations. First, the Optimal Parameter Geodetector depends on a predefined variable selection, which may overlook certain influencing factors. Second, while it reveals factor interactions, it does not establish causal relationships, requiring further validation through complementary quantitative or qualitative methods. The findings elucidate the formation mechanisms and evolutionary dynamics of PLES conflicts in Zengcheng District, providing theoretical and methodological insights that contribute to a broader understanding of spatial conflicts in urban fringe areas worldwide, particularly in rapidly urbanizing regions facing similar land-use challenges.  
Using Zengcheng District as a case study, this research study explores the underlying mechanisms driving PLES conflicts in urban fringe areas and their spatiotemporal evolution. Nevertheless, certain limitations persist. The Optimal Parameter Geodetector may fall short in accurately capturing dynamic spatial variations due to the constrained temporal resolution of the dataset. Furthermore, the research model did not comprehensively incorporate the intricate effects of policy interventions and anthropogenic activities, both of which exert profound influences on regional spatial conflicts. To bridge these gaps, future research should incorporate high-resolution spatiotemporal datasets and explicitly integrate policy and governance variables into the analytical framework. These advancements would enhance both the explanatory power and predictive accuracy of PLES conflict analysis, providing a more robust theoretical and methodological framework for understanding spatial conflicts in urban fringe areas globally. By offering insights into land-use dynamics and conflict mitigation strategies, these findings contribute to the integrated planning, sustainable development, and strategic conservation of territorial space. This, in turn, supports balanced urban-rural transitions, fosters regional spatial optimization, and aligns with broader global efforts toward sustainable land management and spatial integration.
## 2 Design Section
### Design Evolution  
This sub-section presents the design procedure of the proposed antenna in detail. The geometrical parameters are illustrated in Figure 1. To minimize the mutual coupling between the feed lines and the patch, the feed lines are printed on the bottom layer of the antenna. Two orthogonal feed ports, _i.e.,_ 1-port and 2-port, excite vertical and horizontal polarized waves, respectively. The CSRR slots are placed in a \"T\" orthogonal configuration under the fractal patch to improve the isolation between the two feed ports. The CSRR structure (which is based on the metamaterial concept) [9,10] and Giuseppe Peano fractal geometry have been adopted to minimize the dimensions of the antenna.  
According our design method, an antenna with good radiation performance would be obtained in terms of the realized gain and the compact dimensions.
## 3 Results
### Data Analysis
#### 3.4.2 In Vitro Studies  
(a) Analysis of aims of studies and respective evaluation methods  
Respective evaluation methods concerning the various aims of the studies are shown below:  
* Color change: 7/10 studies used an objective (spectrophotometric) method.
* Mineral content: 2/2 applied energy dispersive X-ray spectrometry (EDX) and Scanning Electron Microscopy (SEM).
* Adhesion: 1/1 used SEM.
* Microhardness: 1/3 used Vickers and 2/3 Knoop hardness method.
* Chemical stability of dentin: 1/1 used confocal microscopy and Raman spectroscopy.
* Morphology of dentin: 1/2 used confocal microscopy and Raman spectroscopy, while 1/2 used SEM.
* Microroughness: 2/2 used a profilometer.
* Permeability: 1/1 used a staining technique.
* HP penetration: 1/1 used a spectrophotometer to monitor this.
* Pulp \(\Delta\)T: 4/4 used a thermocouple connected to a digital thermometer and data logger.
* Gel \(\Delta\)T: 1/1 used a thermocouple connected to a digital thermometer and data logger.
* Outcome assessment Regarding the primary outcome (a), 43% of the studies presented a positive outcome, 10% were negative, while 47% showed no difference between the test and control groups. Specifically, for each examined aim, the number of studies and the respective outcome are shown in Figure 3, where the abovementioned categories: mineral content, adhesion, microhardness, chemical stability of dentin, morphology of dentin, microroughness, permeability are grouped together under the term \"hard tissue alterations\".  
Figure 3: Aims of in vitro studies and their respective outcome significance. Codification: green: test group significantly better, yellow: no significant difference, red: test group significantly worse—note: the total number differs from the total number of studies, as some studies had more than one aim.  
