esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Models
#### 2.7.2 k-Nearest Neighbors (k-NN)  
The k-NN method is a simple non-parametric technique that offers several advantages in modeling. k-NN is effective for large training data and robust to noisy data [24]. The k-NN algorithm compares each test sample against the nearest k samples from the training data. Each test sample is classified into the most common class among the k-training samples closest to it in the feature space [25]. Higher k will produce results with greater generalization, while lower k will result in a very complex decision boundary [26].
## 2 Materials and Methods
### Patients and Data Collection  
A total of 312 teeth of 52 individuals were examined in this study from May to December 2020. CBCT images of individuals over 15 years old were chosen by simple sampling method. The chronological age was determined using date of imaging and birth date for each patient. Patients were contacted using the recorded phone numbers in case their age and sex data was not available.  
Inclusion criteria were patients over 15 years old, availability of 6 anterior teeth (central incisor, lateral incisor, and canine) of the maxilla or mandible in the images, absence of pathologic lesions, cavities, or restorations, periodontal diseases, pulp stones, occlusal trauma, endodontic, restorative, or prosthetic treatment, and high quality of the images. In addition, due to registration of history and other considerations in the CBCT archive, patients with developmental anomalies or history of surgery of trauma in the craniofacial region were not included. Patients were excluded if their age and sex data was not recorded and were unavailable when contacted by phone. A total of 65 patients met the inclusion criteria and 13 of them were excluded, leaving 52 individuals for the study.  
CBCT images were obtained by a Giano HR CBCT scanner (NewTom, Verona, Italy) with 360 degrees rotation with a pixel size of 150 \(\upmu\)m. Imaging features of this device were pulsed exposure with a maximum voltage of 90 kV, exposure current of 7 mA, and total exposure time of 8 s. Images were saved using NNT Viewer Version 10.1 software (NewTom, Verona, Italy) in digital imaging and communication in medicine (DICOM) format.
# Worldwide Coverage Mobile Systems for
## 4 Ku-Band Antennas Design Techniques
### Primary Focus Antenna (PFA)  
Figure 2 shows the primary focus antennas, also known as satellites dish, have a front profile that is a perfect circle. This antenna is completely symmetric, and the side profile is a parabola, as a difference with offset antennas, wave-frontier toroidal antennas, or double focus antennas.  
In this kind of antennas, the focal point is in the middle point of the shaped plate. The antenna LNB faces the dish, mounted in the vertical axis in the spotlight of the set, in order to receive at such point the signal reflecting on the parabola, concentrating while adding coherently. The size of this kind of antenna is typically over 1.2 m in diameter. Table 9 shows the main advantages and disadvantages of this type of antenna.  
Mesh, Cassegrain, and Gregory are other subfamilies with a different implementation of the primary focus antennas. In the case of Mesh dish, the plate is substituted by some kind of metal grid, avoiding the accumulation of water, snow, etc. This implementation is lighter than the others.
# Improvement in Signal Phase Detection Using Deep Learning with Parallel Fully Connected Layers
## 2 Methods
### Structure of the Neural Network  
This study performs the training using a convolutional neural network that consists of three convolutional layers; two pooling layers; and 1, 2, 4, 8, 16, or 32 parallel fully connected and output layers. Figure 4 shows a network composed of four fully connected layers as an example. The number of output layers corresponds to the number of signal phases that are simultaneously classified, and one output layer corresponds to one signal phase. We note that the outermost signal phases are excluded because they do not have sufficient information for classification. Therefore, the input image size must vary with the number of each output layer, as listed in Table 1. As the number of signal phases to be simultaneously classified increases, the network becomes more complex while the number  
Figure 3: Schematic diagram of the optical setup used in the experiment.  
of input images that are required to recover all signal phases decreases. All convolutional layers use the rectified linear unit (ReLU) function as the activation function. The first convolutional layer has 32 filters, and the remaining two layers have 64 filters. The output layer has a Softmax function set to classify the target signal phase into four values: 0, \(\pi/2\), \(\pi\), and \(3\pi/2\). We also use Dropout [31], which has a 0.5 probability of disabling neurons after each pooling layer, to prevent overfitting of the convolutional neural networks. A categorical cross entropy is used as a loss function, which is minimized using Adam [32] with a batch size of 32.
