esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aims to assess the presence and progression of TMD in adult patients undergoing orthongathic surgery to correct dentoskeletal open bite and evaluate whether these changes can be attributed to the intervention. A retrospective cohort study was conducted on 44 adult patients (14 males and 30 females) aged 18 to 43 years. Articular assessments were performed to evaluate temporomandibular joint (TMJ) health and functionality before (T0) and after (T1) combined orthodontic-surgical treatment. TMJ health was assessed by maximum mouth opening, joint noises, parantfunctions (bruxism or clenching), joint locking, TMJ pain, masticatory muscle pain, and headaches. Statistical analyses used [PERSON]'s Exact Test and paired T-tests. The study shows a significant reduction (_p_ < 0.05) in symptoms, except for locking, with the most substantial decrease in headaches (_p_ = 0.0001). Overall, articular symptoms markedly decreased post-surgery, with sustained joint functionality. Restoring physiological occlusion in patients with anterior open bite is crucial for maintaining the stomatognathic system's balance. Orthognathic surgery, when indicated, appears beneficial in alleviating articular symptoms while preserving TMJ function.  
Keywords:TMJ; TMJD; open bite; orthongathic surgery; retrospective study +
Footnote †: journal: Medical Sciences
## 5 Conclusions  
The purpose of this investigation was to develop a feature-based data fusion technique to improve the probability of defect detection in the adhesive lap joints. Data were experimentally obtained using two common NDE techniques: ultrasonic pulse-echo and radiography. For this investigation, single lap adhesive-bonded aluminum joint with different sizes of brass inclusions were used. For the selection of the zone of interest in ultrasonic A-scans, theoretical analysis and modelling of impulse responses from interfaces in the sample were performed and multiple reflections inside the sample were analyzed, as well as different propagation paths. The modelling results were confirmed by experimental investigations of the propagation of ultrasonic waves. For the analysis, several distinctive features were extracted from the experimental ultrasonic and radiographic data and used as inputs for the data fusion. The comparison of extracted features shows that, at least in the case of the inclusion type of defect, the most promising features giving the highest contrast in terms of the defect/non-defect region from ultrasonic images are signal attenuation, peak-to-peak amplitude, and absolute energy; however, other features, especially frequency domain related, should not be disregarded. Feature-based image fusion of both  
Figure 15: Evaluation of results using receiver operating characteristics (ROC) curve.  
techniques was also applied and evaluated using different data fusion algorithms. This helps to overcome the limitation of multimodal imaging and improves the prediction possibility and reconstruction of the missing information from the images of the single techniques. The fused image i provides intricate details of the defects in the sample on a finer scale. Although radiographic image contributes to the detection of the inclusion type of defect the most, the ultrasonic image provides extra information about additional types of defects, such as disbonds around the edges of the inclusions, which are not visible in the radiographic images. Of the fusion algorithms used, the best performance was achieved using average and saliency algorithms--AUC values of more than 0.99 are achieved.  
Conceptualization, E.J.; methodology, E.J.; software, B.Y., L.M., V.C. and G.A.B.; validation, E.J.; formal analysis, B.Y, D.S. and G.A.B.; investigation, E.J., D.S., E.Z. and G.A.B.; resources, L.M. and E.J.; data curation, B.Y.; writing--original draft preparation, G.A.B. and D.S.; writing--review and editing, E.J., B.Y., V.C. and E.Z.; visualization, B.Y., D.S., V.C., E.Z. and G.A.B.; supervision, E.J.; project administration, E.J.; funding acquisition, E.J., L.M., V.C., E.Z., D.S. and B.Y. All authors have read and agreed to the published version of the manuscript.  
