esa-sceva/satcom-chunk-collection huggingface dataset:
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 3 Materials and Methods
### The Implementation of the Proposed Solution
#### 3.2.4 Creating APIs in AWS API Gateway  
We developed multiple APIs within the system for several key purposes. First, they streamline communication, ensuring seamless interaction between the core components of the solution. Second, they optimize load distribution by managing tasks that would otherwise burden the main server, effectively balancing workload and improving performance. Lastly, they enhance security by decentralizing operations, reducing the risk of system-wide compromise in critical situations.  
Using AWS API Gateway, we configured three APIs: two REST APIs and one WebSocket API, which have the previously presented Lambda functions integrated. Thus, API Gateway sends the received request from a specific path (endpoint) to the integrated function for handling. Each API has the following well-defined roles within the system: one REST API is used for managing employee tasks, work shift times, and Firebase token registration; another is used for retrieving employee timesheets; and the WebSocket API is used for synchronizing task updates between web and mobile applications in real time. Additionally, a structured approach aids in its usage and enhances the understanding of the configuration itself.  
The type of operations and the frequency of data updates needed led to the decision of using one WebSocket API and two REST APIs. Standard request-response processes, in which communication takes place in discrete, rare transactions, are appropriate for REST APIs. On the other hand, the WebSocket API was selected to manage the continuous, real-time communication needed for the employee tasks monitoring function, which calls for regular updates to be sent to connected devices without requiring several separate requests. WebSocket is suitable for keeping track of current task status across several platforms because it allows for permanent, bidirectional connections.
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 4 Results
### Presenting Detailed Use Cases
#### 4.2.4 Connecting to the Smartwatch Application  
In order to connect to the smartwatch, employees use the web application by following a series of steps. After logging into the web app, they are directed either straight to the smartwatch connection page (if they are not a supervisor) or to a supervisor section with access to this page. Once on the connection page, a WebSocket connection is established, and a listener is set up to monitor WebSocket messages. When a message from the server is received, it checks for a connectionInitiated action and verifies that the employee ID in the message matches the authenticated employee's ID. The page then prompts the employee to enter their employee ID on the smartwatch app and press a button, sending two WebSocket messages: one with the connectionInitiated action and another with addEmployeeId. After processing these messages, all WebSocket connections receive a message containing the employee ID and connectionInitiated action, allowing the employee's web page to activate a \"Click here\" button. When the employee clicks \"Click here\", a message with the establishingConnection action, along with their employee ID and name, is sent through the WebSocket. Upon receiving this, the WebSocket server confirms the connection by sending a message solely to the connection associated with that employee ID. Following a successful connection, the employee is redirected, with options to log out or, if they are a supervisor, return to the start page.  
Figure 9 illustrates the sequence of information exchanged between components, beginning when the employee enters their employee ID on the smartwatch and continuing through to successfully accessing their account.  
Figure 8: Sequence diagram of adding information about an employee.  
Figure 9: Sequence diagram of connecting to the smartwatch.
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 3 MPC in Biomedical Applications
### Artificial Pancreas  
MPC is also used to operate artificial pancreas (AP) devices, which automatically deliver the appropriate amount of insulin dosage to type 1 diabetes patients in order to keep their blood glucose levels within acceptable limits [77; 78]. Inside an AP, a control algorithm determines the appropriate dosage under a variety of different daily living conditions and for a variety of patient populations. Although linear MPC models yield computationally efficient control algorithms, such generalized models prove insufficient for effective control because physiological variables vary greatly between individuals and also dramatically within individuals over time. Customized and recursive identification algorithms can be used to overcome this limitation and better characterise the time-varying nonlinear dynamics of AP-related biological processes [79].  
A study [79] presented a new adaptive MPC approach for modeling complex biological systems characterized by transient dynamics. This approach firstly involved the recursive system identification of metabolic process parameters associated with diabetes. Thereafter, the identified system model was employed to develop an adaptive MPC algorithm to control insulin delivery in the blood. Glucose levels were monitored using a feature extraction protocol in order to quickly react to deviations from the target setpoint caused by metabolic events. Subsequently, the constraints of the optimization problem were updated to negotiate between the controller's aggressiveness and robustness in order to recommend the appropriate amount of insulin to be delivered.