It was also revealed that, besides the green light and blue hybrid LED/laser device, the activation sources most commonly used are the near infrared (NIR) wavelengths ranging from 810 to 980 nm.  
Except from the LED/laser hybrid, which was used in only one of the aforementioned studies, the other parameters applied are listed below:  
* H2O2 concentration, high (30-38% (\(w/v\))) is preferably applied.
* Gel thickness 1 to 2 mm.
* Contact time: 4.5-30 min, mainly 15-20 min.
* Spot size/handpiece: 200 \(\upmu\)m fiber up to 4 cm\({}^{2}\)/quadrant; large spot sizes/single-tooth or quadrant handpieces were preferably applied.
* Irradiation time per cycle: 15-180 s, predominantly 30 s.
* Fluence per cycle: 22.5-90 J/cm\({}^{2}\), mainly 45-70 J/cm\({}^{2}\).
* Irradiation cycles: mostly 3.  
(d) Evaluation of pulp temperature  
Four of the included in vitro studies investigated pulp temperature increases as shown in Table 7[76; 84; 89; 91]. Six different combinations of wavelength and bleaching gel were  
\begin{table}  
\end{table}
Table 6: The laser bleaching protocols that exerted a beneficial effect in the in vitro studies.  
explored, with various fluences. The resulting temperature increase ranged from 1.99 \({}^{\circ}\)C to 14.06 \({}^{\circ}\)C.
# Optimal Power Flow Solutions for Power System Considering Electric Market and Renewable Energy
## 2 Formulation of the Studied Problem  
The study focuses on the profit maximization for transmission power networks considering different prices of nodes in the networks and the placement of wind turbines. Thermal power plants must consume fossil fuels to produce and supply electricity to loads. Conventional studies of the OPF problem have ignored the prices at load nodes, and this neglection has not considered the effectiveness of different thermal power plants in power systems. For recent OPF problems considering renewable energies, the neglection has been seen again. So, the study has focused on the maximization of profit for the transmission power networks considering nodal prices and renewable energy, which is supposed to be wind turbines. The objective and constraints are presented as follows:
# Probabilistic Mixture Model-Based Spectral Unmixing
###### Abstract  
Spectral unmixing attempts to decompose a spectral ensemble into the constituent pure spectral signatures (called endmembers) along with the proportion of each endmember. This is essential for techniques like hyperspectral imaging (HSI) used in environment monitoring, geological exploration, etc. Several spectral unmixing approaches have been proposed, many of which are connected to hyperspectral imaging. However, most extant approaches assume highly diverse collections of mixtures and extremely low-loss spectroscopic measurements. Additionally, current non-Bayesian frameworks do not incorporate the uncertainty inherent in unmixing. We propose a probabilistic inference algorithm that explicitly incorporates noise and uncertainty, enabling us to unmix endmembers in collections of mixtures with limited diversity. We use a Bayesian mixture model to jointly extract endmember spectra and mixing parameters while explicitly modeling observation noise and the resulting inference uncertainties. We obtain approximate distributions over endmember coordinates for each set of observed spectra while remaining robust to inference biases from the lack of pure observations and the presence of non-isotropic Gaussian noise. As a direct impact of our methodology, access to reliable uncertainties on the unmixing solutions would enable robust solutions to noise, as well as informed decision-making for HSI applications and other unmixing problems.  
hyperspectral unmixing; probabilistic modeling; data-driven algorithms +
Footnote †: journal: Applied Sciences
## 5 Automation and Teleoperation
### Conclusions  
The introduction of teleoperation and automation in the mining sector offers numerous possibilities, but the choice of options for implementing the necessary networking structure is limited. From among the feasible network technologies, 5G and Wi-Fi 2.4/5/6 GHz are expected to be able to deploy a network with appropriate characteristics (see [16; 65]). Feasible areas of usage would be repetitively used areas where infrastructure can be installed and maintained. However, it is important to note that 5G and WiFi 6 Ghz have not been extensively tested underground yet, and further research is needed to evaluate  
\begin{table}
\begin{tabular}{c c c} \hline \hline Wi-Fi 6 GHz & 3 & 1 & 1 \\ Wi-Fi 2.4 GHz & 3 & 1 & 1-2 \\ 5G & 2 & 2 & 1-2 \\ LTE 4G & 3 & 1-3 & 2+2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summarized Comparison and Evaluation of Wi-Fi, LTE/4G and 5G.  
their operational capabilities in those environments. Additionally, another important factor to consider is the access and licensing of the hardware. 5G components are not as readily available from a variety of vendors as Wi-Fi hardware. On the other hand, 5G offers the opportunity for a managed and controlled network, resulting in reliable communication links having a higher quality of service.
