esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
For this review, an electronic search was performed via PubMed, Scopus, Web of Science, and Google Scholar using the terms \"Augmented Reality\", \"Virtual Reality\", \"AR and VR\", \"AR and Dentistry\", \"VR and Dentistry\", \"Use of AR and VR in Dentistry\", \"AR/VR and dental education\", for published research articles from the year 2011 to 2021. A secondary search was further performed after analyzing the list of articles that met the pre-determined inclusion criteria of the study. Three independent researchers S.F., A.M. and N.A., read the articles that were retrieved by the search engines, and studies that did not meet the inclusion criteria were excluded. Due to the heterogeneity of the design of the included studies, a systematic review and meta-analyses could not be performed. Therefore, a narrative review with the \"best-evidence synthesis\" [8] approach was carried out.  
The pre-determined inclusion criteria for this study were as follows:  
* Clinical trials
* Case-control studies
* Observational studies
* Studies where AR and VR have focused on dentistry
* Studies in the English Language  
The studies that were excluded from this study were:  
* Review articles
* Short communications
* Letters to Editors
* Studies in languages other than English  
The data that were collected from the studies were extracted by the investigators and then noted under the following headings: \"authors\", \"year of publication\", \"studytype\", \"sample size\", \"outcomes\", and \"field of dentistry\". Any disagreement amongst the investigators was solved by discussion with a third reviewer, A.L.
applied sciences Article  
Capillary-Driven Microdevice Mixer Using Additive Manufacturing (SLA Technology)  
[PERSON] 12,*(c) and [PERSON] 1  
Laboratory of Microsystems & Nanotechnology, Mechanical Engineering Department, Polytechnic University of Catalonia (UPC), Colom Street 11, 08222 Barcelona, Spain Mechanical Engineering Department, Universidad Politecnica Salesiana, Quito 170517, Ecuador Correspondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]  
This study presents a novel microfluidic mixer designed, fabricated, and characterized using additive manufacturing technology -- stereolithography (SLA) -- and harnessing capillarity principles achieved through microstructure patterning. Micromisers are integral components in optimizing mixing and reaction processes within microfluidic systems. The proposed microdevice employs a tank mixing method capable of blending two fluids. With a channel length of up to 6 mm, the process time is remarkably swift at 3 s, and the compact device measures 35 \(\times\) 40 \(\times\) 5 mm. The capillarity-driven working flow rates range from 1 \(\upmu\)L/s to 37 \(\upmu\)L/s, facilitated by channel dimensions varying between 400 \(\upmu\)m and 850 \(\upmu\)m. The total liquid volume within the device channels is 1652 mL (6176 \(\upmu\)L including the supply tanks). The mix index, representing the homogeneity of the two fluids, is approximately 0.55 along the main channel. The manufacturing process, encompassing printing, isopropyl cleaning, and UV (ultraviolet) curing, is completed within 90 min. This microfluidic mixer showcases efficient mixing capabilities, rapid processing, and a compact design, marking it as a promising advancement in microfluidic technology. The new microfluidic mixer is a major step forward in microfluidic technology, providing a cost-effective and flexible solution for various uses. Its compatibility with SLA additive manufacturing allows for quick prototyping and design improvements, making it valuable for research and practical applications in chemistry, biology, and diagnostics. This study highlights the importance of combining advanced manufacturing techniques with basic fluid dynamics to create effective and easy-to-use microfluidic solutions.  
micromizer; additive manufacturing; mix index; SLA
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Respiratory Rate
#### 3.6.2 RGB Depth  
[PERSON] and colleagues not only proposed and evaluated the use of CCD cameras for respiratory rate measurements but also authored a series of publications exploring new trends in contactless sensors such as Kinect. In 2015, the use of the Kinect camera for respiratory rate measurement was validated [46]. RGB depth images were captured and processed to obtain sinusoidal waves during exercise performed in a stationary cycle ergometer. The frequency found in the set of waves represented the respiratory rate. Later, the authors investigated whether the new method could provide good estimates of the ventilation threshold (VT). The experimental setup was maintained, but was applied to an incremental test, specifically to identify this variable. The authors found that respiratory rate measures are possible from increments above 160 W and ventilatory threshold values can be estimated with \(\pm\)10 W of deviation from the VT calculated by gas analyzer [72].
