esa-sceva/satcom-chunk-collection huggingface dataset:
# Interoperability of Digital Tools for the Monitoring and Control of Construction Projects
## 4 Results and Discussion
### Cyber-Physical Systems
#### 4.1.1 Field Data Capture Technologies (FDCT)
In this way, it is possible to validate the geometry of 3D models with geospatial techniques, such as GPS coordinates, and thus perform centimeter-level precision measurements, such as length, area, and volume measurements [43]. These accuracy measurements make it possible to evaluate the amount of work performed and efficiently and accurately identify construction deviations [19]. There are a considerable number of studies on outdoor progress monitoring; however, research on indoor progress monitoring is lacking. Researchers have used image processing methods or laser scanners but have not utilized photogrammetry [19]. Machine teams, robots and employees can collaborate on shared building activities thanks to device-to-device communication, which can connect many concurrent processes and enable new modalities of construction. These possibilities come with obstacles and necessitate the development of new enabling technologies, such as domain-specific hardware and software tools tailored to the needs and restrictions of the construction industry [54]. The use of the technologies could be conditioned to meteorological conditions [55]. In the case of the use of UAVs and image capture, recreational and semi-professional drones may not be prepared to withstand rain, for example. Additionally, light conditions on a sunny or cloudy day may affect the quality of the images, so it is necessary to consider weather conditions when planning flights [55; 56; 57].
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 5 Qualitative Evaluation  
In this section, we evaluate the accuracy of the pose estimated by our approach. The qualitative results are illustrated in Figure 6. We present results of superquadric and pose parameter estimation from four scenes. The images in column Figure 5(a) depict RGB images of the scene, Figure 5(b) the visualized normal map, Figure 5(c) the binary segmented image (Section 3.2), Figure 5(d) the labeled image, and Figure 5(e) the visualized superquadric surface and ground plane (colored in grey). Note that the RGB images are shown just as the reference, and the pixels of the person are eliminated due to the label matching in Section 3.4. The figure confirms that the shape and the pose of each primitive-shaped object are estimated in different scenes. Moreover, it is evident that the pose of primitives can be successfully estimated even if the user touches and moves with the objects.  
We compare the pose estimation results with three conventional methods and one baseline method. The qualitative results are shown in Figure 7. Note that the estimated superquadric surface is used to visualize the result of pose estimation. In these scenes, miss-segmentation was occurred due to the interaction with the user and the object (drew a rectangle in red). Although the pose estimation failed  
Figure 6: Output images of the each processing step at different scenes. (**a**) RGB image of the captured scene, (**b**) normal map, (**c**) geometric segmentation result, (**d**) labeling result, and (**e**) superquadric and pose parameter estimation result. Superquadric surfaces were rendered onto 2D images.  
with the baseline and the conventional methods (Figure 7b-e), our proposed method successfully estimated the pose of each object (Figure 7f). As the geometric segmentation cannot distinguish the pixels of person and object, the pose estimation is conducted with an entire labeled pixel. This verifies the robustness of the proposed method against the miss-segmentation of object pixels.
## 1 Introduction
The architecture is designed based on the results of the study presented in Section 1.1 and the ongoing smart campus project of Ming Chuan University in Taiwan. In the proposed architecture, essential elements of a context-aware smart classroom are defined based on our study results, including a novel technology integration model for building and managing multiple smart classrooms in the university campus, and supporting measures for physically implementing the model. In this work, the Raspberry Pi (RPi), a small and affordable computer for learning computer programming [7], is used as the major device for smart classroom control. Our study shows that architecture for massively building, maintaining, and integrating smart classrooms with a campus-wide view, a novel model, and learning based smart classrooms design has not appeared in previous studies. As a result, the way of implementing each component of the proposed architecture are discussed in detail and are demonstrated with examples in this work. The remaining part of this study is organized as follows. The related works are reviewed in the following subsections of this section. The components of the proposed architecture, including a context-aware smart classroom prototype, a technology integration model, and the supporting measures for smart classroom operations, are described in Section 2. The implementations to prove the concepts of the proposed architecture are demonstrated in Section 3. Discussions and conclusions are given in Section 4.
