esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Bite Correction Using a Mid-Palatal TAD with an Intrusion TPA: Anteroposterior Position of TAD
## 3 Interoposterior Position of a Mid-Palatal TAD
### Case 3 (Posterior Intrusion, Figures 8 and 9)  
_Case 3_ is a severe open bite and an example of the \"posterior intrusion\" type. The TAD is located posterior to the first molar (Figure 8I). The hooks of TPA are extended distally. Maximally third molars were extracted when starting the intrusion. On superimposition (Figure 9), there was posterior intrusion without anterior intrusion. Maximally teeth were moved distally (Figure 9C). These mechanics are favorable for severe skeletal Class II open-bite cases and can be used to efficiently remove the posterior wedge and close the open bite. The advantage is that an open bite can be solved quickly by the intrusion of the posterior teeth and extrusion of the anterior teeth.  
Figure 7: Posterior intrusion (**A**,**C**,**E**) vs. total intrusion (**B**,**D**,**F**). (**A**,**C**,**E**) In _Case 3_, to make the posterior intrusion more efficient, the screw and hooks were placed distally. The posterior wedging teeth were intruded more efficiently. (**B**,**D**,**F**) In _Case 4_, the screw was placed mesial to the first molar. There was an intrusion of total maxillary teeth. When the patient complains of a gummy smile, total intrusion of maxillary teeth is recommended. The drawings in (**A**,**B**) were obtained by superimposing the cephalogram _after leveling_ with the post-treatment one; black line: before starting intrusion, red line: after the intrusion.  
Figure 8: _Case 3_. Posterior intrusion. (**A–E**) Initial intraoral photos showing a narrow upper arch and a wide lower arch. There were three incisors in the mandible. (**F–J**) After leveling, the posterior intrusion was started. (**K–O**) Eight months of intrusion. The open bite was decreased. Space for the missing mandibular central incisor was opened up. Overjet was decreased with the proclination of lower incisors and the counterclockwise rotation of the mandible. (**P–T**) Debonding. (**U–Y**) Implant and crown restoration of the mandibular right central incisor.  
Figure 9: _Case 3_. Skeletal and dental changes of “posterior intrusion” case. (**A**) The superimposition of the initial and posttreatment cephalograms. (**B**) Maximillary superimposition showing (1) intrusion and distal movement of the upper molar, and (2) extrusion and slight retraction of the upper incisor. Mandibular superimposition showing proclination and extrusion of lower incisors. (**C**) Superimposition of cephalograms after leveling (blue line) and post treatment (red line). This superimposition excludes the incisor extrusion during the compensating curve’s leveling. The molar was intruded and moved distally. The incisor was extruded and retroclined.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Implementation and Validation of MBMQA  
The validation stage of the MBMQA routing scheme was completed by comparing the mathematical formulas and computation of the desired settings against parameter values received. In addition, route calculation functionality of the MBMQA routing scheme was performed based on device resources (RB, Mobility, and QL). The decision of the routing metric was compared with conventional MP-OLSRv2 and MEQSA-OLSRv2 schemes. For the purpose of analysis, all the devices were implemented as mobile with variable speed. In order to demonstrate the effectiveness of the proposed scheme and avoid devices with fewer resources in the optimal path, eight devices with different resources were randomly distributed, as shown in Figure 3. Devices 1 and 8 represent the source-destination pair, and 2, 3, 4, 5, 6 and 7 are the intermediate devices with different attributes. The proposed approach selected devices 6 and 7, which appear to have sufficient resources for route selection, e.g., higher residual battery, comparatively lower mobility and supplementary free queue slots. Therefore, the proposed approach selected route \(1\to 6\to 7\to 8\) as an optimal route among available devices by comparing available devices. In addition, the shortest path, \(1\to 2\to 3\to 8\), was not selected for the optimal path due to the value of the low resources. In addition, device resource values such as RB, Mobility, and QL were monitored during the simulation running time. As a result, it can be seen that the MBMQA scheme is a strict energy, mobility, and queue length-aware routing scheme.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 3 Proposed Routing Optimization Algorithm
### Principle and Implementation
#### 3.1.2 Calculation of the Routing Evaluation Parameters  
Based on the forward neighbor information table, the routing evaluation parameters of total forward neighbors, including the energy equilibrium degree, single-hop transmission ratio, effective forward rate, cache queue index, and residual energy level factor, can be calculated according to the routing model of Section 2. In the process, node \(i\) gathers the relevant information about the forward neighbor \(j\) for subsequent routing selection. This information includes its residual energy, the distance to the forward neighbor \(j\) and the sink, the residual energy and the queue length of node \(j\), the distance between node \(j\) and the sink, and so on.