## 4 Fuzzy Decision Model for Battery Recharging
### Fuzzy Rules  
The fuzzy rules establish the decision-making framework of the system. Each rule outlines conditions based on the input variables and determines the corresponding output decision. Our system adheres to the following rules:  
1. If _EnergyLevel_ is _Critical_, then _Decision_ is _Recharge_.
2. If _EnergyLevel_ is _Caution_, and _DistanceToTarget_ is _Close_, and _DistanceToEnergySource_ is _Far_, then _Decision_ is _Recharge_.
3. If _EnergyLevel_ is _Caution_, and _DistanceToTarget_ is _Close_, and _Distance_ to _EnergySource_ is _Medium_, then _Decision_ is _FinishTask_.
4. If _EnergyLevel_ is _Caution_, and _DistanceToTarget_ is _Close_, and _DistanceToEnergySource_ is _Close_, then _Decision_ is _FinishTask_.
5. If _EnergyLevel_ is _Caution_, and _DistanceToTarget_ is _Medium_, then _Decision_ is _Recharge_.
6. If _EnergyLevel_ is _Caution_, and _DistanceToTarget_ is _Far_, then _Decision_ is _Recharge_.
7. If _EnergyLevel_ is _Operational_, then _Decision_ is _FinishTask_.
8. If _EnergyLevel_ is _Full_, then _Decision_ is _FinishTask_.  
To find the final decision, the first step in the fuzzy inference process is fuzzification, where the system evaluates each elementary condition in the rule premises. This involves taking the precise input values and mapping them to fuzzy values based on their membership in predefined fuzzy sets.  
Once the input values are fuzzified, the system calculates the premises of each rule (\"IF \(V\) is \(A\)\"). The result of this calculation is known as the activation value of the rule, which indicates how strongly the rule is triggered by the current inputs. Then, the implication function is applied to the conclusion of each rule, producing a fuzzy subset that represents the distribution of possible output values based on the activated rule.  
These fuzzy subsets, corresponding to the same output across different rules, are then combined using an aggregation method--often the maximum function. This step merges the outputs of all relevant rules into a single fuzzy subset.  
Finally, the aggregated fuzzy subset is converted into a precise output value through a process called defuzzification. Among various defuzzification methods, one of the most common is the centroid or barycenter method, which calculates the center of gravity of the area under the curve representing the fuzzy subset. We used this method in our simulations because it produces a single, clear output value that the system can use to make a final decision.  
Figure 4: Decision.
## 7 Future Research Directions
### Consideration of Uncertainty and Risk Management  
One critical aspect that requires further investigation in the field of multi-objective message routing for EnFVs is the consideration of uncertainty and risk management. In real-world transportation systems, uncertainties such as unpredictable traffic conditions, weather disruptions, or changes in vehicle availability can significantly impact the performance and reliability of message routing algorithms [87]. Moreover, flying vehicles face additional uncertainties related to airspace regulations, airspace congestion, and flight path deviations due to safety considerations. To address these challenges, future research should focus on developing routing optimization models that explicitly incorporate uncertainty and risk management mechanisms [88]. Bayesian approaches, probabilistic modeling, or uncertainty quantification techniques can be employed to represent and reason about uncertain factors in the routing optimization process. By integrating real-time data and predictive models, these approaches can estimate the likelihood of events and dynamically adapt the routing decisions to minimize potential risks.  
One potential avenue for research in this direction is to develop robust optimization models that consider the worst-case scenarios or robust objectives [89]. Robust optimization aims to optimize the system's performance against a set of plausible future scenarios, rather than assuming a single deterministic scenario. This allows for more resilient and adaptive routing decisions that can handle unforeseen events and uncertainties in the transportation system. Furthermore, the integration of risk management techniques can help in identifying and mitigating potential risks associated with message routing [90]. Risk assessment methodologies, such as fault tree analysis or failure mode and effects analysis, can be employed to identify critical failure points and vulnerabilities in the routing system. By incorporating risk mitigation strategies, such as redundant routes, backup communication channels, or dynamic reconfiguration mechanisms, the routing algorithms can enhance system reliability and resilience in the face of uncertain events. Research in this area could also explore the application of machine learning and data-driven approaches to improve uncertainty and risk management in routing. By leveraging historical data and learning from past routing decisions, machine learning algorithms can assist in predicting and mitigating potential risks and uncertainties. For example, anomaly detection techniques can identify abnormal traffic patterns or system behaviors, triggering proactive measures to ensure reliable message routing.  