## 2 Related Works
### Traditional Methods  
The traditional methods employed for enhancing underwater images, such as histogram equalization [11], wavelet transforms [12], and the Retinex algorithm [13], exhibit certain drawbacks. [PERSON] et al. [32] utilized Particle Swarm Optimization (PSO) to mitigate the effects of light absorption and scattering, but this approach may be susceptible to noise and demands meticulous parameter tuning. [PERSON] et al. [33] proposed a Rayleigh distribution histogram stretching method that enhances contrast; however, it can lead to excessive enhancement and loss of image details. [PERSON] et al. [34] presented a variational Retinex-based method for underwater image enhancement, which may result in oversmoothing and the loss of fine details. [PERSON] et al. [35] developed a Bayesian Retinex method incorporating multi-order gradient priors, but its iterative optimization process can impose a significant computational burden. The UDCP algorithm [9], based on the dark channel prior, offers simplicity in implementation; however, it may yield inaccurate results in the presence of complex underwater color interference. Furthermore, [PERSON] et al. [36] introduced MLLE, an efficient and robust approach, but it may still exhibit limitations in handling certain challenging underwater scenarios. The depth estimation model proposed by [PERSON] et al. [10] based on the Underwater Light Attenuation Prior (ULAP) suffers from performance degradation in complex underwater environments due to the presence of color interference factors. These conventional methods often lack the flexibility required for implementation, necessitating accurate parameter estimation and valid underwater optical properties to achieve satisfactory results. Additionally, the computational complexity and multi-step processing of these algorithms can result in significant time overhead.
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 3 Methodology
### Change Point Detection
#### 3.2.2 KL Divergence  
In this method, audio segments are modeled as Gaussian distributions. The KL divergence between two segments \(X_{i}\sim N(\mu_{1},\Sigma_{1})\) and \(X_{i+1}\sim N(\mu_{2},\Sigma_{2})\) is computed as:  
\[D_{KL}(X_{i}\parallel X_{i+1})=\frac{1}{2}\bigg{(}\text{tr}(\Sigma_{2}^{-1} \Sigma_{1})+(\mu_{2}-\mu_{1})^{T}\Sigma_{2}^{-1}(\mu_{2}-\mu_{1})-d+\log\frac {|\Sigma_{2}|}{|\Sigma_{1}|}\bigg{)} \tag{4}\]  
where:  
* \(X_{i}\), \(X_{i+1}\) represent two consecutive segments distributions.
* \(\mu_{1}\), \(\mu_{2}\) are mean vectors of the distributions \(X_{i}\) and \(X_{i+1}\), respectively.
* \(\Sigma_{1}  
* \(d\) is the dimensionality of the data or the number of features in \(X_{i}\).  
If KL difference is larger than the given threshold, it indicates a change point; otherwise, it does not. This approach requires specifically choosing the threshold value, as it is sensitive to this value. An inappropriate threshold may lead to either missed detections or false alarms, affecting the accuracy of change point detection.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 5 Constraints of IPCB Geometry Configuration  
IPCB geometry configuration faces many constraints because of its unique features and management strategy, such as airspace constraints, flight altitude constraints, and time interval constraints.
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 6 Experimentation and Result Analysis
### Result Analysis under Various Vehicle-Density Ratios
#### 6.3.1 Test Case 1: Low Density (10 Vehicles)  
In test case 1, we used low-density vehicles, which are arbitrarily scattered using uniform distribution in deployment area A. The case of V2I transmission was considered, in which a server is considered to be infrastructure, and aims to communicate the information towards all the vehicles in the VANET. However, this could lead to the dissemination of data towards accident scenarios or malicious road information in some realistic scenarios. In our experimentation, we fixed the server at location (0,0) to retain the best route, and to eradicate the use of the same nodes for manifold routes. Figure 6b displays the mean route-discovery performance achieved across 1000 Monte Carlo tracks. It shows that JMA discovers the best route path in whole run ranges. Comparison of the various algorithms shows that JMA finds the best route more reliability in the fast-run range compared with other algorithms, namely GA, Cuckoo, and PSO.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Parameter Values** \\ \hline \multirow{5}{*}{GA variants} & Crossover rate = 0.8 \\  & Mutation rate = 0.2 \\  & Selection rate = 0.8 \\  & Roulette wheel selection operator \\  & Elitism rate = 0.5 \\ \hline Cuckoo Search & Probability of abandoning eggs = 0.2 \\ \hline \multirow{2}{*}{PSO Variants} & Cognitive parameter C1 = 0.5 \\  & Social Parameter C2 = 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter configurations of the algorithms compared in our experimentations.  