## 3 Methods
### Unsupervised Learning for Automatic Hierarchy  
A challenge we faced in this section was the inability to know in advance what types of images users would generate with generative AI. This means that we could not construct a pre-labeled dataset to classify user results or predefine the number of clusters for automatic hierarchy organization. Therefore, we addressed this challenge using HDBSCAN [28], a density-based clustering algorithm within unsupervised learning. The main operational process of HDBSCAN consists of four steps. First, the mutual reachability distance is calculated. HDBSCAN introduces the concept of mutual reachability distance, a transformed distance metric that considers both core distances and pairwise distances between points. This enables clusters to adapt to local density variations dynamically. In the second step, a hierarchical tree (dendrogram) of clusters is constructed based on the mutual reachability distance. This tree captures small, densely packed clusters and larger, more dispersed ones. The third step involves evaluating the stability of clusters across various density thresholds. Stability is measured by the degree to which clusters persist within the hierarchy. The most stable clusters are selected as the final results. In the fourth and final step, points that do not belong to any cluster from the third step are classified as noise. This allows for the natural handling of outliers and noisy data.  
Through these processes, HDBSCAN does not require the number of clusters (k) to be predefined, unlike centroid-based methods such as K-means [29]. Furthermore, HDBSCAN exhibits robust performance against noisy data by leveraging its transformed distance metric, outperforming traditional density-based clustering algorithms such as single linkage [30]. Ultimately, this approach enables more efficient clustering. Additionally, compared to DBSCAN [31], HDBSCAN has the advantage of reflecting local density information and achieving hierarchical clustering based on the structure of the input data. For these reasons, we adopted HDBSCAN in our study and set the hyperparameters to a minimum cluster size of 2 and an alpha value of 0.001.
# Surface Coatings and Treatments for Controlled Hydrate Formation: A Mini Review
###### Abstract
Nanoparticles [25], Carbon-based materials [26] including graphene [27] and activated carbon [28] that provide large surface area and ample nucleation sites, which promote the kinetics of hydrate formation, have also been reported. On the other hand, Numerous studies are available in the literature where distinct surfaces [29, 30, 31, 32, 33, 34] are used for this purpose. Metal-induced hydrate nucleation has also been reported recently [35, 36]. However, the use of surface treatments or coatings to accelerate the hydrate formation is in its infancy. [PERSON] et al. [37] critically reviewed the role of functionalized solid surfaces (mainly particles) and a confined environment in controlling hydrate formation and the underlying mechanism. The authors concluded that open hydrophobic solids can promote the hydrate formation kinetics, whereas hydrophilic solids can reduce it. The mechanism proposed was that hydrophobic solids can order the structure of water and enhance the density of hydrate formers near the solid/liquid interface, whereas hydrophilic surfaces can distort the water structure and deplete the density of hydrate formers near the solid/liquid interface. The mechanism in the case of confined systems was found to be rather complex. The area of smart coatings for controlled hydrate formation is relatively new and not yet reviewed. [PERSON] et al. [38] dedicated just a small section in their review on coating technology for hydrate management. [PERSON] et al. [39] also presented briefly in their review the state of the art on the surface effect on hydrate nucleation. A survey of the literature until mid 2021 indicates that significant progress has been made at this front in the last decade that resulted in 16 published articles [40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55], 3 patents [56, 57, 58], and a few commercial products [59, 60, 61, 62] already available in the market. Therefore, the aim of this work was to review the recent efforts made in the development of functionalized solid surfaces via coating or surface treatment and evaluating the role of the resulting solid/solid interactions in controlling hydrate nucleation and growth using model compounds such as tetrahydrofuran (THF) and cyclopentane (CP) at atmospheric pressure and gaseous molecules (CH\({}_{4}\), CO\({}_{2}\), etc.) in realistic conditions of high pressure. A list of the types of coatings, substrates used, hydrate-forming systems, and the P-T conditions in which the studies were carried out are summarized chronologically in Table 1.
# Individuals Identification Based on Palm Vein Matching under a Parallel Environment
## 6 Conclusions  
In this paper, we introduce the CPM algorithm as an effective and efficient alternative to calculate the displacement between palm vein images for individuals recognition based on deformation information. In addition, in order to increase the number of queries per second, considering a real-world application on a large database, we implement a master-worker scheme under a multi-core platform for the matching process.  
