esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Mask R-CNN for Solid-Volume Food Instance Segmentation and Calorie Estimation
## 4 Calorie Estimation Approach  
The most common vegetable Gimbap contains 350-400 kcal. If different ingredients are used, such as tuna, cheese, and tempura, the calorie is close to 500 kcal. In this research, the calorie of one roll of basic Gimbap is defined as \(cal_{r}=375\) kcal. Usually, one roll of Gimbap is sliced into 10-12 pieces. Therefore, the calorie of one piece of Gimbap is defined as \(cal_{p}=35\) kcal. A Gimbap calorie estimation approach is proposed in Figure 6.  
One variable of the detection result is boxes \([y_{min},x_{min},y_{max},x_{max}]\), which is the bounding box coordinates. The bounding box area is calculated as (7)-(9):  
\[bbox_{x} =(x_{max}-x_{min})Img_{w} \tag{7}\] \[bbox_{y} =(y_{max}-y_{min})Img_{h} \tag{8}\]\[bbox_{area}=bbox_{x}\times bbox_{y} \tag{9}\]  
where \(Img_{w}\) and \(Img_{h}\) are the width and height of the test image.  
Based on the output binary mask matrix, the binary image is created. Pixels with values more than 0 are extracted. Using OpenCV findContours function, the mask contour is obtained. Then mask contour area \(cnt_{area}\) is calculated, based on OpenCV contourArea function. The area ratio is computed as:  
\[area_{ratio}=\frac{cnt_{area}}{bbox_{area}} \tag{10}\]  
As discussed in Section 2, the calibration is processed by calculating the physical size of each pixel on the \(x\) and \(y\) axes. For calculating Gimbap calories block, it contains several cases, based on the class of Gimbap. First, a gain \(gb\) is calculated as follows:  
\[gb=\left\{\begin{array}{l}\frac{bbox_{x}/bbox_{y}}{area_{ratio}}\ if\ bbox_{x}>bbox_{y}\\ \frac{bbox_{y}/bbox_{x}}{area_{ratio}}\ if\ bbox_{y}>bbox_{x}\end{array}\right. \tag{11}\]  
The pseudo-code to calculate Gimbap calories is as Figure 7, based on (11). The Gimbap class is obtained from the Mask R_CNN classification result. During the annotation process, class 1 is labeled as Gimbap1, and class 2 is labeled as Gimbap2. The Total_Calorie_of_Gimbap is equal to zero. It is accumulated based on the number of detections in one image. Finally, the calorie estimation of the combination of Gimbap is represented by Total_Calorie_of_Gimbap.  
The calorie estimation approach can be extended in any solid-volume food, such as pizza, cake, burger, fried shrimp, oranges, and donuts. Based on the Mask R-CNN algorithm, the mask area \(cnt_{area}\) and bounding box coordinates \([y_{min},x_{min},y_{max},x_{max}]\) are obtained. Depending on the calibration process, physical sizes corresponding to each pixel on the \(x\) and \(y\) axes \(L_{ppx}\) and \(L_{ppy}\) are obtained. The solid-volume food's surface area can be calculated within a certain range. If the calorie of each piece of food is defined, then the calorie of the food in the image can be estimated.  
Figure 6: Gimbap calorie estimation diagram.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 6 Performance Study
### Performance Comparison with Other Algorithms
#### 6.2.1 Effects of Node Density  
The effects of node density on network performance are examined for DASF, LPF and RAW algorithms. The node density varies from three to 12 nodes over a 60 \(\times\) 60 m\({}^{2}\) area, while other parameters keep default values. The calculated duty cycle interval values, using Equation (17), are {8.72, 10.36, 14.23, 18.51, 22.97, 27.51} s when the node density is {3, 4, 6, 8, 10, 12}, respectively.  
Figure 6: Effects of the node density. (**a**) effects on packet delivery ratio, (**b**) effects on average E2E delay, (**c**) effects on collected DCSR.  
As shown in Figure 5(a), the packet delivery ratio of three algorithms is high over different values of node density.  