Figure 5: Mean route reliability results achieved over 1000 Monte Carlo trails based on varying population size (\(N_{P}\)).  
The results for best route discovery of JMA is presented in Figure 5(a). Here, we can clearly note that JMA finds the best route in the initial fast-run range and sustains the same for the long-run range. The CS algorithm can find the best route only in the mid-run range, and deviates from the best route in a course of iterations. Figure 6 shows the outcome of the worst route reliability for all algorithms. Here, JMA has achieved enhancements of 9.23%, 8.98%, and 7.24% compared with the GA, PSO and CS algorithms, respectively. Thus, the results show that the overall performance of JMA is satisfactory with a low-density vehicle rate.
## 7 Conclusions  
In this paper, we propose a new CSTO mechanism for ISCC that allows vehicles to transmit only the sensing data that are non-redundant with the RSU during task offloading. The optimization objective is to jointly optimize task offloading decisions and communication resource allocation to minimize the processing delay of all vehicular sensing tasks. Since the optimization problem is a complex MINLP problem, it is difficult to solve using traditional methods. Therefore, we design a two-stage iterative optimization algorithm that decomposes the original problem into a task offloading decision subproblem and a communication resource allocation subproblem and solves them iteratively to obtain a suboptimal solution. The superiority of our scheme is demonstrated through simulation experiments with four different parameters and by comparing it with three baseline schemes.  
In addition, our scheme may perform poorly in IoV scenarios that involve a large number of vehicles and RSUs. For example, as the number of vehicles and RSUs increases, the convergence speed and performance of the DQN algorithm may decrease in larger state and action spaces. Although convex optimization can provide the optimal solution for the communication allocation subproblem, the iterative process of the proposed two-stage algorithm may lead to significant computational overhead in real-time applications.  
Figure 8: RSU workload with or without collaborative computing.  
Figure 7: Total task processing delay with different computing resources of vehicles.  
Therefore, it is worth investigating more advanced reinforcement learning algorithms (e.g., Proximal Policy Optimization, PPO) to address complex and dynamic IoV scenarios. Furthermore, we are going to consider the impact of synchronization delays on task offloading and resource allocation.  
Conceptualization, B.H., X.F. and Y.Z.; methodology, B.H. and N.C.; software, B.H. and S.Z.; writing--original draft preparation, B.H.; writing--review and editing, X.F., Z.G. and H.-C.C.; project administration, L.H.; funding acquisition, Y.Z., L.H., Z.G. and H.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grants 62371406, 62171392, the Natural Science Foundation of Xiamen, China under Grant 3502Z202473053, the Key Science and Technology Project of Fujian Province under Grant 2024H6030, and the National Science and Technology Council of Taiwan, R.O.C., under Grant NSTC 112-2221-E-259-003-MY2.  
Not applicable.  