## 5 Experiments
### Algorithm Selection as a Function of Number of Real Images Used to Train the Generator  
The next set of experiments is conducted in order to understand what amount of synthetic samples is necessary for an Algorithm selector to improve over the best Algorithm. First, three types of Algorithm selection strategies are evaluated in this experiment: \(conf\), \(rand\), and \(copy\) (as previously described in Section 4.2), along with two types of training datasets. In these experiments, the Algorithm selector was either trained on 270 real images or 500 synthetic images. The results are shown in Figure 11.  
The first observation is that, when using real data for training, all three Algorithm selection \(rand\), \(conf\), and \(copy\) result in constant accuracy. This is natural because the Algorithm selection is always trained on the same 270 real images (solid plot lines). The synthetic images are, however, generated for each different number of seed samples from \(\mathbb{D}_{E}\), and therefore the Algorithm selector accuracy varies (dashed plot lines).  
The second observation is the evolution of Algorithm selector accuracy in the classification task. First, observe that from the Algorithm selection methods trained on real data, the \(copy\) is the most accurate, followed by \(rand\), and the least accurate is \(conf\). Second, for 50, 200 and 900 seed images, the \(copy\) Algorithm selection is the most accurate, while for 500 seed images, the \(rand\) Algorithm selection is the most accurate. Finally, observe that when the number of training images grows to over 100, the \(copy\) and \(rand\) Algorithm selections are much more accurate than the \(conf\) Algorithm selection.  
Figure 11: Algorithm selection accuracy as a function of real images used to train the image generator.  
The last observation is that with the _copy_ strategy, both Best RAS and Best SAS obtain the highest accuracy. Only the SAS _copy_ Algorithm selection strategy outperforms the best Algorithm (best Algorithm accuracy is shows as black line with triangles as data points marker) as well as the Best RAS _copy_ strategy in some cases.
## 5 Numerical Results and Discussion
### Detection Performance under Rayleigh Fading Channel  
In the Rayleigh fading channel, the BER, PER, and SER performance of the proposed simplified scheme, uncoded scheme, and the exact LLR scheme are verified. As can be seen from Figure 8, compared with the uncoded scheme, the detection performance of the coding scheme is significantly improved. The performance of the simplified LLR scheme is extremely close to that of the exact LLR scheme. As shown in Figure 8c, when PER = \(1\times 10^{-3}\), compared with the uncoded scheme, the proposed simplified scheme obtains a gain of nearly 6.1 dB. Compared with the exact LLR scheme, the simplified LLR scheme in (25) only has a performance loss of about 0.02 dB, and the simplified LLR scheme in (26) has a performance loss of about 0.1 dB. Therefore, the simplified scheme has little performance loss and friendly-complexity. In particular, the simplified LLR schemes in (26) do not require CSI when using MS decoding. In addition, as shown in Figure 9, we can draw similar conclusions under \(\rho\
eq 1\), which, however, is not illustrated here. Clearly, our simulation results in Figures 8 and 9 indicate that our simplified LLR scheme depicts good performance under the Rayleigh fading channel.  
Figure 5: Under phase noncoherent channel with perfect CSI, the impact of the maximum number of iterations of the LDPC decoder on the detection performance, where \(\rho=1\). (**a**) BER performance; (**b**) SER performance; and (**c**) PER performance.  
Figure 6: Detection performance for LDPC coded IEEE 802.15.4c MPSK signals under phase non-coherent channel, wherein different LLR calculation schemes are considered and \(\rho=1\). **(a)** BER performance; **(b)** SER performance; and **(c)** PER performance.  
Figure 7: Detection performance for LDPC coded IEEE 802.15.4c MPSK signals under phase non-coherent channel, wherein different LLR calculation schemes are considered and \(\rho\
eq 1\). (**a**) BER performance; (**b**) SER performance; and (**c**) PER performance.  