As shown in Figure 5(b), while the E2E delay of DASF does not change much, that of RAW keeps increasing. With RAW, while a node forwards a packet, if there is no active node in the forwarding candidate set, the sender will wait until the closest node to the sink among candidate nodes wakes up. Therefore, the E2E delay increases with an increase of the duty cycle interval. Nevertheless, in DASF and LPF, the node chooses the first node to wake up among the potential receivers. Thus, as can be seen from Figure 5(b), DASF and LPF witness relatively consistent average E2E latency. Note that DASF achieves the lowest E2E delay in most cases. For example, when the node density is 10, packets are forwarded to the sink within 9.32 s on average under the DASF scheme. Meanwhile, the average E2E delay is 13.45 and 31.61 s under the LPF and RAW algorithms, respectively.  
As shown in Figure 5(c), only our algorithm can satisfy the delay requirement over the range of node densities. LPF can guarantee the delay bound until the node density is eight. When the node density is greater than eight, LPF's performance is under the requirement. For example, when the node density is 10, under DASF, 98.0% of the packets can reach the sink within 20 s compared to 90.5 and only 21.7% with the LPF and RAW algorithms, respectively. Additionally, our algorithm allows the operator to predict the number of needed nodes over a certain area in order to meet the delay requirement.
# Optimization of Coverage and Capacity Using Smart Antennae
###### Abstract  
In the rural and geographically remote regions of Taiwan, the high cost of establishing information infrastructure has resulted in significantly lower internet penetration and usage rates compared with urban areas. To address the network demands in such remote mountainous areas, the deployment of multiple mobile base stations has become essential. However, the wireless implementation of base stations can lead to signal interference issues. This research aims to enhance the signal reception capabilities of end-user devices by utilizing intelligent directional antennas. This study employs five directional smart antennas, each of which can be independently adjusted to be active or inactive. Unlike traditional omnidirectional antennas that cause interference in overlapping coverage areas for end-user devices, our proposed adaptive directional antenna algorithm optimizes energy consumption by selectively activating directional antennas and concurrently reduces signal interference problems for end-user devices. The results of this research offer valuable insights for improving network connectivity and efficiency in remote and underserved areas. Through experimental simulations conducted in an environment with 10 base stations per square kilometer, the utilization of smart antennas, as opposed to omnidirectional antennas, results in a significant improvement of 33.8% in signal coverage.  
coverage and capacity optimization; smart antennae; power control Article
# A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis
## 2 Review
### Kriging-Based Active Learning Algorithms
#### 2.2.2 U Learning Function  
The sign of the prediction in MCS is crucial for the reliability analysis. The point with a high potential risk of crossing the threshold \(G(x)=0\) should be added to the DoE. The potentially crossing points can present the properties of: (1) Being near \(G(x)=0\) or the threshold value; (2) Being significantly uncertain or having high Kriging variance; (3) Both happen simultaneously. As a result, Equation (15) is proposed as the learning function for AK based on prediction \(\hat{G}(x)\) and variance \(\hat{\sigma}(x)\)[4].  
\[U(x)=\frac{\big{|}\hat{G}(x)\big{|}}{\hat{\sigma}(x)} \tag{15}\]  
Here, it is assumed that the prediction \(\hat{G}(x)\) has a mean of zero based on Equation (11), then \(U(x)\) is obeyed to normal distribution with mean zero and covariance \(\hat{\sigma}(x)\). So, \(\mathcal{O}(U(x))\) is the probability of the points \(x\) correctly classified according to the sign of \(\hat{G}(x)\). When \(U(x)\) is smaller, the probability of being correctly classified is lower. The new training point is discovered to be the one with the minimal \(U(x)\). This process will continued until the stopping criterion \(min(U(x))\geq\gamma\) is satisfied. Commonly, \(\gamma=2\) is suitable, with the probability of correct classification being equal to 95.45%. AK-MCS has shown effectiveness in a variety of challenging instances. However, for the global design space modeling and small failure probability problem, the population required for AK-MCS will be very large. To overcome this problem, some methods, for example combined with IS, can be implemented, referring to study [55] for details.  
To approximate the performance function more accurately at the extreme region and critical region where \(G(x)\) is close to zero, adding the EI learning point and U function learning point to be training points simultaneously has been considered. It requires two completely independent learning functions, as well as two iterating stopping criteria, which will lengthen the calculation time and perhaps result in non-convergence.