Our proposed methodology includes pre-processing techniques as a robust ROI selection, intensity normalization, and histogram equalization process, aiming to enhance and to preserve the local details of the vein patterns. In addition, the DAISY descriptor and CPM algorithm are combined in a parallel matching process with the aim of reducing the execution time and increasing the number of processed queries per second of the recognition pipeline. Experimental validation on three well-known databases demonstrates that our proposed approach reaches the state-of-the-art results while it is considerably superior in time efficiency, overcoming the limitations of contactless palm vein systems. To the best of our knowledge, the method we present is the first that aims to reduce the influence of deformations by using it as discriminative information for palm vein recognition.  
Moreover, as future work, we will study the parallelization of the DAISY descriptor and the CPM algorithm under hybrid parallel platforms in order to improve much more the performance of our method on a real-world application for individuals' identification.  
Conceptualization and methodology, R.H.-G. and M.M.; software and validation, C.R. and R.H.-G.; formal analysis, R.H.-G. and R.J.B.; writing--original draft preparation, R.H.-G.; writing--review and editing, R.H.-G. and R.J.B.; supervision, R.J.B. and M.M.; project administration, R.J.B.; funding acquisition, R.J.B. and M.M.  
This research was funded by Project CONICYT FONDEF/ Cuarto Concuso IDeA en dos Etapas del Fondo de Fomento al Desarrollo Cientifico y Tecnologico, Programa IDeA, FONDEF/CONICYT 2017 ID17i0254.  
Portions of the research in this paper use the CASIA-MS-PalmprintV1 collected by the Chinese Academy of Sciences' Institute of Automation (CASIA). Portions of the research in this paper use the VERA-Palmvein Corpus made available by the Idiap Research Institute, Martigny, Switzerland.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(i\) & **Approach** & \(z=(R_{0}-R_{i})/SE\) & \(p\) & **[PERSON]** \\ \hline
6 & [PERSON] et al. [68] & 1.96 & 0.0495 & 0.0083 \\
5 & [PERSON] et al. [29] & 1.64 & 0.1017 & 0.01 \\
4 & [PERSON] and [PERSON] [28] & 1.31 & 0.1904 & 0.0125 \\
3 & [PERSON] [34] & 0.98 & 0.3261 & 0.0167 \\
2 & [PERSON] et al. [38] & 0.65 & 0.5127 & 0.025 \\
1 & [PERSON] et al. [41] & 0.33 & 0.7434 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Approaches sorted by \(p\)-value and adjusting of \(\alpha\) resulting from Holm procedure at \(\alpha=0.05\).
# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 4 Simulation
### Evaluation Results
#### 4.2.4 Comparison of Performance with Data Augmentation Technique Using Gaussian Noise
This suggests that when augmenting data using arbitrary PDFs, such as Gaussian distribution, large deviations from the actual RTFs can occur, potentially degrading the model's performance. In contrast, our proposed method tends to generate data that closely resembles real-world data, thereby enhancing the model's performance.
# Face Swapping Consistency Transfer with Neural Identity Carrier
## 3 Methods
### Training Losses
#### 3.4.2 Neural Identity Carrier Training  
Given target and reference image/video \(X_{t}\), \(X_{ref}\) and an identity identity image \(X_{id}\), the transfer network is trained by minimizing  
\[\mathcal{L}_{transfer}=\mathcal{L}_{primary}+\mathcal{L}_{3D} \tag{9}\]  
To learn the process of identity transformation, a primary loss is essential. In consideration of the artifacts in face-swapping proxy, we model the uncertainty at the same time. We adjust the aleatoric uncertainty loss to fit our scenario. The primary loss is formulated as  
\[\begin{split}\mathcal{L}_{primary}=&\frac{1}{2\sigma (X_{i})^{2}}||VGG(X_{o})-VGG(X_{ref})||^{2}\\ &+\frac{1}{2}\log\sigma(X_{i})^{2}\end{split} \tag{10}\]  
where \(VGG(\cdot)\) denotes the \(VGG\) features which consist of features from layers _conv1\(2\), _conv2\(2\), _conv3\(2\), _conv4\(2\) and _conv5\(2\). The \(\sigma\) denotes the model's noise parameter--predicting how much noise we have in the outputs. It is noteworthy that we learn the noise parameter \(\sigma\) implicitly from the loss function. \(\mathcal{L}_{primary}\) is a basic perceptual error between \(X_{o}\) and \(X_{ref}\). This loss can basically guarantee that the NICe can learn identity transformation from arbitrary face-swapping proxy.  