Figure 8: Detection performance for LDPC coded IEEE 802.15.4c MPSK signals under Rayleigh fading channel, wherein different LLR calculation schemes are considered and \(\rho=1\). (**a**) BER performance; (**b**) SER performance; and (**c**) PER performance.  
Figure 9: Detection performance for LDPC coded IEEE 802.15.4c MPSK signals under Rayleigh fading channel, wherein different LLR calculation schemes are considered and \(\rho\
eq 1\). **(a)** BER performance; **(b)** SER performance; and **(c)** PER performance.
###### Abstract  
The objective of this study was to assess the proximity of the mandibular molar roots and the lingual cortical bone in patients with various vertical facial patterns and determine factors related to root-cortical bone contact. A total of 145 patients (84 males, 61 females, mean age: \(22.0\pm 1.76\) years) were assigned to hypodivergent (36 patients), normovergent (80 patients) and hyperdivergent (29 patients) groups based on their facial height ratio. Cone-beam computed tomography (CBCT) images were used to measure the distance between the mandibular molar roots and the lingual cortical bone, and any instances of root-cortical bone contact were identified. The study investigated the correlation between the contact of the mandibular molar roots with the lingual cortical bone and several variables, including sex and cephalometric measurements. The distance between the mandibular molar roots and the lingual cortical bone was significantly shorter in the hyperdivergent group than in the hypodivergent group (\(p<0.05\)). Of the total root-cortical bone contact, 87.6% was observed in the mandibular second molars, and the distal roots of the mandibular second molars had the highest contact rate at 43.1% in the hyperdivergent group (\(p<0.05\)). Among the evaluated variables, only the distance between the distal root apex of the mandibular second molar and the mandibular plane was found to be associated with contact of the mandibular molar roots with the lingual cortical bone. An increase of 1 mm in this distance was associated with a 22% decrease in the likelihood of contact between the mandibular second molar roots and lingual cortical bone (\(p<0.001\)). Given the proximity and high contact ratio between the mandibular molar roots and lingual cortical bone, it is recommended that these structures be evaluated using CBCT before planning molar distalization or intrusion in hyperdivergent patients.  
Keywords:lingual cortical bone; cone beam computed tomography; vertical facial morphology +
Footnote †: journal:
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.21. Elus  
The ELUS [75] framework provides a level of indirection between function calls and function invocation. This allows components marked as _reconfigurable_, or functions within those components, to be dynamically updated at runtime. Data transfer between the old and new component's attributes is achieved using _get_ and _set_ methods. A _RECONFIGATOR_ receives new code, and informs an _AGENT_ to load the component code and update related lookup tables. Updates are disseminated using a publish/subscribe protocol, with unicast requests and broadcast retransmissions. Nodes publish all their code versions periodically, and interested nodes request them, and the publishing nodes become _senders_ and transmit the data. Unicast NACKs are used to request missing packets. The ELUS TRANSPORT PROTOCOL is used to send reprogramming requests for a component to nodes.  
ELUS provides no specific features related to autonomic updates, but it provides additional granularity and performance improvements that may be useful features in an autonomic environment.
## 3 Results and Discussion
### Monitoring of A Freeze-Drying Cycle for a Mannitol/Sucrose Solution
#### 3.1.5 Residual Water Content in the Freeze-Dried Material  
One replicate per cuvette was measured using the Karl Fischer titration. About 400 mg of solid mannitol/sucrose was available per each cuvette, which was initially filled with 8.5 mL of the 5% pharmaceutical formulation. The average residual water content in the material after drying was 5.85 \(\pm\) 0.20% (m/m), \(\pm\)1 SD for \(n\) = 5. The average water content in cuvettes 1 and 5 equipped with probes was 5.69 \(\pm\) 0.17%, and in the remaining cuvettes 2, 3 and 4 without probes, it was 5.96 \(\pm\) 0.18%. Whether the lower water content in cuvettes 1 and 5 is directly correlated to the presence of the probes (and the higher primary drying speed) is unsure, but there is at least an indication. Table 1 contains additional information about the water content per cuvette, the order of nucleation and the minimum temperatures of the supercooled pharmaceutical formulation just before nucleation and subsequent freezing.