## 3 Results
### Relevant Findings of the Studies  
There was a total of 13 studies analyzed, including 8 randomized clinical trials, 2 prospective clinical studies, 1 naturalistic study, and 1 retrospective study. The av  
Figure 4: Risk of bias domains of the included studies.  
erage age of participants across the studies reviewed varied slightly. Studies included children ranging from 4 to 8 years old, with some focusing on preschoolers and others including children up to 9 years old. This indicates that the research on the effectiveness of silver diamine fluoride (SDF) in preventing and treating caries in primary molars spans a broad age range typical of primary dentition.  
Regarding the techniques studied, a variety of interventions were investigated in the reviewed literature. These included comparisons between SDF and traditional methods like the atraumatic restorative technique (ART) or conventional restoration, as well as evaluations of SDF against other materials such as glass ionomer cement (SMART), sodium fluoride varnish, and resin sealants. Additionally, some studies assessed the effectiveness of SDF, either alone or in combination with other treatments like sedative filling or light cure calcium hydroxide. This diversity in studied techniques reflects the ongoing exploration of different approaches to managing caries in primary molars and highlights the interest in finding effective, minimally invasive interventions for young children.
## 6 Conclusions  
Wireless sensor networks (WSNs) are susceptible to attacks and interference in harsh environments, making reliability a crucial and essential issue. The reliability block diagram (RBD) technique has been used in various research related to WSNs and other different situations to evaluate reliability; however, all of these studies utilize RBD based on the component state of either working (1) or failing (0). Additionally, the reliability-based RBD technique assumes that the system fails if any component fails, however, the parallel system functions when at least one component is functioning. Our proposed RBD can calculate the system reliability based on the node energy, where the reliability is evaluated for different types of chains and various models of chain structures. A D-Chain is a chain that starts formation based on the nearest node to the BS and selects the chain head (CH) regarding the minimum distance of the node to the BS. On the other hand, a Q-Chain starts chain formation based on the farthest node from the BS and selects the CH based on the maximum weight. Each chain has three different models: a single-chain, a multi-chain/single-hop, and a multi-chain/multi-hop. The system reliability is calculated for all these models based on a stationary chain head (CH) and mobile CH. Our results show that Q-Chain reliability outperforms D-Chain reliability in all cases, models, and situations. A single chain is less reliable than a multi-chain for both D-Chain and Q-Chain, regardless of whether the CH is stationary or mobile. In addition, multi Q-Chain/single-hop outperforms all models in both cases, stationary CH or mobile CH. This work can be extended to calculate the system reliability-based throughput using the same concept as  
Figure 23: Multi-chain single-hop mobile CHs energy reliability-based proposed RBD: yellow (a) D-Chain, (b) Q-Chain.  
Figure 24: Multi-chain multi-hop mobile CHs energy reliability-based proposed RBD: yellow (a) D-Chain, (b) Q-Chain.  
the proposed RBD technique, evaluating the system reliability for different chain models and various network configurations.  
All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Evaluation and Results
### Access Outcome Analysis and Performance Metrics
#### 5.2.1 Access Decision Distribution  
* Granted: 356 requests (23.7%)
* Denied: 1144 requests (76.3%)
# A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals
###### Abstract  
Background:The rapid growth of international maritime trade has intensified operational challenges at marine terminals due to increased interaction between vessels, trucks, and trains. Key issues include berth congestion, inefficient truck arrivals, and underutilization of terminal resources. Ensuring coordinated planning among transport modes and fostering collaboration between stakeholders such as vessel operators, logistics providers, and terminal managers is critical to mitigating these inefficiencies. _Methods:_ This study proposes a multi-agent, multi-objective coordination model that synchronizes vessel berth allocation with truck appointment scheduling. A solution method combining prioritized planning with a neighborhood search heuristic is introduced to explore Pareto-optimal trade-offs. The performance of this approach is benchmarked against well-established multi-objective evolutionary algorithms (MOEAs), including NSGA-II and SPEA2. _Results:_ Numerical experiments demonstrate that the proposed method generates a greater number of Pareto-optimal solutions and achieves higher hypervolume indicators compared to MOEAs. These results show improved balance among objectives such as minimizing vessel waiting times, reducing truck congestion, and optimizing terminal resource usage. _Conclusions:_ By integrating berth allocation and truck scheduling through a transparent, multi-agent approach, this work provides decision-makers with better tools to evaluate trade-offs in port terminal operations. The proposed strategy supports more efficient, fair, and informed coordination in complex multimodal environments.  
