esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Loss Function  
In this paper, we combine the binary cross-entropy (BCE) loss with Dice loss, which is calculated as in Equations (15)-(17).  
\[Loss_{BCE}=-[y_{n}logx_{n}+(1-y_{n})\log(1-x_{n})], \tag{15}\]  
\[Loss_{Dice}=1-\frac{2\sum_{i}x_{i}y_{i}}{\sum_{i}x_{i}+\sum_{i}y_{i}}, \tag{16}\]  
\[Loss_{total}=\lambda Loss_{BCE}+(1-\lambda)Loss_{Dice}, \tag{17}\]  
\(x_{n}\) denotes the value of the element in the prediction graph, and \(y_{n}\) denotes the value of the corresponding element in the labeling graph. \(\lambda\) is a hyperparameter. In the Dice loss formula, \(x_{i}\) is the probability value that the \(ith\) element in the prediction map belongs to a certain category of foreground, and \(y_{i}\) is the true value of the \(ith\) element in the labeled map. The Dice loss is not affected by the size of the foreground, and the BCE loss can play a guiding role for the Dice loss in the network learning process. The BCE loss provides pixel-level classification guidance with strong gradient signals for individual pixels, while the Dice loss addresses the class imbalance problem by focusing on the overlap between the predicted region and the real region. This is particularly important for cloud segmentation because the proportion of clouds/sky is significantly different. This combination leverages the local optimization ability of BCE and the global shape consistency of Dice, thereby achieving more robust boundary delineation and shape preservation.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation
### Evaluation against SkySensor
#### 4.4.3 Success Rate  
Success rate in retrieving a true skyline result is studied by varying the query rate. In this experiment, the number of dimensions in attribute space was fixed at 3. The query rate was varied from 0.02 packets/node/second to 0.1 packets/node/second. Queries were generated uniformly from different parts of the network. From Figure 16c, it is interesting to note that with a low query rate, the success rate of SkySensor is slightly higher than EDDS. However, this result reversed when the query rate was over 0.04 packets/node/second and then the success rate of SkySensor reduced as the query rate increased. This occurred due to the high concentration of all sensor readings around three clusters that eventually created congestion. A significant number of query requests and query responses were lost due to the interference, congestion and hotspot around the edge or gateway of the cluster.
## 5 Performance Analysis and Numerical Results
### Results of the Sensitivity Analysis on Properties of Data Dissemination
#### 5.2.2 Traffic Volume  
This experiment employed a data transmission mode with favorable data dissemination effectiveness: QPSK 1/2. A total of 40 (10 groups) simulations were completed. The macroscopic results of one group (No. 4 random number seed) are shown in Figure 11, while the comprehensive statistical results are summarized in Figure 12.  
Based on the furthest data dissemination distances observed on different roads in Figure 11, as indicated by the green line marks, it can be observed that the impact of an increased traffic volume on the macroscopic data packet dissemination speed is relatively limited. This observation is further corroborated by the aggregated statistical data presented in Figure 12, which reveal a similar trend: while the number of vehicles that receive data packets doubles with the doubling of the vehicular traffic volume, the increase in the  
Figure 11: Macroscopic results of one group (No. 4 random number seed).  
data dissemination distance is not significant, showing approximately 10% growth under 0.5\(\times\), 1\(\times\), and 2\(\times\) traffic volume conditions. Taking the experimental group depicted in Figure 11 as an example, under 1\(\times\) traffic volume conditions, the number of vehicles that receive data packets increases by 94% compared to 0.5\(\times\) conditions, while the data dissemination distance only increases by 7%. Under 2\(\times\) traffic volume conditions, the number of vehicles that receive data packets increases by 110% compared to 1\(\times\) conditions, while the data dissemination distance only increases by 13%. From a communication perspective, an increasing traffic volume leads to more vehicles successfully receiving data packets. However, it also decreases the SNR, reducing the probability of successful data packet reception. When considering the joint impact of these two factors, it becomes apparent that the influence of the traffic volume on the data dissemination distance is somewhat limited.  
There is a notable contrast in the data dissemination distance between 0.25\(\times\) and 0.5\(\times\) traffic volumes. This disparity is because the 0.25\(\times\) traffic volume is exceptionally low, leading to a sparse vehicle presence on the road. In specific locations, the significant distance between adjacent vehicles increases the likelihood of data dissemination failure, as illustrated in Figure 10(a), particularly observed in the middle section of the west-side north-south road. In general, when there is a certain baseline and continuous traffic flow, the overall impact of the total traffic volume on the data dissemination distance remains limited (around 10%). Interestingly, in specific scenarios, the data dissemination distance may even increase after a reduction in traffic volume in specific directions.
# BIM-Based Management System for Off-Site Construction Projects
## 2 Literature Review
### OSC Project Management Systems  
Traditionally, the overall system used for project management has been termed as the project management information system (PMIS). According to the Project Management Body of Knowledge 6 th Edition, a PMIS is an information system consisting of the tools and techniques used to gather, integrate, and disseminate the outputs of project management processes. [PERSON] and [PERSON] [73] proposed a system named the project management systems-application service provider (PM-ASP) to eliminate the inefficiency of traditional communication methods. Initially, PMISs were designed as online programs, software, or systems with information management (e.g., creating, sharing, and distributing) capabilities. However, with the growing importance of real-time monitoring and integrated management, the smart PMIS (SPMIS) was introduced, which involved advanced technologies, such as radio frequency identification (RFID), BIM [74], and simulation [75].  