# Glueless Multiple Input Multiple Output Dielectric Resonator Antenna with Improved Isolation
## 3 Results and Discussion
\(P_{\theta}\) and \(P_{\phi}\) are angle of arrival power densities for elevation and azimuth directions, respectively, which are represented under following normalization condition:  
Figure 4.2  
Figure 4.3 Simulated and measured gain and simulated radiation efficiency of proposed DRA. Assume uniform distribution of horizontal and vertical components of incident waves, which is widely considered a case of isotropic environment in rich multipath case, to operate the MIMO system. Take \(P_{\theta}=P_{\phi}=1/(4\pi)\) and \(P_{V}=P_{H}\). Table 1 depicts calculated values of \(\rho_{12}\) using Equations (1)-(4) and measured far-field radiation components, which are less than 0.3 in the desired frequency band of operation. Simulated values of \(\rho_{12}\) are not shown here because EM simulator HFSS uses the 5\(-\)parameter method to compute the correlation coefficient, which comes out less than 0.006 for all frequencies of interest. Low values of \(\rho\) confirm the good diversity performance and channel characteristics required for efficient MIMO operation at a higher data rate. For the dual-port MIMO system, another important MIMO parameter, called TARC, can be calculated using Equation (5) given in [8; 27] for different values of angle \(\theta\). Figure 4.4  
where \(\theta\) is input feeding phase. A desirable value of TARC for the MIMO system is less than 0 dB. It is inferred from Figure 8 that TARC is less than \(-\)10 dB for all input feeding phase angles in the entire operating frequency range. Surface current distribution of MIMO DRA is shown in Figure 9, which shows minimal mutual coupling between ports. Justifiable performance comparison has been done by including recent single element MIMO DRAs in Figure 4.5 A major highlight of the proposed antenna is its compactness and novel glueless technique to hold the DRA on the substrate. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Frequency (GHz)** & **8.3** & **8.4** & **8.5** & **8.6** & **8.7** & **8.8** \\ \hline \(\rho_{12}\) & 0.16 & 0.05 & 0.17 & 0.29 & 0.17 & 0.04 \\ \hline \end{tabular}
\end{table} 
Table 1: Measured Envelope Correction Coefficient (\(\rho\)). Figure 8: Simulated TARC of MIMO DRA for different values of \(\theta=0,\ 30^{\circ},\ 60^{\circ},\ 90^{\circ},\ 120^{\circ},\ 150^{\circ},\ 180^{\circ}\) in Equation (5).
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 3 Method and Algorithm
### Paillier Federated Network
#### 3.4.2 Improved Paillier Algorithm  
However, due to the high complexity of the Paillier algorithm when doing encryption and decryption, it will affect the efficiency of network training. Therefore, we use an improved version of Paillier, and the correctness and efficiency of the optimization have been proved in detail in [48].  
**Key generation**: Use \(\alpha\) as the divisor, if \(\lambda\) replaces the position of the \(\lambda\) in the private key. We can modify \(g\) in the public key and ensure that the order of \(g\) is \(\alpha n\).  
**Encryption**: Assuming that the plaintext is \(m\), the ciphertext is \(c\), and \(r\) is a random positive integer, and satisfies that \(r\) is less than \(\alpha\). The improved encryption process can be shown as:  
\[c=g^{m}\cdot(g^{n})^{r}\ mod\ n^{2} \tag{13}\]  
**Decryption**: The Decryption process can be shown as:  
\[m=\frac{L(c^{\alpha}mod\ n^{2})}{L(g^{\alpha}mod\ n^{2})}\ mod\ n \tag{14}\]  
It can be seen from the above algorithms that the biggest advantage of using \(\alpha\) instead of \(\lambda\) is in the decryption. The number of power operations has changed from \(2\cdot\lambda\) times to \(2\cdot\alpha\) times. Since \(\alpha\) is a divisor of \(\lambda\), the time overhead has been significantly reduced. The computational complexity of Native Paillier is \(\mathcal{O}(\left|n\right|^{3})\), and the computational complexity of improved Paillier is \(\mathcal{O}(\left|n\right|^{2}\left|\alpha\right|)\)[49].