In summary, future research in multi-objective message routing for EnFVs should give significant attention to the consideration of uncertainty and risk management. The integration of uncertainty quantification techniques, robust optimization models, risk assessment methodologies, and machine learning approaches can enhance the adaptability, reliability, and resilience of routing algorithms in dynamic transportation systems.
## 2 Traditional Channel Estimation Technique
Letting \(\mathbf{R}=E[\mathbf{x}(n)\mathbf{x}^{T}(n)]\) denotes the covariance matrix of input signal \(\mathbf{x}(n)\) and \(\uplambda_{\max}\) as its maximum eigenvalue. The well-known stable convergence condition of the SLMS is  
\[0<\upmu_{\text{LMS}}<1/\uplambda_{\max} \tag{13}\]  
In order to remove \(\mathsf{S}\alpha\mathsf{S}\) noise, the traditional SLMS algorithm [14] was first proposed as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\upmu\text{sgn}\left(e(n)\right)\mathbf{x}(n) \tag{14}\]  
To ensure the stability of SLMS, \(\upmu\) should be chosen as  
\[0<\upmu<\sqrt{2\upmu}\sigma_{e}(n)/\uplambda_{\max} \tag{15}\]  
where \(\sigma_{e}(n)\) denotes the unconditional variance of estimation error \(e(n)\). For later theoretical analysis, the \(\sigma_{e}^{2}\) is conditioned by  
\[ \begin{array}{ll}E\left\{e^{2}(n)\left|\mathbf{v}(n)\right.\right\}&\approx E \left\{e^{2}(n)\right\}=\sigma_{e}^{2}(n)\\ &=E\left\{\left[z(n)-\mathbf{v}^{T}(n)\mathbf{x}(n)\right]^{T}\left[z(n)-\mathbf{v}^{T}(n) \mathbf{x}(n)\right]\right\}\\ &=\gamma^{2/\alpha}+\text{Tr}\left\{\mathbf{R}\mathbf{C}(n)\right\}\end{array}  \tag{16}\]
## 2 Related Works
### Target-Based Approaches  
Target-based approaches tend to find the placement of cameras according to a target of interest. The target can be an object, an event or a phenomenon that may evolve in space and time. The target-based approaches focus mainly on the nature of the target rather than focusing on the level of coverage [6].  
In [7], the authors propose a method for automatic sensor placement based on 3D CAD data file. The purpose of this method is to find the best viewpoint positions of a vision-sensor with the appropriate parameters such as position, orientation, and optical setting. In addition, each viewpoint has to satisfy some predefined constraints that are mainly related to the physical and optical properties of the sensor. This approach was developed for the field of model-based robot vision. The main goal is to move one sensor (using a robot) from one position to another one around the object to monitor and analyse important features that characterize the studied target. Likewise, there are other similar works related to 3D reconstruction of objects and space such as [9, 10, 11], _etc_.  
In [6], the authors introduce an analytical formulation to monitor the locomotive motion paths taken by people through a scene of interest. This method focuses on maximizing the observability of the target and capturing its motion. The authors stipulate that their approach requires only a minimum set of a priori knowledge about the target and no a priori knowledge about the physical landscape where the event takes place.  
Based on the results of the paper [1,6] have developed a solution that combines fixed and mobile cameras to define the placement of a Surveillance Camera Network. The authors propose to use fixed cameras to observe the entire scene in order to extract the movement of targets (people in this case). Then, this obtained knowledge is used to determine the placement of mobile cameras based on quality criteria which are: Foreshortening, ground coverage, and resolution.