To enhance the quality of simulation, we adopt 3D losses with trained static detail extractor \(E_{d}\) and coarse encoder \(E_{c}\). 3D losses consist of three components, albedo loss \(\mathcal{L}_{albedo}\), shape loss \(\mathcal{L}_{shape}\) and detail loss \(\mathcal{L}_{detail}\), formulated as  
\[\mathcal{L}_{3D}=\mathcal{L}_{albedo}+\mathcal{L}_{shape}+\mathcal{L}_{detail} \tag{11}\]  
In consideration of the swapping area, the skin consistency between the face and the neck can be perceived by the human vision system easily. We utilize albedo loss to improve albedo consistency between \(X_{o}\) and \(X_{t}\). The albedo loss is defined as  
\[\mathcal{L}_{albedo}=\|\alpha_{X_{o}}-\alpha_{X_{t}}\| \tag{12}\]  
where \(\alpha_{X_{o}}\) and \(\alpha_{X_{t}}\) are the albedo coefficients of \(X_{o}\) and \(X_{t}\), encoded by \(E_{c}\) respectively.  
The shape loss \(\mathcal{L}_{shape}\) focuses on identity preserving. Formally, we minimize  
\[\mathcal{L}_{shape}=\|\beta_{X_{o}}-\beta_{X_{id}}\| \tag{13}\]  
where \(\beta_{X_{o}}\) and \(X_{id}\) are the shape parameters of \(X_{o}\) and \(X_{id}\) encoded by \(E_{c}\) respectively.  
The detail loss \(\mathcal{L}_{detail}\) can greatly enhance detail information. We define it as  
\[\mathcal{L}_{detail}=\|\delta_{X_{o}}-\delta_{X_{id}}\| \tag{14}\]where \(\delta_{X_{o}}\) and \(\delta_{X_{id}}\) are detail information's latent code of \(X_{o}\) and \(X_{id}\) encoded by \(E_{d}\) respectively.
## 4 Experiment Results and Discussion
### Experiments on the MNIST and Fashion-MNIST Dataset
#### 4.1.3 Ablation Study with the Scaled Thresholds  
To examine the effect of the threshold on the accuracy of the converted SNNs with low latency, we scaled the thresholds, suggested by the proposed threshold balancing technique, with the factor \(\alpha\) (\(0<\alpha\leq 1\)), in order to increase the firing rates of the spiking neurons. Figure 9a,b show the accuracies of the converted SNN models by the proposed method for different scaling factor values \(\alpha\) on both datasets, respectively.  
Figure 8: Performance comparison of the proposed method and other CNN–SNN conversion methods on the Fashion-MNIST dataset [25; 26; 27].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Latency \(T_{infer}\)Methods** & [25] & [26] & [27] & **Our Method** \\ \hline
**512** & 74.65\% & 74.73\% & 66.15\% & **92.11**\% \\
**256** & 73.65\% & 73.38\% & 51.01\% & **91.76**\% \\
**128** & 68.89\% & 68.14\% & 36.69\% & **91.36**\% \\
**64** & 60.15\% & 58.11\% & 19.45\% & **90.77**\% \\
**32** & 45.07\% & 43.66\% & 13.13\% & **89.29**\% \\
**16** & 17.73\% & 17.82\% & 10.0\% & **85.76**\% \\
**8** & 10.14\% & 10.15\% & 10.0\% & **73.84**\% \\
**4** & / & / & 10.0\% & **31.21**\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification accuracies vs. latencies for the compared methods on the Fashion-MNIST dataset.  
As observed in Figure 9a, with \(\alpha=0.6\), our converted SNN model achieved a quite similar accuracy to the corresponding CNN with a latency of 64 time steps, while it ensured a competitive accuracy of 98.94% with a short latency of 4 time steps. Note that, with a latency of 4 time steps, our converted SNN even attained higher accuracy than other methods in 64 time steps. In Figure 9a, with \(\alpha=0.4\), with a very short latency of 4 time steps, our converted SNN model even achieved higher accuracy compared with the other methods [25; 26] with 512 time steps. Moreover, our converted SNN ensured a competitive accuracy of 92.08% with 512 time steps with a conversion loss of less than 0.1%. Appendix D shows the classification accuracy of our converted SNNs with different scaled thresholds on both datasets.