## 2 System Model and Power Amplifier Operations
### Is-Mimo-Ofdm and PAPR
The signal transmitted after precoding, \(\mathbf{X}\), can be represented as follows:  
\[\mathbf{X}=\zeta\mathbf{W}\mathbf{S}, \tag{5}\]  
where \(\zeta\) is the TX power normalization factor, which is approximated as \(\zeta=\frac{1}{\left\|\mathbf{W}\mathbf{S}\right\|_{F}}\approx\sqrt{\frac{N_{ t}}{K}}\), \(\left\|\cdot\right\|_{F}\) denotes the Frobenius norm, \(\mathbf{W}\) is the \(N_{t}\times K\) precoding matrix to reduce the IUI, and \(\mathbf{S}\) is the \(K\times 1\) message signal vector.
## 3 Results and Discussion
### Determination of Compression Density of Sea Buckthorn During Pressing  
The degree of compression is characterized by the ratio of pressing weight, product density, and modulus of elasticity when pressing dry materials [23]. In this research, the determination of the compression ratio is complicated by the fact that a liquid phase is extracted from sea buckthorn. When the liquid phase is separated from sea buckthorn, there is a change in volume, mass, and phase composition of the raw material. Consequently, the change in compression density of sea buckthorn is directly related to its moisture content. Compression density is a critical parameter that reflects the degree to which the fruit mass is compacted in the pressing chamber, directly influencing the juice extraction efficiency. Understanding how the compression density varies with mechanical adjustments provides deeper insight into the pressing process and aids in optimizing the equipment settings for maximum yield [24; 25].  
The springs with higher maximum forces (Springs 2 and 4) consistently produced higher compression densities across all rotation speeds. Spring 4, despite having a slightly lower maximum force than Spring 2, achieved the highest compression densities, reaching up to 951 kg/m\({}^{3}\) at 200 rpm. The superior performance of Spring 4 suggests that factors beyond maximum force, such as spring stiffness and material properties, play significant roles. The design of Spring 4 may allow for more effective pressure transmission and better adaptation to the pressing mechanics of sea buckthorn fruits (Figure 8).  
Figure 7: Change in juice yield during sea buckthorn pressing depending on the design of the pressure control spring and rotation speed (different letters above the bars indicate significant differences with increasing the rotation speed, \(p<0.05\)).
# Universally Composable Oblivious Transfer with Low Communication
## 3 Constitutions of Protocol
### Symmetric Encryption Function  
A standard UC-secure OT function consists of a UC-secure ROT function and an encryption function. To ensure the confidentiality and integrity of the information during the encryption and decryption of the OT protocol, we use an authentication symmetric encryption function. The authentication encryption algorithm [27] can achieve both encryption and authentication.  
Like the hash function, we use the intermediate value \(\left(\left(R_{q}^{l\times 1}\times R_{q}^{l\times 1}\right)\times R_{t} \right)\times R_{q}^{n}\) as an additional input to the authentication symmetric encryption function to distinguish among instances. As a component of UC-secure OT protocol, the authenticated symmetric encryption function has the character of non-committing and robustness. Let \(\mathcal{K}\), \(\mathcal{M}\), and \(\mathcal{C}\) represent the authenticated encryption's key, plaintext, and ciphertext space, respectively.  
**Definition 3** (Non-committing): _We say an authentication encryption scheme (Enc, Dec) is non-committing if there exists PPT algorithm \(\mathcal{S}_{1}\). \(\mathcal{S}_{2}\) such that \(\left(e,k\right)\) and \(\left(e^{\prime},k^{\prime}\right)\) are distinguishable where \(k\leftarrow\mathcal{K}\), \(r\leftarrow\mathcal{U}\left(\right)\), \(e\leftarrow E(k,r,M)\), \(e^{\prime}\leftarrow\mathcal{S}_{2}(1^{\mathcal{K}})\), and \(k^{\prime}\leftarrow\mathcal{S}_{2}(e^{\prime},r,M)\)._  
**Definition 4** (Robustness): _Given additional input \(r\), \(S\) is a set of random keys from \(\mathcal{K}\). Let \(V_{S,r,e}\) denote a set of valid keys that satisfies \(Dec(k,r,e)\
eq\bot\), for the given ciphertexts \(r\), \(e\), \(i\), \(e\), \(We\) say (Enc, Dec) is robust if, for every ciphertext, the probability of \(|V_{S,e}|>1\) is negligible._  
As the ciphertexts of common symmetric encryption algorithms do not contain any commitment to the plaintext messages or encryption process, it is impossible to determine whether the key inputted for decryption is correct. However, if the authentication code obtained by decrypting the ciphertext with the inputted key does not match the authentication sequence obtained by encrypting, the algorithm directly outputs \"Failure.\" and does not output the wrong decryption result. Hence, it has higher security than common symmetric encryption.  