This research has received funding from the Research Council of Lithuania (LMTLT), agreement No S-MIP-22-5 and agreement No S-PD-22-25.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Parabolic Equation Modeling
The Table 4 presents a brief comparison of the two numerical techniques (SSFM and CN-FDM), highlighting the advantages and disadvantages of each method. Figure 12: Variables, notations, and boundary conditions in PEM 2D analysis. Parabolic equation modeling, as a numerical technique, requires the application of appropriate boundary conditions to ensure exact solvability and adherence to physical considerations. A Dirichlet boundary condition can be imposed at each point of the curve \(h=z(x)\) representing the ground, where the wave function is directly prescribed, for instance, by assuming a perfect conductor, leading to \(\psi(h)=0\). If the ground surface is not treated as a perfect conductor but rather defined with a specific impedance, a Robin boundary condition must be applied in the form \(\partial\psi/\partial z=\gamma\psi\)[51]. Given that the problem domain under investigation is finite, it requires proper termination, necessitating the definition of additional boundary conditions [52]. One possible approach is to introduce termination segments parallel to the \(x\) and \(z\) axes. On these boundaries, the Engquist-Majda boundary condition [53] must be imposed in the forms \(\partial\psi/\partial z+ik_{0}\psi=0\) and \(\partial\psi/\partial x+ik_{0}\psi=0\), respectively. These conditions ensure the reflectionless propagation of outgoing waves.
## 2 Fundamental Concepts Related to FL  
### Fundamentals of FL  
A conventional FL ecosystem encompasses \(N\) participants/clients, one central server, and a training protocol/algorithm. In each round, the global model, denoted with \(\Delta W\), is assessed with respect to performance, refurbished, and distributed to all parties (e.g., servers and clients) encompassed in the ecosystem. The training procedure of FL keeps repeating until the convergence criterion is met. There are \(N\) rounds in the FL, and the analysis of one round of FL is explained in our recent work [12]. In the beginning, participants acquire a \(\Delta W\) (global model update) from the centralized server. Subsequently, each client independently performs training on the local data and computes a local weight. Afterward, the local updates from each participant are forwarded to the central server for aggregation purposes. The function/aggregator, \(g\), used at the central server for averaging at time/epoch \(t\) is in Equation (1):  
\[g(t)=\frac{1}{N}\sum_{i=1}^{N}\Delta W_{i}^{t} \tag{1}\]  
where \(N\) shows the # of clients, \(g(t)\) shows the global weight at \(t\) epoch/time, and \(\Delta W_{i}^{t}\) is the updated gradient at epoch \(t\) for the \(i\)th client.
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 4 Experiment and Analysis
### Code Base  
In the method-based code search, we obtain the _Javadoc_ comments corresponding to each method by parsing the abstract syntax tree of the Java source code. Feature information is extracted as a natural language description of the method to obtain the large dataset. The steps needed to construct the training set are as follows:  
* We use the SQL statement in Figure 7 for data fetching. This SQL statement is executed through Google BigQuery API. We obtained 63,015 Java projects with at least 2 stars in the GitHub database from 2016 to 2019, including 7,190,099 Java code files according to the conditions. The methods that do not contain _Javadoc_ comments are filtered out in this step.
* For each annotated Java method, we extract semantic information, and use \(<\)_method name_, _API invocation_, _token collection\(>\)_ to represent the code snippets.
* For the _Javadoc_ comment in each method, functional information is extracted as a natural language description. The specific methods used in this step are as follows: (a) We delete the statements beginning with _@param_ and _@return_ in the comments, which introduce information about parameters and return values. These statements are not closely related to code functions; (b) We delete the statements in the comments that contain the keywords _createdby_ and _author_, which introduce the relevant information introduced by the creator and the author. These statements have nothing to do with the function of the code; (c) We delete the comments led by _TODO_ and _FIXME_, which have nothing to do with the function of the code; (d) We delete the content containing URL and date in the comment, which has nothing to do with the function of the code; (e) We delete the statement containing \(Copyright/LICENSE\) in the comment, which describes the copyright and other information and has nothing to do with the code function;For the content-filtered annotations, the first sentence is selected as the natural language description of the method. Because the first sentence of the functional annotation is an overall description of the code function. Based on the above conditions for the source code, we conducted a dataset containing 1,263,974 sets of code snippets and corresponding functional descriptions. To ensure the consistency of the data, the experiments in this study are all carried out on this dataset.
# Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages
## 2 Literature Review
### VR in Systems Engineering  
According to [PERSON] [27], VR is a three-dimensional simulation that enables users to interact with digital objects as they would in the real world. These environments can replicate actual settings or present entirely imagined worlds developed using specialized software. In recent years, VR has seen rapid advancements, with major technology providers and industry leaders, such as Facebook, Google, Microsoft, and Samsung, investing significantly in immersive imaging technologies like headsets, supporting software, and AI.  
This technological progress has positioned VR as a valuable tool in systems engineering, particularly in the development of complex products like vehicles, aircraft, and ships, which often involve geographically dispersed teams. VR addresses the logistical and coordination challenges of such projects by streamlining processes like design, prototyping, and validation. It enables immersive, real-time collaboration across locations, enhancing communication and reducing travel, redesigns, and physical prototyping costs. As [PERSON] [27] emphasizes, VR supports more efficient decision-making and facilitates integration throughout the entire system lifecycle--from initial concept to final production.  
Nevertheless, integrating VR into systems engineering requires careful attention to platform usability, project alignment, and long-term cost-effectiveness. As highlighted by [PERSON], [PERSON], and [PERSON] [28], deployment across domains such as software, human factors, and maintenance must be strategically planned to ensure interoperability and scalability.
## 6 Practical Application and Results
### Technology Analysis  
The state-of-the-art review provided various design considerations, including AR/MR design recommendations for supporting SA [13], guidelines for AR/MR user interface design [45; 46; 47], and basics of user experience design [48], to mention a few. To examine the state of practice, AR applications for smartphones and MR software for headsets were tested, focusing on exploring the interface design, capabilities, and user experience of current AR/MR solutions for construction. Regarding devices, smartphones were selected for the AR prototype, and the Trimble XR10 headset was used for the MR prototype due to its compliance with construction safety standards. Interaction modes specific to each device were considered for the interaction conceptualization.
## 2 System Model  
In this article, a static underwater optical wireless sensor network is adopted, and Figure 1 illustrates the network model employed for the proposed LE-KCR algorithm with the sensors randomly located underwater following a Poisson distribution. In order to alleviate the effect of the directional transmission property on UWOC, each node is presumed to be equipped with a prismatic array of three high-power blue LED modules [11], with each one having a field of view of 120 degrees, to realize omni-directional transmission. We assume that an appropriate amount of autonomous underwater vehicles (AUVs) [36] with cruise function is deployed as sink nodes to collect data. In [37], the possible methods of deploying such networks were proposed. The sensors are divided into multiple clustersfor data reporting through the cluster-heads selected from them. After a cluster-head collects data, it will transmit the data to the base station on land through the communication link established by the nearest AUV and the sea buoys. Noticeably, AUVs can also assist the sea buoys to update the position of nodes in the network regularly to reduce the position error of nodes [17]. In the subsequent part, both the underwater channel and the consumed energy are modeled.
# Adaptive Aggregated Attention Network for Pulmonary
## 4 Results and Discussions
### Visualization of Classification Results  
To verify the performance of our proposed architecture better, we visualize the diagnosis results of some benign and malignant nodules. In Figure 8, the benign nodules are presented in the first row and the malignant nodules are presented in the second row. We make a comparison between the predicted probability of our proposed architecture, and the average diagnosis level of four radiologists and the comparison results are shown for each nodule. If the predicted probability is less than 0.5, the nodule is predicted to be a benign nodule; otherwise, it is predicted as a malignant nodule. Similarly, if the average diagnosis level of four radiologists is less than 3, the nodule is labeled as benign nodule and, if the average diagnosis level is more than 3, the nodule is labeled as malignant nodule. The visualization results show that our predicted probability of each nodule is consistent with the average diagnosis level of doctors, which proves that our proposed model is robust and that the classification results are very reliable.  