# Collaborative Multi-Robot Formation Control and Global Path Optimization
## 3 Improved Global Path Planning with Ant Colony Algorithm  
### Classical Ant Colony Algorithm  
The ACO achieves optimal search of paths through information probability selection and pheromone identification between ant colonies and their environment. The probability of the \(k\)th ant choosing the next node location at the intersection is as follows:  
\[P_{ij}^{k}=\left\{\begin{array}{l}\frac{[\tau_{ij}(t)^{\pm}[\eta_{ij}]^{\beta} }{\sum\limits_{j\in\left\{allowed_{k}\right\}}[|\tau_{ij}(t)|^{\pm}|\eta_{ij} ]^{\beta}]},j\in allowed_{k}\\ 0,otherwise\end{array}\right. \tag{12}\]  
\[\eta_{ij}=\frac{1}{d_{ij}} \tag{13}\]  
where \(allowed_{k}\) is the \(k\)th ant selectable node, \(\alpha\) is the pheromone heuristic factor; \(\beta\) is the desired heuristic factor, \(\tau_{ij}\) is the pheromone concentration of path \((i,j)\), \(\eta_{ij}\) is the heuristic information, and \(d_{ij}\) is the Euclidean distance between the current node \(i\) and the node \(j\) to be selected. The pheromone update rule is as follows:  
\[\tau_{ij}(t+1)=(1-\rho)\cdot\tau_{ij}(t)+\Delta\tau_{ij}(t,t+1) \tag{14}\]  
\[\Delta\tau_{ij}(t,t+1)=\left\{\begin{array}{l}\frac{Q}{L_{k}^{\prime}},\ (i,j)\in P_{k}(begin,end)\\ 0,\ otherwise\end{array}\right. \tag{15}\]  
where \(\rho\in(0,1)\) is the pheromone volatility coefficient, \(\Delta\tau_{ij}(i,j)\) is the pheromone increment from node \(i\) to node \(j\), \(Q\) is the pheromone intensity; \(L_{k}\) is the path length of the \(kth\) ant in one cycle, and \(P_{k}(begin,end)\) is the path taken by ant \(k\) from the starting point to the end point.
## 3 Test Results and Analysis
### Deformation Analysis  
The displacement ductility coefficient \(\mu\) is defined as the ratio of the corresponding displacement to the limit point of the beam body. It is generally used to measure the deformation ability of the component. The greater the displacement ductility coefficient, the stronger the deformation ability of the component. The unreinforced contrast beams A0 and B0 had a good plastic deformation ability, and the displacement ductility coefficient was 3.81 and 7.29, respectively. The peak deflection and ductility coefficient of the reinforcement beams A1 and B1 were greatly reduced, and the corresponding ductility coefficient was 1.84 and 3.90, respectively. The ductility coefficient of the test beam was significantly reduced after strengthening the thin layer of epoxy mortar, mainly due to the sudden fracture of the reinforced thin layer after reaching the peak load.  
Therefore, in order to strengthen the beam in a subsequent study, we can consider adding fiber fabric or high-strength wire mesh to the reinforced thin layer.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Beam ID**} & \multicolumn{2}{c}{**Cracking Point**} & \multicolumn{2}{c}{**Yield Point**} & \multicolumn{2}{c}{**Peak Point**} & \multicolumn{2}{c}{**Limiting Point**} & \multirow{2}{*}{\(P_{m,0}\)} & \multirow{2}{*}{**Destruction Mode**} \\ \cline{2-2} \cline{5-10}  & \(P_{\alpha r}\)/kN & \(f_{\alpha r}\)/mm & \(P_{y}\)/kN & \(f_{y}\)/mm & \(P_{m}\)/kN & \(f_{m}\)/mm & \(P_{u}\)/kN & \(f_{u}\)/mm & \(P_{u}\)/km & \(P_{u}\)/km & \(P_{u}\)/km & \(P_{u}\)/km & \(P_{u}\)/km \\ \hline A0 & 26.06
## 4 Discussion
Both SIF and MODIS NDVI spatially approximated the extent of the 2010-2011 drought as compared to soil moisture and previous analysis [45], and SIF captured the response almost as well as did the NDVI, even if it has lower spatial resolution and much smaller energy, suggesting that SIF can also serve as an early indicator of drought in the future. However, state-of-the-art retrievals from GOME-2 are still limited by the sensor's design; the satellite was intended to monitor ozone, not SIF. Further, GOME-2 measurements occur at 0930 local time, well before the midday insolation and accompanying light saturation that causes stomata to close and thus shut down photosynthesis; vegetation may not be fully stressed at the time of SIF measurements. Taken together, these caveats suggest that GOME-2 SIF data may have under-reported water stress during the 2010-2011 drought. Measurements by an optimized sensor at peak insolation could serve as an improved record of vegetation stress. The spatial extent of the failed 2011 long rains was amplified by the reduced availability of water resources following the failed short rains in the previous season. Failed rainy seasons amplify the consequences of subsequent reductions in rainfall. The converse, however, was not true in 2010-2011: anomalously high rainfall in MAM 2010 did not protect the region from the failed rains of OND 2010.