The data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Motion Mapping
### Locomotion Mapping
#### 3.5.2 Displacement  
The position of the Spine_Base skeleton point (point 0) is regarded as the position of the demonstrator. The vector pointing from the previous position of the Spine_Base point to the current position of Spine_Base is represented by \(\vec{V}_{\text{p<}}\). After the errors caused by vertical deviations of the Spine_Base point have been eliminated, the distance between the positions of the human demonstrator in two adjacent locomotion loops can be obtained as follows:  
\[|\vec{V}^{\prime}_{\text{p<}}|=\sqrt{(x_{\text{c}}-x_{\text{p}})^{2}+(z_{\text {c}}-z_{\text{p}})^{2}}, \tag{28}\]  
where \(\vec{V}^{\prime}_{\text{p<}}\) is the projection of \(\vec{V}_{\text{p<}}\) onto the horizontal plane, \(x_{\text{c}}\) is the \(x\)-coordinate of the Spine_Base point in the Kinect frame in the current locomotion loop, and \(x_{\text{p}}\) is the \(x\)-coordinate of the Spine_Basepoint in the Kinect frame in the previous locomotion loop. Similarly, \(z_{\rm c}\) is the \(z\)-coordinate of Spine_Base in the Kinect frame in the current locomotion loop, and \(z_{\rm p}\) is the previous \(z\)-coordinate. Then, we can obtain the angle \(\varphi\) as depicted in Figure 9, using the same approach as for the calculation of \(\omega\). First, the angle between \(\vec{V^{\prime}}_{\rm p\text{-}c}\) and \(\vec{N^{\prime}}_{\rm pLT}\) is introduced as follows:  
\[\theta_{\varphi}=\arccos(\frac{\vec{V^{\prime}}_{\rm p\text{-}c}\cdot\vec{N^{ \prime}}_{\rm pLT}}{|\vec{V^{\prime}}_{\rm p\text{-}c}||\vec{N^{\prime}}_{\rm pLT }|}). \tag{29}\]  
Then, \(\varphi\) can be obtained as follows:  
\[|\varphi|=\pi-\arccos(\frac{\vec{V^{\prime}}_{\rm p\text{-}c}\cdot\vec{V^{ \prime}}_{\rm p17\text{-}21}}{|\vec{V^{\prime}}_{\rm p\text{-}c}||\vec{V^{ \prime}}_{\rm p17\text{-}21}|}), \tag{30}\]  
\[\varphi=\begin{cases}|\varphi|,&\text{if }\theta_{\varphi}\leq\frac{\pi}{2},\\ -|\varphi|,&\text{if }\theta_{\varphi}>\frac{\pi}{2}.\end{cases} \tag{31}\]  
Thus, we can obtain  
\[\Delta x=|\vec{V^{\prime}}_{\rm p\text{-}c}|\sin\varphi, \tag{32}\]  
\[\Delta y=|\vec{V^{\prime}}_{\rm p\text{-}c}|\cos\varphi, \tag{33}\]  
where \(\Delta x\) is the displacement along the \(x\)-axis of the human base frame and \(\Delta y\) is the displacement along the \(y\)-axis of the human base frame. For convenience in task execution, we directly apply the displacements of the human demonstrator to the robot.  
The displacement of the human's left foot between two adjacent locomotion loops, \(d_{l}\), and the displacement of the right foot, \(d_{r}\), are both considered. If both \(d_{l}\) and \(d_{r}\) meet or exceed a threshold value of \(d_{0}\), then the robot will enter the walking mode to execute locomotion with the corresponding displacement.
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 5 Experiment and Performance Verification
### Comparative Experimental Setup  
To more fully verify the effectiveness of the proposed solution, the experiment compared the following four different schemes:  
1. A task offloading strategy based on the edge computing optimization algorithm (EMC-PPO).
2. A task offloading strategy based on the edge computing optimization algorithm without edge collaboration (EMC-PPO-NO), which is also solved using the PPO algorithm. However, there is no collaboration between edge nodes, and when an edge node lacks the required service cache for task execution, it can only download the cache from the central cloud.
3. Complete offloading to the central cloud (CLOUD).
4. A random offloading strategy (Random).
## 4 Evaluation of Biological Properties
### Immunomodulatory Effects  
Recently, modulation of the immune system has become a promising way to treat several human pathologies. Indeed, the reactivity of our immune system can be decreased due to several risk factors. This decrease induces a disequilibrium between immune response and disorders, including pathogenic microbial infections. In several ways, reinforcement of the immune system can establish homeostasis and remove these disorders. Moreover, it is well established that regulatory T cells (Treg) have a significant contribution to managing immune homeostasis. In particular, some previous works have indicated that Treg cells possess an essential function in inhibiting colitis-induced inflammatory responses by regulating inflammation and suppressing the release of inflammatory cytokines through modulating different genes such as NF-\(\kappa\)B, TNF-\(\alpha\), interleukins (IL-1 and IL-6), and chemokines [123].  