## 3 Results  
According to the bibliographic search using PubMed, EBSCO, and ScienceDirect databases, 1679, 1146, and 3735 articles, respectively were found, totaling 6560 research papers. Subsequently,  
Figure 1: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram, adopted from [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], & [PERSON] [PERSON]. [8]. CTS: carpal tunnel syndrome. duplicates were identified among the information sources, and twelve cases were detected in EBSCO; therefore, 6548 papers remained for review. According to the first criterion (titles and abstracts), we eliminated a total of 6446 articles: 1593 from PubMed, 1124 from EBSCO, and 3729 from ScienceDirect. We proceeded to thoroughly review the remaining 102 papers following the previously described criteria 2 and 3. According to the second criterion, we rejected six reviews because they related to CTS factors other than the ones examined in this work, such as psychosocial factors, clinical characteristics of patients, and hand-arm vibration syndrome, among others. Using the third criterion, we found that two articles were in a language other than English, so we reviewed them; however, one paper did not have an abstract in English and the other did not consider risk factors for CTS, therefore we discarded them. Out of the 80 articles we selected for their complete review, eight did not investigate the relationship between CTS and the established factors; therefore, 72 articles were used in this work, which aimed to find out whether there is a relationship between the selected factors and CTS in terms of prevalence, diagnosis, severity, and surgical results, among others. Finally, 72 articles were grouped according to the factors stipulated for this work, i.e., age, BMI, cardiovascular risks, dominant hand, sex, and multifactors except for the abdominal circumference and respiratory rate since no research was found regarding CTS for these factors. The classification of multifactors refers to the study of two or more of the factors involved; therefore, in several of the works reviewed, anthropometric, occupational, and ergonomic factors were also considered. The investigations were classified into six categories: (1) association of age with CTS, (2) association of BMI with CTS, (3) association of cardiovascular risk factors and CTS, (4) association of hand dominance with CTS, (5) association of sex with CTS, and (6) association of multifactors with CTS. These categories are described in the next paragraphs. At the end of this section, Table 1 shows a summary of the results obtained. Figure 2 illustrates the leading journals that published topics associated with CTS for those that published more than one paper. The journals _Hand_ and _Muscle & Nerve_ have published at least four of these articles. Many journals specialize in topics associated with surgeries or problems in the hands. For a list of the journals with one published article included in this study, see Appendix A.  
Figure 2: Journals published on CTS (only those with at least two articles included in the study are shown here).
## 4 Discussion
Two previous studies comparing titanium surfaces treated with UVA and UVC showed that despite emerging superhydrophilia on both titanium surfaces, the cellular fixation capacity was significantly greater for the UVC-treated surfaces. Reported findings indicate that photofunctionalization with ultraviolet C light accelerates the process of osseointegration, both in animals and in humans. This effect may be of help in immediate loading procedures, as well as in short implants and in cases of poor bone quality, low primary stability, or systemic diseases [7; 8; 9; 10]. The intention of our work was to determine the effect of both irradiation power and time on the wettability angle of standard surfaces, which does not depend solely on the composition of the material. Surface topography also plays an important role, depending on whether it is a pure titanium surface, acid-etched, or sand-blasted. Rough surfaces lose their hydrophilicity more quickly than smooth surfaces. In addition, an interesting observation is that the hydrophilicity and hydrophobicity of rough surfaces are greater than those of smooth surfaces, while their angles vary widely as a result of biological ageing. In our study, all surfaces were of the same nature: Grade IV titanium with RBM-TC surface (Ticare, Mozo Grau, Valladolid, Spain), so that the only variables capable of affecting the wettability angle were power and time of exposure. This study showed that UVC photoactivation modifies titanium surfaces from a hydrophobic (control) to a hydrophilic state at both 6 and 85 W, each showing statistically significant differences from the control group. However, according to the wettability classification based on the contact angle, superhydrophilic surfaces were not achieved, as no angle was less than 10\({}^{\circ}\). These differences were accentuated by an increase in time. However, the results revealed that the most significant effect depended on the power of the emitter, although the effect was not evident before 30 min. After 60 min, the effect of 85 W was significantly more pronounced (73\({}^{\circ}\) versus 19\({}^{\circ}\)) and was equally or more effective at 120 min (61\({}^{\circ}\) versus 31\({}^{\circ}\)). However, in the case of the 85 W group, this improvement did not change to any great extent, changing from a contact angle of 19\({}^{\circ}\) at 60 min to 31\({}^{\circ}\) at 120\({}^{\circ}\), a paradoxical effect not seen at 6 W. We share the opinion that this is due to the saturation of compounds in the atmosphere, which, once detached from the titanium surface, are re-deposited on the titanium surface with its increased absorption capacity. This study points to an increase in the hydrophilicity of titanium surfaces treated with UV light, and it would be of particular interest to continue with an in vivo study. Such a study should focus on applying a power of 85 W and a time of 60 min, a combination that yielded the best result in vitro.
# Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency
## 3 AI-Based Approaches in Building Energy Management
### Commonly Used Machine Learning Methods
#### 3.2.6 Regression  
Multiple linear regression (MLR) is a machine learning model based on statistical linear regression, and it is widely used in several studies to predict the monthly heating and cooling demands of residential buildings. A support vector machine is typically used in contexts where the objective is to classify data. The main goal of these methods is to find a function from the obtained target for all the training data, including information about the weather condition, the relative humidity, and the intensity of the solar radiation [75, 76]. The prediction accuracy and robustness of both the MLR and the SVR methods require improvement for use with the highly non-linear energy demand function.  
[PERSON] et al. compared predictions of the hourly cooling load in a building made using SVR with those made using several ANN models [77]. In another study on non-residential buildings, [PERSON] et al. used SVR as well as MLR and ANNs to predict the short-term thermal load for non-residential buildings [78]. These studies showed that the improvement in the energy use prediction when using SVR was better than using other AI-basedprediction methods. The trade-off between prediction accuracy and computation speed when using the SVR has also been shown to be better than that provided by MLR and ANNs.  
Logistic regression is based on the statistical logistic function (also referred to as the sigmoid function), and is basically used as a model for classification. It is widely used in events or problems that require predictions of the probability of occurrence. [PERSON] et al. used logistic regression in analyzing the effect of certain significant factors on the amount of energy consumed when heating rural buildings in China [79].
# Linear Time Non-Local Cost Aggregation on Complementary Spatial Tree Structures
## 5 Conclusions and Future Work  
In this paper, we propose an efficient non-local cost aggregation method on two complementary spatial tree structures. Recursive algorithms with linear time complexity are presented to compute the output on each tree. Our spatial tree structures provide more suitable ways to calculate the geodesic distances in spatial and intensity spaces. The integration of these two trees balances the information propagated in all directions. Experimental results in stereo matching and optical flow estimation demonstrate that our method is superior to MST-based non-local filtering methods. We also compare handcrafted features and deep features extracted by CNNs in stereo matching. The results show that handcrafted features can successfully preserve the discontinuities in disparity images, while deep features extracted by CNNs are superior at dealing with ambiguity in homogeneous regions. After refinement, the percentage of erroneous pixels and average end-point error in non-occluded areas using features extracted by deep CNNs to compute matching costs are improved by 37.2% and 8.2%, respectively. It is straightforward to fuse the matching costs of these two kinds of features based on the texture pattern in the local area. Moreover, our method can also be applied to edge-preserving graphics applications. We will work on this in the future.  
Conceptualization, H.W. and H.Z.; methodology, P.B.; software, P.B.; validation, P.B. and H.Z.; investigation, Y.D.; resources, Y.D.; writing--original draft preparation, P.B.; writing--review and editing, P.B.; visualization, P.B.; supervision, Y.D.; project administration, H.Z.; funding acquisition, P.B. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research and Development Program of Shaanxi Province (Grant No. 2023-YBGY-103) and the National Natural Science Foundation of China (Grant No. 61975161).  
Not applicable.  
The data presented in this study are openly available in [[https://www.cvlibs.net/datasets/kitti/eval_stereo.php](https://www.cvlibs.net/datasets/kitti/eval_stereo.php)]([https://www.cvlibs.net/datasets/kitti/eval_stereo.php](https://www.cvlibs.net/datasets/kitti/eval_stereo.php)) and [[https://vision.middlebury.edu/stereo/data/](https://vision.middlebury.edu/stereo/data/)]([https://vision.middlebury.edu/stereo/data/](https://vision.middlebury.edu/stereo/data/)), accessed on 15 March 2017.  
The authors would like to thank [PERSON] for his help in applying the Research and Development Program of Shaanxi Province (Grant no. 2023-YBGY-103) and preparing this manuscript. We appreciate the help from anonymous editors and reviewers for their valuable suggestions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BF & Bilateral filter \\ GF & Guided filter \\ NL & Non-local filter \\ ST & Segment-tree filter \\ DT & Domain Transform \\ FCGF & Fully connected guided filter \\ LRNL & Linear recursive non-local filter \\ \end{tabular}
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 3 Test Results
### Attenuation Law of Lasers with Different Wavelengths in the Same Suspended Media
#### 3.2.3 Clay Suspended Medium  
When the suspended medium is clay, the curve of attenuation coefficient with turbidity is shown in Figure 5a. We compared the lasers with wavelengths of 405 nm, 450 nm, 532 nm, 650 nm, and 808 nm. When the laser wavelength is 650 nm, the increasing rate of laser attenuation coefficient is the lowest with the increase of turbidity. When the turbidity is lower than approximately 25 NTU, the 450 nm laser attenuation coefficients are all less than 650 nm. With the increase of turbidity, the 650 nm laser is more advantageous. The cloud diagram of the laser attenuation coefficients with different wavelengths and different turbidity grades is shown in Figure 5b.  