marie terminals; distributed scheduling; truck appointment systems; berth allocation; multi-objective optimization Article
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 2 Angle-Based Parametrization
### Figure of Merit (FOM)  
The choice of an FOM is the most critical parameter on the optimization setup. The FOM is a value used to characterize the performance of the design. We evaluated the Y-junctions efficiency by the transmittance over a set bandwidth. To improve transmittance without affecting the area on-chip, we limited the size of the MMI by restricting the parameter range of the angles and setting an FOM directly proportional to the transmittance of the fundamental TE mode over a frequency window. We ran the optimizations over SCL (conventional) and OESCL (wide) bands to obtain MMI geometries and evaluated the performance.  
Our goal is to maximize the FOM. Let \(T(f)\) be the transmittance evaluated at the frequency \(f\). We propose the FOM as the transmittance accumulated from \(f_{1}\) to \(f_{2}\) shown in Equation (2).  
\[FOM=\int_{f_{1}}^{f_{2}}T(f)\,df \tag{2}\]  
where \(f_{1}=\frac{c}{\lambda_{1}}\), \(f_{2}=\frac{c}{\lambda_{2}}\), the frequency is in Terahertz, and \(c\) is the speed of light in the vacuum. The values of \(\lambda_{1}\) and \(\lambda_{2}\) depend on the operating band. We define two FOMs:  
1. For the \(SCL\) band, \(\lambda_{1}=1.625\)\(\upmu\)m and \(\lambda_{2}=1.46\)\(\upmu\)m. The ideal value for the FOM is 10.425 assuming rectangular transmittance windows with a maximum amplitude of 50% on one output waveguide along the SCL band.
2. For the \(OESCL\) band, \(\lambda_{1}=1.625\)\(\upmu\)m and \(\lambda_{2}=1.26\)\(\upmu\)m. The ideal value for the FOM is 26.721 assuming a transmittance of 50% on one output waveguide along the OESCL band.  
Figure 2: Layout of the top view of the splitter. The lengths of the widths \(W_{1,2,3,\ldots,\lambda}\) were optimized by the angles, \(\alpha_{i}\), which are the parameters of the optimization. The figure of merit (FOM) is the transmittance over a window of frequency taken at the _monitor_.  
Although it is more geometry-restrictive than density topology and level-set parametrization, our approach shows promising results and potential for improvements (see Section 4). We produced fabrication-ready devices without any additional post-processing steps for smoothing or tweaking the optimized geometry. In the same fashion, other fabrication constraints can be morphed into optimization parameters, as we propose here, to produce faster and direct results.
## 2 Introduction of Interpolator  
An interpolator is used to generate the velocity profile and position commands by interpreting a part program for each axis. For a linear interpolation (G01), Figure 1 reveals the S-shaped velocity and Acc/Dec profiles [22; 23] for a single block that consists of constant speed periods (A), a constant slope for the Acc/Dec periods (B), and a constant Acc/Dec period (C). Based on the S-shaped Acc/Dec control, the velocity profile can be calculated according to the maximum velocity \(f_{max}\), the maximum acceleration \(A_{max}\), the time constant of the S-shaped Acc/Dec \(T2\), and the maximum jerk \(J_{max}\), as illustrated in Figure 1.  
Generally, each axis has an allowable acceleration value that is determined according to the driving capability of its servo motor. Compared with a single block, multiple blocks of numerical control (NC) codes require one more parameter, which is given as the corner velocity \(V_{c}\)[24] (Figure 2b). However, velocity discontinuity may occur when the velocity profile is distributed to each axis, as indicated in Figure 2c,d. Clearly, a higher corner velocity reduces the MT, but discontinuity can cause high acceleration and jerk for each axis as well as a large vibration in the corner.  
Figure 1: S-shaped acceleration/deceleration (Acc/Dec) control for a signal block.  
Figure 2: Corner velocity in velocity profile: (**a**) a sample NC code and multiple blocks (**b**) tangent velocity profile (**c**) x-axis velocity profile (**d**) y-axis velocity profile.  