The protocol is robust because the probability of getting the same authentication code using different keys and additional inputs is negligible.
# Predictability of ClinCheck in Overbite Correction with Aligners: A Systematic Review
###### Abstract  
Background: The use of aligner therapy for open bite and deep bite correction has increased in contemporary society. There is no evidence that unify the results present the in literature regarding a real comparison between clinical outcomes and the results predicted by the ClinCheck software 3.0 (Align Technology, Santa Clara, CA, USA). Furthermore, the literature shows conflicting data about the protocols and not all authors compare the programmed movements and the clinical results obtained for the overbite correction. Therefore, the aim of this systematic review is to assess the predictability of ClinCheck in the correction of vertical discrepancies by comparing the planned outcomes with the actual clinical results performed with clear aligners. Methods: The research question focused on the effectiveness of ClinCheck in predicting the actual correction of deep bite AND open bite in adult patients. Five electronic databases (PubMed, Scopus, Embase, Web of Science and Cochrane Library) were investigated, with the following keywords: overbite AND aligners. A quality assessment was performed using the Newcastle-Ottawa scale, while the risk of bias was evaluated using the ROBINS-I tool 2.0. PROSPERO ID: CRD420251078610. Results: Out of a total of 838 records initially screened, seven studies fulfilled the inclusion criteria and were ultimately selected for this systematic review. The analysis focused on assessing the divergence between the overbite correction predicted by ClinCheck and the outcomes observed in clinical practice. Conclusions: ClinCheck demonstrated a predictability of 62.1% for overbite correction in open bite cases and 41.5% in deep bite cases. However, not all studies report the planned tooth movements. Among the studies that addressed this aspect, the majority reported no significant association between the overbite correction predicted during treatment planning and the results ultimately achieved--except for one study, which demonstrated significant accuracy in achieving absolute extrusion in the correction of open bite.  
systematic review; overbite; aligner
# Model Test Analysis of Groundwater Level Fluctuations on
## 2 Geological Setting of the Study Area  
### Meteorology and Hydrology  
The study area is located in Lingui District, Guilin, at the southern end of the Xianggui Corridor, at an average altitude of 150 m. Meteorological observations show that the annual mean temperature in the study area ranges from 17.0 to 20.0 \({}^{\circ}\)C, with cold winters, the average daily temperature in January and February remaining at 5 to 7 \({}^{\circ}\)C, and the annual extreme minimum temperature at \(-5\) to 0 \({}^{\circ}\)C. The summer and autumn seasons are significantly warmer, with extreme maximum temperatures of 37.0 to 39.0 \({}^{\circ}\)C. Temperatures are significantly higher in summer and autumn, with annual maximum temperatures ranging from 37.0 to 39.0 \({}^{\circ}\)C. The total annual precipitation is 1266.0-1986.0 mm, and the spatial and temporal distribution of rainfall is uneven, with more precipitation in spring and summer, and severe droughts in autumn and winter, with heavy rainfall mainly concentrated in May-June. The water level in the Creek is 154.60 m. The water level in the Creek is affected by the seasons and is mainly recharged by rainfall and domestic wastewater from the surrounding area, with a small volume of water. The water table in the study area is basically located at the bedrock surface and is subject to seasonal variations, with a range of 2-3 m.
# Physical Hybrid of Nanographene/Carbon Nanotubes as Reinforcing Agents of NR-Based Rubber Foam
## 3 Results and Discussion
### Hardness and Resilience  
Hardness and resilience tests were used as a criterion to determine the elastic properties of NR/CNT/GNS rubber foams, and the data are illustrated in Figures 5 and 6, respectively. As shown in Figure 5, by adding GNSs and CNTs to the NR matrix, a significant change in hardness was observed with low filler loading levels (0.5-1 phr). However, the hardness stabilized and did not display statistically significant change beyond 1 phr loading.  
Figure 4: Variation of average cell size of NR/CNT/GNS foam nanocomposites versus. CNT/GNS hybrid content.  
Figure 5: The variation in hardness in NR foams containing different content of GNS/CNT hybrid.  