# VANET Clustering Based Routing Protocol Suitable for Deserts
## 5 The Proposed Cluster-Based VANET Routing Protocol
### Cluster Member's Communication with the Public Networks  
In normal situations, only vehicle types \(V_{m}\), \(V_{s}\) and \(V_{ms}\) can communicate with the outside networks. Vehicles always prefer using the VANET, but if the destination is out of the VANET coverage or they need to communicate with public networks and the destination is unreachable through  
Figure 9: Routing algorithm for public networks and between clusters.  
the multi-hop VANET, in such cases, the source CH firstly searches its cluster members to find a member that is equipped with an appropriate communication link to communicate with the public network. If no cluster members are equipped with suitable equipment to communicate with the public network, then it forwards the request to the neighboring CH, which is equipped with the appropriate communication link, as shown in Figure 10. Neighboring CHs upon receiving the communication request will proceed as follows.  
1. Check the request. If this is the first time receiving, it continues the procedure; if not, the request will be discarded.
2. Check whether its equipment is type \(V\), and then, it will skip to Step 5.
3. If the vehicle is type \(V_{m}\), then it will skip to Step 9.
4. Check whether this cluster has a free available \(V_{m}\) or \(V_{ms}\), then it forwards the routing request and waits for a response, then skips to Step 6.
5. Forward the routing request to the neighboring CH, and wait for a response.
6. If the waiting time exceeds \(t_{r}\) and it does not receive the route response, then it retransmits the request and adds one to the request number; otherwise, skip to Step 8.
7. If the number of retransmissions exceeds the limit \(r_{max}\), then end the route request process; in the case of vehicle \(V_{m}\), return to Step 4, while for \(V_{ms}\), return to Step 5.
8. If the CH receives more than one route response, it will choose the vehicle with the fewest number of routing hops, the minimum cost and the shortest distance to the destination. At the same time, it stores the other routes as backup routes.
9. It Adds its own \(msg\) to the (_REEP msg_) message and forwards the message to the previous hop CH in the source direction. If the routing process fails to find a vehicle type \(V_{m}\), the source vehicle searches for a vehicle type \(V_{s}\) using a similar way as mentioned in the above steps. If it is still not able to establish a route through a satellite or mobile connection, it will send a _REER_ notification to the source vehicle indicating a routing failure.
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 2 Materials and Methods
### Error Analysis
#### 2.2.2 Fitting of the Absorption Signal  
In data processing, it is necessary to perform line-shape fitting on the absorption signal to obtain the IA. The fitting of the absorption signal can be transformed into the solving of a nonlinear least-squares problem. The essence of the problem is to obtain a set of parameters \(\beta\) such that \(\chi^{2}(\beta)\) is a minimum value:  
\[\chi^{2}(\beta)=\sum_{j=1}^{j=n}\big{[}a_{j}-F\big{(}v_{j},\beta\big{)}\big{]} ^{2}, \tag{14}\]  
where \(a_{j}\) is the absorbance corresponding to the absorption signal data point \(j\) and \(v_{j}\) is the corresponding wavenumber, which is simulated using Equation (12) in this work. \(F(\
u,\beta)\) is the regression function, which is a single Voigt line-shape function in this work.  
In most practical applications, because \(a_{D}\) (i.e., the Doppler HWHM) and \(a_{L}\) (i.e., the collisional HWHM) have clear physical meanings, they are generally used as parameters for fitting [31]. To facilitate the analysis, this work uses the normalized coefficient \(x^{\prime}\) and theline damping parameter \(y\) instead of the two HWHMs as parameters for fitting. Therefore, the fitting parameters \(\beta=(\
u_{f},A_{f},x^{\prime}_{f},y_{f})\) and the regression function are written as  
\[F(\
u,\beta=(\
u_{f},A_{f},x^{\prime}_{f},y_{f}))=A_{f}\frac{1}{\sqrt{\pi}}x^{ \prime}_{f}K(x^{\prime}_{f}(\
u-\
u_{f}),y_{f}). \tag{15}\]  
To solve the above problem, this work uses the Levenberg-Marquardt fitting algorithm (L-M algorithm), which is widely used in most absorption analyses [32]. A more detailed introduction to the fitting algorithm can be found in the literature [33]. In actual calculations, the L-M algorithm has a certain dependence on the initial values of the fitting parameters. To achieve the best fitting effect, this work combines the calculation methods of Doppler and the collisional HWHM in reference [34] to pre-calculate the initial values of fitting parameters.  