[PERSON] et al. investigated the immunomodulatory effects of daucosterol in a mouse model of DSS-induced colitis. This research demonstrated that this molecule could regulate the population and activation of immune cells, including Treg cells, macrophages, B1 cells, and NK cells in colitis. These cells are involved in several diseases (malignant tumors, infections, mucosal immunity, allergies, etc.). They also found that this bioactive compound suppresses ROS and colitis-induced inflammatory cytokines, such as IL-6, TNF-\(\alpha\), IFN-\(\gamma\), and IL-1\(\beta\), and is associated with downregulation of macrophage infiltration and upregulation of Treg cell number [31].  
To discover new immunoregulatory approaches using natural drugs isolated from medicinal plants to fight against illnesses caused by _Candida albicans_, [PERSON] et al. evaluated the possible immunomodulatory activity of daucosterol against disseminated Candidiasis in mice by regulating the immune response. They observed that splenic CD4+ T cells (DSC4T) in daucosterol-treated mice produce IFN-\(\gamma\) and IL-2 cytokines more abundantly than cytokines IL-4 and IL-10, inducing the polarization of CD4+ T cells towards a Th1-type immune response [34]. However, some studies reported that the release of Th1 cytokine would enhance host resistance to disseminated candidiasis by improving the killing capacity of different immune cells, including activated macrophages, NK cells, and cytotoxic T cells against cells infected with _C. albicans_[124; 125]. Furthermore, [PERSON] et al. [40] proved the immunocompetent activity of daucosterol isolated from the leaves of _Liriodendron chianensis_. They demonstrated that daucosterol, among other bioactive compounds, markedly decreased the NO content of LPS-induced rat peritoneal macrophages and decreased splenic lymphocyte proliferation in mice [43]. These assessments could pave the way for new immunoregulatory strategies exerted by this biologically active compound for the treatment of numerous immunologic pathologies.
## 3 Results and Discussion
### Effect of the BA Natural Weathering
For the filtrate formed by stabilizing the <40.0 mm BA fraction in the laboratory for up to 10 weeks, the Cu concentrations (172.32 \(\pm\) 3.45 to 451.25 \(\pm\) 9.02 \(\mu\)g L\({}^{-1}\)) were above the limit values. Our experimental studies showed that the copper concentrations decreased rapidly (19.5-72.0%) during the first four weeks of the process. As the stabilization duration increased, the concentration change slowed and became almost stable after 16 weeks from the beginning of weathering (aging). After 24 weeks, all the BA eluate fractions had a Cu concentration <50 \(\mu\)g L\({}^{-1}\). In three fractions (<5.6 mm with natural weathering; <40.0 mm with natural weathering and with laboratory aging), after 4 weeks, the lead concentrations were higher than the leaching limit value (50 \(\mu\)g L\({}^{-1}\)) (Figure 10). The highest concentration of Pb occurred after 2 weeks in the laboratory-aged BA filtrate (411.25 \(\pm\)\(\mu\)g L\({}^{-1}\)). Even after 24 weeks, the concentration of Pb in the filtrate was higher than the leaching limit value. The concentrations  
Figure 10: Concentration of lead (Pb) in different fractions of bottom ash eluate and leachate during natural weathering and laboratory aging. Figure 9: Concentration of copper (Cu) in different fractions of BA eluate and leachate during natural weathering and laboratory aging. of Pb in all the eluate fractions decreased during stabilization and did not exceed the limit value. The concentrations of lead in the fractions became almost stable and did not exceed 25 \(\upmu\) L\({}^{-1}\) after 12 weeks of the weathering process. The concentration of zinc in the BA filtrate and the eluates of the different fractions did not exceed the limit value of 300 \(\upmu\)e L\({}^{-1}\) during the stabilization process (Figure 11). Compared to the other metals, the concentration of zinc in the eluates decreased the most rapidly, with a remarkable decrease (from 19.5% to 78.5%) already noticeable after two weeks of the weathering process. Compared to the concentration in the untreated bottom ash eluates, the zinc concentration in the \(<\)40.0 mm fraction was 93.9% and 91.5% smaller after 24 weeks under natural weathering and laboratory aging conditions, respectively. From this experimental research, we concluded that the metal concentrations in untreated MSWI bottom ash eluates exceed the limit values; therefore, they cannot be used in civil engineering. Three months is long enough to conduct heavy metal stabilization in MSWI BA. After the natural weathering process, the heavy metal concentration was lower than the limit values.