Moreover, we add some visualization of misclassification examples which are shown in Figure 9. Based on Figure 9, our method is still insufficient for the recognition of some uncertain nodules. For example, for the first nodule on the left in Figure 9, its malignant level is diagnosed by two doctors as 4, which represents a malignant nodule, and its malignant level is diagnosed by one doctor as 3, which represents an uncertain nodule. Moreover, its malignant level is diagnosed by one doctor as 2, which represents a benign nodule. Finally, the average malignant level is 3.25, which represents the nodule as being slightly malignant and similar to an uncertain nodule for which the malignant level is 3. For such nodules, professional doctors also have differences in the diagnosis of such nodules and cannot clearly diagnose them. Such nodules often need to be further confirmed in the patient's follow-up examination. Therefore, the diagnosis of such nodules is very difficult. In the future, we hope to obtain lung cancer datasets with patient follow-up examinations, such as the National Lung Cancer Screening Trial (NLST) dataset, and we will make a further study.  
Figure 8: Visualization of benign and malignant diagnosis results: the number for each nodule shows the predicted malignant probability and the average malignancy level of four doctors.
# Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability
## 3 Optimization Model
### Fractional Chaotic Particle Swarm Optimization Algorithm  
After generating the initial population in the algorithm, the position and speed of the particles should be updated. In this article, the method of fractional order derivatives is used to model the movement of particles [16]. In general, the equations of particle swarm optimization algorithms in discrete form are as follows:  
\[\begin{split}|v[t+1]&=wv[t]+\sum_{i=1}^{2}\rho_{i} r_{i}(\chi_{i}[t]-x[t])\\ |x[t+1]&=v[t]\end{split} \tag{3}\]  
and the continuous form of the above equations is as follows:  
\[\begin{split}|\frac{d}{dt}v[t]&=wv[t]+\sum_{i=1}^{ 2}\rho r_{i}(\chi_{i}[t]-x[t])\\ |\frac{d}{dt}x[t]&=v[t]\end{split} \tag{4}\]  
Because the particles move in a zigzag pattern, due to the complexity of the particle movement and the dependence of their movement on the previous position, fractional order derivatives are used which have a better description of the long-term past of the particle movement [9; 10]. The above equations were written using [PERSON]'s fractional derivative model as follows:  
\[\begin{split}|^{c}D^{a}v[t]&=wv[t]+\sum_{i=1}^{2} \rho_{i}r_{i}(\chi_{i}[t]-x[t])\\ |^{c}D^{a}x[t]&=v[t]\end{split} \tag{5}\]  
By discretizing [PERSON]'s fractional Mortier derivative for \(v\) and \(x\), the velocity and location of the particles based on the fractional Mortier derivative (\(\alpha\)) are as follows:  
\[\begin{split} v(t+1)&=(\alpha+h^{a}w)v(t)+h^{a} \rho_{1}r_{1}(\chi_{1}(t)-x(t))\\ &\quad\quad+h^{a}\rho_{2}r_{2}(\chi_{2}(t)-x(t))\\ x(t+1)&=\alpha x(t)+\frac{1}{H^{a}}v(t+1)\end{split} \tag{6}\]  
The stability of the particle swarm optimization algorithm has been proven by applying fractional order derivatives [9].  
The important point in determining the optimal value of the order of the fractional derivative is that this value depends on the optimization problem and has a specific value for each problem.
# High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method
## 5 Results and Discussion  
### Results of Classifiers  
#### 5.1.1 Hit Ratio  
To explore the forecasting performance of our proposed approach on the rebar futures price, several methods described in Section 4.4, including XGBoost-SMOTE, XGBoost, SVM, ANN, and RF, are used as the benchmark methods in the experiments. Firstly, we focus on the hit ratio results to compare the forecasting accuracy of the proposed method and the benchmark methods. The hit ratio results are provided in Table 5.  