A correlation between the hardness of the foams was observed with the increase in the hybrid filler loading levels, while the correlation between the resilience and the hybrid filler content was inverse. For instance, adding only 0.1 phr of CNT/GNS to the NR foam increased the hardness from 35 to 39 Shore A and decreased the resilience from 65% to 60%, while adding 2 phr of CNT and GNS had no effect on the hardness while substantially reducing the resilience (48\(-\)50%). Increasing the CNT/GNS content decreased the resilience because of the strong filler-rubber interaction, higher crosslink density, and lower NR chain flexibility. On the other hand, hardness could increase via either or both mechanisms: hindering the rubber chain mobility caused by higher curing density and filler-rubber interactions and reducing the average foam cell size (discussed above). Similar behavior was observed for polymer solid or foam composites reinforced by other types of nano reinforcements [18; 30].
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 5 Performance Evaluation and Results
### Results
#### 5.5.3 Varying the Application Message Send Intervals
The results demonstrate that the _Adaptive Interval_ approach using seven minutes and _No Variation_ approaches resulted in very similar values, since the difference was very small, only 3.18 ms. However, the difference between the 15 min _Adaptive Interval_ approach and the _No Variation_ approach was 22.155 ms, about a 10% difference. The higher the number of application messages, the greater the required time to transmit the messages and, therefore, higher the delay. Hence, the _Adaptive Interval_ approach using 15 min sends a smaller number of messages for the regular application; thus, this variation results in shorter delay than the _No Variation_ approach. In this sense, the use of different message send intervals helped to improve service differentiation for the critical application, prioritizing more the traffic of this application. The average energy consumed of all network nodes can be seen in Figure 12(b), except for sink. Again, there was a slight difference between the two interval variations of the _Adaptive Interval_ approach. The _Adaptive Interval_ approach surprisingly reached lower consumption than the _No Variation_ approach, which is 9.87% (1204.82 mA) and 12.18% (1486.78 mA) lower in 7 min and 15 min, respectively. It is worth pointing out that the Instance 30 has a higher application message sending interval (7 min and 15 min) than Instance 10. Therefore, the nodes' radios remain in _sleep mode_ for a longer amount of time in _Adaptive Interval_ approaches. Although with the greater amount of control and application messages sent and received with the _Adaptive Interval_ approach, power consumption is significantly lower in this mode [34], which enabled a reduction in power consumption. Despite the slight difference between the message sending intervals between instances 10 and 30 (2 and 10 min, respectively), promising results can be seen from the use of different instances for the same application using a different send interval for each instance of the same application. For example, a greater reduction in energy consumption was achieved even though there were more control and application messages. A reduction in delay of the critical application showed that using separate  
Figure 13: Average end-to-end delay for critical application (Instance 20) and average energy consumption. intervals for the same regular application can help to improve delay performance, thus providing service differentiation for more critical applications.
## 3 Space Time Block Code (STBC)
### Alamouti STBC System Model
The received signal at the first time slot is:  
\[y_{1}=h_{1}x_{1}+h_{2}x_{2}, \tag{7}\]  
The received signal at the second time slot is:  
\[y_{2}=-h_{1}x_{2}^{*}+x_{1}^{*}h_{2}, \tag{8}\]  
In order to predict the transmitted symbol successfully, two different methodologies are deployed at the UE. The first methodology is based on the Maximum Likelihood (ML) estimation decoder only, which depends on the minimum Euclidean distance. Supposing that all the signals in the modulation constellation are equiprobable. The ML decoder chooses a pair of signals \((x_{1},x_{2})\) from the signal constellation to minimize the distance metric over all possible values of \(\hat{x}_{1}\) and \(\hat{x}_{2}\)[35]. Hence, the number of steps required to detect the correct symbol by ML is \(M^{2}\). The process of ML detection can be understood as the act of minimizing the following decision metric (9):  