When the laser wavelengths are 450 nm and 650 nm, there are two \"ravines\" with low attenuation coefficients. We adopted the above-mentioned fitting formula and solved the following two simultaneous equations:  
\[Y=0.3412X-1.6622 \tag{11}\]  
\[Y=0.2203X+1.602 \tag{12}\]  
When the turbidity is 27 NTU, the attenuation coefficients of the two are equal. Thus, when the turbidity is lower than 27 NTU, the 450 nm laser is suggested, and when the turbidity is higher than 27 NTU, the 650 nm laser is suggested. This follows the same law as for silt.
## 2 Materials and Methods
### Methods
#### 2.2.4 Cross-Validation and Performance Metrics  
In order to estimate the goodness and the robustness of our classification model a cross validation framework was applied. In general, for a \(k\)-fold Cross Validation (CV) procedure, the original dataset is randomly partitioned into \(k\) equal sized subsamples. Of the \(k\) subsamples, a single subsample is retained for validation and the remaining \(k-1\) subsamples are used to train the model. To select the most performing algorithms between GLM, RF and ANNs we applied a common five-fold. Then we used the feature set individuated by [PERSON] to feed this learning model and to distinguish the three-levels CVD risk. To make our model more robust we studied the trend of the classification accuracy while varying the k parameter: \(k=10,5,3\) and Leave One Out (LOO) Cross Validation. However, for the present case such procedure could greatly affect the model accuracy. Accordingly, we also assessed a LOO cross validation in order to maximize the number of observations used for training. It is well known that the results obtained with such procedure should be considered cum grano salis as they could lead to an accuracy overestimation. A figurative example of our procedure is shown in Figure 2.  
The model performance were firstly evaluated in terms of Area Under Roc Curve (AUC) and accuracy, namely the rate of correct classifications. In a classification problem with \(M\) classes, the accuracy for the class \(i\), with \(i=1,\dots,M\) is defined as:  
\[Acc_{i}=\frac{TP_{i}+TN_{i}}{TP_{i}+TN_{i}+FP_{i}+FN_{i}} \tag{3}\]  
where \(TP_{i}\), \(TN_{i}\), \(FP_{i}\), \(FN_{i}\) represent true positives, true negatives, false positives and false negatives, respectively, for \(i-th\) class. The overall accuracy is obtained from the average on all classes. In addition, we also evaluated the model sensitivity and precision and the F1 score for each class \(i\):\[Sens_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}}; \tag{4}\]  
\[Prec_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}}; \tag{5}\]  
\[F1_{i}=2\cdot\frac{Prec_{i}\cdot Sens_{i}}{Prec_{i}+Sens_{i}}. \tag{6}\]  
All the data processing and the carried out statistical analyses were performed in R version 4.1.0 ([[https://www.r-project.org/](https://www.r-project.org/)]([https://www.r-project.org/](https://www.r-project.org/)), accessed on 31 August 2021).
## 4 Discussion  
### Comparison between CwTV and CwVN Groups  
We initially hypothesized that CwVN would have poorer auditory discrimination abilities than CwTV; however, this was not supported by our data. Examination of age-matched CwVN and CwTV indicated that these two groups did not differ based on pitch discrimination abilities. Additionally, estimates of attention were examined in children with and without vocal fold nodules, and there were no significant differences for either estimate of attention between CwTV and CwVN. Thus, although previous work has suggested that CwVN may have attentional differences [31; 45; 46], these differences appear to manifest at a larger personality-based level that does not translate to the perceptual discrimination of voice. The lack of difference in pitch discrimination abilities is different than what has been noted in adults with dysphonia [33], suggesting potential differences between CwVN and adults with vocal hyperfunction. [PERSON] and colleagues [33] hypothesized that adults with hyperfunction had differences in their discrimination abilities due to an adaptation effect. This suggests that an individual who is consistently listening to their own dysphonic voice may rely less on auditory feedback, as the consistent vocal source they hear is variable. Thereby, this adaptation to a variable mechanism could result in impaired discrimination abilities. The current study hypothesized that CwVN would have poorer discrimination abilities; however, the voices of the CwVN in the current study were not recorded. Therefore, direct relationships cannot be drawn between their dysphonia severity and pitch discrimination abilities. However, it is likely that many CwVN have been dysphonic for less time than an adult with a voice disorder and, thus, may have less of an adaptation effect. Future work examining the relationship between dysphonia severity in CwVN and pitch discrimination abilities is needed to further elucidate this potential connection.