To reduce discontinuity, the Acc/Dec after interpolation (ADAI) [12] for each axis is applied. The ADAI is a finite impulse response 25 filter with a time constant equal to \(T_{a}\) that smooths the acceleration and jerk profile, as displayed in Figure 3. The application of ADAI sacrifices contour accuracy and increases the MT. Table 1 reveals how the machine performance varies as the interpolation parameters change. The tradeoff among various parameter settings for the contour errors, MT, and corner vibration (CVib) motivated the current researchers to propose an optimization method based on the constructed ANN model.
## 6 Experimental Evaluation
### Experimental Results: Performance of RTF Estimators  
In this subsection, we compare our eKF-RTF estimator with the well-known eigenvalue decomposition (EVD) [11] and covariance whitening (CW) [9] sub-space methods for RTF estimation. The results are shown in Table 4. In addition, we show the results obtained with an oracle estimation of the RTF (OracleC) as a performance upper-bound. This oracle RTF was obtained from the clean speech signals using Equation (3) for time-frequency bins where speech presence was detected (using the speech presence probability obtained from clean speech), while the RTF of the previous frame was reused for the remaining ones. The evaluation is carried out in terms of speech distortion (SD) and the speech intelligibility metric STOI. We evaluate SD instead of PESQ because here we are only interested in the distortion introduced over the reference clean speech due to RTF estimation errors when using a distortionless beamformer. The comparison is performed using the best SAP estimator obtained for each device placement according to Table 3 (eKF-PLDn for CT and eKF-P&C for FT). A similar improvement due to our proposed SAP estimators, as the one observed in Table 3 for the eKF-RTF estimator, is expected for the other RTF estimators (due to the fact that these estimators would also take advantage of more accurate estimates of the noise SCM). We compare the RTF estimators using only one SAP estimator in order \begin{table}
\begin{tabular}{c l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Place.**} & \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**SD (\%)**} & \multicolumn{8}{c}{**STOI (\%)**} \\ \cline{3-14}  & & \multicolumn{8}{c}{**SNR (dB)**} & \multicolumn{8}{c}{**SNR (dB)**} \\  & & **20** & **15** & **10** & **5** & **0** & \(-\)**5** & **20** & **15** & **10** & **5** & **0** & \(-\)**5** \\ \hline \multirow{4}{*}{CT} & EVD-PLDn & **0.52** & 0.66 & 0.98 & 1.53 & 2.46 & 3.64 & 96.96 & 93.72 & 87.76 & 78.70 & 66.89 & 54.88 \\  & CW-PLDn & **0.52** & 0.63 & 0.92 & 1.41 & 2.27 & 3.43 & **97.00** & **93.77** & 87.87 & 78.89 & 67.10 & 55.08 \\  & eKF-PLDn & **0.52** & **0.58** & **0.72** & **0.90** & **1.16** & **1.44** & 96.99 & **93.77** & **87.96** & **79.12** & **67.56** & **55.61** \\  & _OracleC-PLDn_ & _0.07_ & _0.11_ & _0.16_ & _0.22_ & _0.28_ & _0.34_ & 97.33 & 94.32 & _88.72_ & _80.05_ & _68.57_ & _56.60_ \\ \hline \multirow{4}{*}{FT} & EVD-P\&C & 3.64 & 3.56 & 4.03 & 5.12 & 7.11 & 10.04 & 95.56 & 91.84 & 85.38 & 75.80 & 63.69 & 51.69 \\  & CW-P\&C & 3.96 & 3.79 & 4.19 & 5.22 & 7.15 & 10.01 & 95.54 & 91.88 & **85.46** & 75.94 & 63.85 & 51.78 \\  & eKF-P\&C & **2.09** & **2.63** & **3.32** & **4.23** & **5.57** & **7.49** & **95.78** & **91.96** & 85.45 & **76.01** & **64.01** & **52.03** \\  & _OracleC-P\&C_ & _0.24_ & _0.45_ & _0.81_ & _1.26_ & _1.82_ & _2.43_ & 97.05 & _93.89_ & _88.23_ & _79.66_ & _68.18_ & _56.17_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Speech distortion (SD) and STOI results for different RTF estimators when used for MVDR beamforming. Results are broken down by both SNR and device placement.