\[d^{2}(y_{1},h_{1}\hat{x}_{1}+h_{2}\hat{x}_{2})+d^{2}(y_{2},-h_{1}\hat{x}_{2}^{ *}+h_{2}\hat{x}_{1}^{*})=|y_{1}-h_{1}\hat{x}_{1}-h_{2}\hat{x}_{2}|^{2}+|y_{2}+ h_{1}\hat{x}_{2}^{*}-h_{2}\hat{x}_{1}^{*}|^{2}, \tag{9}\]  
This decoding scheme necessitates a comprehensive search of all possible pairs and typically, its complexity increases exponentially with the number of transmit antennas and higher modulation orders. On the other hand, the second methodology is based on the combination of the received signals in the first stage. \(\widetilde{x}_{1}\)_and_\(\widetilde{x}_{2}\) are extracted by simultaneously solving Equations (7) and (8). Then, the second stage is implemented using the ML detector to apply the decision rules. In this case, ML decoding can be more simplified. The decision statistics of the symbols \(\widetilde{x}_{1}\)_and_\(\widetilde{x}_{2}\) can be calculated as follows [35]:  
\[\widetilde{x}_{1}=\frac{1}{\left\|\mathbf{H}\right\|^{2}}(h_{1}^{*}y_{1}+h_{2}y_{ 2}^{*}), \tag{10}\]  
\[\widetilde{x}_{2}=\frac{1}{\left\|\mathbf{H}\right\|^{2}}(h_{2}^{*}y_{1}-h_{1}y_{ 2}^{*}), \tag{11}\]  
\[\left\|\mathbf{H}\right\|^{2}=\sum_{i=1}^{2}\left\|h_{i}\right\|^{2} \tag{12}\]  
The likelihood received symbols \(\hat{x}_{1}\) and \(\hat{x}_{2}\) will be the nearest estimated symbol in the constellation could be done by minimizing the distance between the decision statistics of the symbol \(\widetilde{x}_{\text{i}}\) and the set of all the possible symbols in the constellation \(s_{\text{i}}\in\mathbf{S}\)[35]. \[\hat{x}_{\text{i}}=arg\underset{s_{\text{i}}\in\mathbf{S}}{\text{min}}|s_{\text{i }}-\widetilde{x}_{\text{i}}|^{2} \tag{13}\]  
Hence, the number of steps required to detect the correct symbol by ML is 2M. So, the second method will be used through this paper to detect the received symbol, since it requires fewer steps for the ML detector.
## 3 The Vibration Model of Circuit Unit
### Dynamic Equation of Circuit Unit  
Since only the longitudinal vibration characteristics of the circuit unit are investigated, the system can be regarded as a damped single-degree-of-freedom system under external excitation [28]. Based on the mechanical structure of the circuit unit mounting described above, the circuit unit is simplified to a mass, and the mass is represented by \(m\). The vibration-damping unit is simplified as a set of spring-damper systems, where stiffness and damping are \(k\) and \(c\).  
Due to the influence of gravity, the static equilibrium position of the damping system is not the center point. The static equilibrium position is taken as the origin of the coordinate  
Figure 2: Schematic diagram of the vibration model of the MWD.  
system to establish a coordinate system. The downward direction is specified as the positive direction. Given the role of damping in energy consumption, vibration analysis is often used in the principle of energy equivalence. Moreover, the circuit unit quality does not change with movement. In this system, the elastic force of the system can be simplified to linear, and the damping can be simplified to linear viscous damping. As the deformations of the two groups of springs are consistent, they can be regarded as a set of springs in parallel with equivalent stiffness: \(k=k_{1}+k_{2}\). Damping is the same, with equivalent damping \(c=c_{1}+c_{2}\). Therefore, the model is further simplified, as shown in Figure 3.  
The force analysis of the circuit unit is carried out, and the mass \(m\) is taken as the separator. According to [PERSON]'s second law and D'Alembert principle, the vibration equation can be obtained.  
\[m\ddot{x}(t)=-c\big{[}\dot{x}(t)-\dot{y}(t)\big{]}-k[x(t)-y(t)] \tag{1}\]  
This is expressed as follows:  
\[m\ddot{x}(t)+c\dot{x}(t)+kx(t)=c\dot{y}(t)+ky(t) \tag{2}\]  
The above equation is the forced vibration response of the circuit unit under external excitation, where \(m\) is the mass of the circuit unit in kg, \(k\) is the stiffness of the damping mechanism, \(c\) is the damping coefficient of the damping mechanism, \(\ddot{x}(t)\) is the acceleration of the measuring mechanism in \(\text{m}/\text{s}^{2}\), \(\dot{x}(t)\) is the velocity of the measuring mechanism in \(\text{m}/\text{s}\), \(\dot{y}(t)\) is the speed of the drill string in \(\text{m}/\text{s}\), \(x(t)\) is the displacement of the measuring mechanism in \(\text{m}\), and \(y(t)\) is the displacement of the drill string in m.