The Voigt function has no analytical solution, and there are thus a variety of numerical methods and approximation formulas with which to calculate it [27; 35; 36; 37]. The calculation speed and accuracy of the approximation methods affect the solution of IA. Considering the calculation accuracy and speed, this work uses the 916 algorithm [38] to perform the high-precision simulation of the Voigt line shape. A Voigt line-shape function approximate calculation method (Fadf algorithm) proposed by [PERSON] is then used to fit the absorption signal [39; 40; 41; 42]. The Fadf algorithm is based on a Fourier expansion and Laplacian continuous fractions that enable a fast approximation of the Voigt function.  
The fitting parameter \(A_{f}\) obtained after the fitting iteration is the measured value of the IA. Combined with the theoretical true value \(A_{S}\) of the IA, the relative error of the IA (i.e., the IA error) is obtained as  
\[R=\frac{A_{f}-A_{S}}{A_{S}}. \tag{16}\]
# NuCap: A Numerically Aware Captioning Framework for Improved Numerical Reasoning
## 3 Methodology
### Image Encoder
#### 3.1.2 Region-Attended Object Encoder  
NuCap adopts RAOE to extract object-centric features based on a Transformer-based object detection architecture. Following the GRIT framework [26], RAOE leverages Deformable DETR [28], which enhances multi-scale feature learning while maintaining low computational overhead.  
The input to RAOE consists of the multi-scale feature maps \(F=\{F_{1},F_{2},F_{3},F_{4}\}\) where each \(F_{i}\) represents a different spatial resolution, ranging from fine to coarse. To unify their dimensions, each feature map is processed through a shared \(1\times 1\) convolution followed by group normalization (GN):  
\[\hat{F}_{i}=\text{GN}(\text{Conv}_{1\times 1}(F_{i})),\quad\forall i\in\{1,2,3,4\} \tag{3}\]  
A set of learnable object queries is initialized as \(Q^{(0)}=\left\{q_{i}\right\}_{i=1}^{N_{q}}\) where \(N_{q}\) is the number of object queries. Each query \(q_{i}\) serves as an object-centric representation and is refined through iterative attention-based interactions with the multi-scale features\(\tilde{F}=\{\tilde{F}_{1},\tilde{F}_{2},\tilde{F}_{3},\tilde{F}_{4}\}\). These object queries first undergo self-attention to model inter-query relationships:  
\[\hat{Q}^{(l)}=\text{Self-Attention}(Q^{(l-1)}) \tag{4}\]  
This is followed by deformable cross-attention, in which the object queries attend to multi-scale visual features at sampled reference points:  
\[\tilde{Q}^{(l)}=\text{Deformable-Attention}(\hat{Q}^{(l)},\tilde{F},R^{(l-1)}) \tag{5}\]  
Here, \(R^{(l-1)}\) denotes the reference points propagated from the previous layer. The initial reference points \(R^{(0)}\) are computed from the object queries via a linear projection followed by a sigmoid activation:  
\[R^{(0)}=\sigma(W,Q^{(0)}) \tag{6}\]  
These reference points \(R^{(l)}\) guide the attention mechanism across multiple feature levels, enabling the model to dynamically focus on spatially relevant regions. The attended features \(\tilde{Q}^{(l)}\) are then passed through a feedforward network (FFN) to update the object query representations:  
\[Q^{(l)}=\text{FFN}(\tilde{Q}^{(l)}) \tag{7}\]  
The decoder iterates over \(L\), layers, progressively refining the object-centric features. The final output, denoted as \(F_{\text{RAOE}}\), obtained from the last decoder layer, encodes both spatial and semantic information and serves as a key visual input for the caption decoder.
## 4 Experimental Environment and Methodology in Smart Farming Environment
### Methodology
Accurate labeling is essential for ensuring the model's robustness, especially in the context of smart agriculture, where precise monitoring of soil and crop conditions is needed for optimal resource management. By addressing these challenges through pseudo-labeling, the proposed method improves labeling efficiency and accuracy, enabling the model to deliver consistent and reliable classifications for various agricultural sensor states. This approach supports the effective deployment of machine learning models in smart farming applications, contributing to improved operational decision making and resource optimization.