Among all the methods, XGBoost-SMOTE-NSGA-II achieved the best average hit ratio result in predicting the direction movements of the rebar futures price. As shown in Table 5, the average hit ratio of XGBoost-SMOTE-NSGA-II was about 53.98%, while XGBoost, RF, SVM, and ANN yielded the values of 48.98%, 48.95%, 47.17%, and 48.05%, respectively. Specifically, it was found that the average hit ratio result of XGBoost was a slightly larger than that of RF, SVM and ANN, suggesting that the direction classification performance of XGBoost is superior to other machine learning technologies. Compared to that improvement, the proposed method XGBoost-SMOTE-NSGA-II obviously achieved  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Testing Period** & **ANN** & **SVM** & **RF** & **XGBoost** & **XGBoost-SMOTE** & **XGBoost-SMOTE-NSGA-II** \\ \hline Period 1 & 44.03\% & 44.86\% & 51.44\% & 51.03\% & 61.04\% & 61.04\% \\ Period 2 & 46.53\% & 47.57\% & 50.69\% & 52.08\% & 51.03\% & 51.03\% \\ Period 3 & 51.34\% & 48.92\% & 42.20\% & 45.16\% & 50.00\% & 50.00\% \\ Period 4 & 50.30\% & 47.34\% & 51.48\% & 47.63\% & 53.85\% & 53.85\% \\ Average & 48.05\% & 47.17\% & 48.95\% & 48.98\% & 53.98\% & 53.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hit ratio results for rebar futures price direction forecasting.  
a superior result in terms of the average hit ratio, as well as generating the best hit ratio results in testing periods 1 and 4. In particular, despite the better classification performance achieved by XGBoost in testing period 2, the hit ratio results in the other three testing periods were worse than our proposed approach. Similarly, ANN yielded a larger hit ratio in testing period 3, while our proposed method still outperformed ANN in the other three testing periods. It demonstrates that the integration of the SMOTE technology successfully promoted the classifier's prediction accuracy. In conclusion, our proposed approach has a relatively superior forecasting ability to forecast the movement direction of the rebar futures price.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 2 Materials and Methods
### TF-FP: IF1213 PPP Model with IGMAS Product Correction  
The TF-FP model, which is an IF1213 PPP model, can be applied to IGMAS PIFCB product correction. In this case, the time-varying part of the phase hardware bias of the receiver is ignored, and the satellite part is not. The precise satellite clock is the same as in Equation (7). Combining Equation (7) with Equation (6) corrects the satellite DCB product and the precise satellite clock. The TF-FP is expressed as follows:  
\[\left\{\begin{array}{l}p_{r,IF12}^{s,Q}=\mu_{r}^{s,Q}\cdot x+d\overline{t}_ {r}+m^{s,Q}\cdot Z_{r}+\varepsilon_{r,IF12}^{s,Q}\\ r_{r,IF12}^{s,Q}=\mu_{r}^{s,Q}\cdot x+d\overline{t}_{r}+m^{s,Q}\cdot Z_{r}+ \overline{\overline{N}}_{r,IF12}^{s,Q}+\tilde{\tau}_{r,IF12}^{s,Q}\\ \tilde{\tau}_{r,IF13}^{s,Q}=\mu_{r}^{s,Q}\cdot x+d\overline{t}_{r}+m^{s,Q}\cdot Z _{r}+IFB_{r}+\varepsilon_{r,IF13}^{s,Q}\\ r_{r,IF13}^{s,Q}=\mu_{r}^{s,Q}\cdot x+d\overline{t}_{r}+m^{s,Q}\cdot Z_{r}- pifc_{r,IF13}^{s,Q}+\overline{\overline{N}}_{r,IF13}^{s,Q}+\tilde{\tau}_{r,IF13}^{s,Q} \end{array}\right. \tag{17}\]  
where \(pifc_{r,IF13}^{s,Q}=\delta\rho_{r,IF12}-\delta\varphi_{IF12}^{s,Q}-\delta\varphi _{r,IF13}^{s,Q}+\delta\varphi_{IT13}^{s,Q}\) is the content of the IGMAS PIFCB product. Other parameters are the same as described above. After correction by IGMAS PIFCB products, Equation (17) is the same as Equation (12) in GPS.