An OSC project ends when a component from the origin reaches its destination. This component goes through its lifecycle along the supply chain with the information on the platform [76]. Several studies have highlighted the significance of the information flow and sharing and have proposed platform-based approaches. [PERSON] and [PERSON] [77] highlighted the lack of tools for data creation, access, exchange, and storage during an OSC project and proposed a system to address the information complexity from the SCM perspective. However, this system was a framework-level proposal that was not validated by practical application to an OSC project. Additionally, several case studies have been performed to establish management models to trace the life of precast concrete (PC) components with RFID. In addition to the use of RFID, [PERSON] and [PERSON] [78] developed an information exchange platform through cloud-based BIM. [PERSON], et al. [79] verified that the information exchange using BIM in OSC projects could help to enhance the SCM performance. Notably, the authors focused only on information acquisition with IT but did not advance to the automation or optimization of this information flow in terms of SCM. In addition, phase-by-phase focused management may limit communication with the individuals involved in other phases. Overall, none of the existing studies has focused on RFID and BIM-based integrated management systems (IMSs) that can connect the internal and external entities in each phase based on the SCM perspective and enhance the understanding of the OSC project.
## 1 Introduction
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Technology** & **Material** & **Reference** \\ \hline Backside wet etching + thermal annealing & Fused silica & [PERSON] et al. [12] \\ \hline Ablation of microholes + wet etching & Silicon & [PERSON] et al. [13] \\ \hline Ablation of microholes + wet etching & Fused silica, sapphire & [PERSON] et al. [14] \\ \hline Modification of photosensitive glass + thermal treatment, wet etching, annealing & Foturan glass & [PERSON] et al. [15] \\ \hline Ablation (pressure \& resoldification) & PDMS & [PERSON] et al. [16] \\ \hline Ablation (pressure \& resoldification) & Chalcohalide glass & [PERSON] et al. [17] \\ \hline Two-photon polymerization & Photoresist & [PERSON] et al. [18] \\ \hline Two-photon polymerization & Photoresist & [PERSON] et al. [19] \\ \hline Laser ablation + CO\({}_{2}\) laser polishing & Fused silica & [PERSON] et al. [20] \\ \hline Laser ablation + CO\({}_{2}\) laser polishing & Soda-lime glass & [PERSON] et al. [21] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Femtosecond laser based optics fabrication processes. Figure 1: Schematic illustration of the beam homogenizer principle for an imaging beam homogenizer consisting of two lens arrays (LA1 & LA2) and a Fourier lens (FL). The incoming intensity distribution is transferred into a Top-Hat profile while its shape is defined by the footprint of the single lens geometry. pressure as well as a long resolidification time of the material. A similar process has been shown by [PERSON] et al. [17] to fabricate single shot lenses in chalcohalide glass. Also the two-photon polymerization process can be used to fabricate different types of polymeric lens arrays shown by [PERSON] et al. [18] and [PERSON] et al. [19]. [PERSON] et al. [20] and [PERSON] et al. [21] applied the femtosecond laser to fabricate periodic microstructures in fused silica and soda lime glass, respectively, which are further polished and reshaped by a CO\({}_{2}\)-laser step to polish the preform and form the desired geometry. A similar process applying both, a femtosecond and a CO\({}_{2}\)-laser is also used in our lens array fabrication process. While a reshaping is essential in the previous publications, we define the lens geometry by precise layer-wise ablation with the femtosecond laser. In a second process, the CO\({}_{2}\) laser is used to reduce the previously introduced surface roughness while a reshaping of the initially femtosecond laser-generated geometry is supposed to be minimized. Furthermore, the most standard lens array fabrication processes use polymer substrates or include an etching step, while our process offers the fabrication of glass lens arrays without using polymer substrates or etching.
## 3 Epidemiological Model and Evolutionary Game Theory
### Evolutionary Game Theory
#### 3.2.1 Evolutionarily Stable Strategy  
In this study, evolutionary game theory is used to model the human behavior in infectious disease epidemics. Evolutionarily stable strategies, a very important concept in evolutionary game theory, are discussed in this section.  
In the basic idea of biological evolution, the number of individuals with high adaptability proliferates and the number of individuals with low adaptability decreases [29]. The distribution of individuals with various characteristics within a population change over time. In the study of populations, it is important to investigate the distribution of individuals that can realize a steady state in the long term. The steady state of a population is a state in which the distribution of individuals does not change. Natural selection is a concept in biological evolution. This is the idea that the population of highly adapted individuals increases, and the population of less adapted individuals decreases. An important question in biological evolution is how the distribution of a population changes when different types of individuals invade the population. A new individual invading a population is called a mutation. A steady state is said to be evolutionarily stable when the state is maintained without proliferation of mutations even when a small number of mutations invade the population.  
Then, we discuss evolutionarily stable strategies. Consider a steady state where all the individuals in a population chooses strategy \(s\). Suppose that a mutation that selects a different strategy \(t\) with a small ratio \(\epsilon>0\) invades the population. Such a population can be denoted as \((1-\epsilon)s+\epsilon t\). Figure 3 shows the invasion of strategy \(t\) into the population of strategy \(s\). The mutation that selects a different strategy \(t\) with a ratio \(\epsilon>0\) is called a mutation of strategy \(s\).  
The conditions for strategy \(s\) to be evolutionarily stable can be expressed in the following equation. Let \(u(s,(1-\epsilon)s+\epsilon t)\) be the payoff of adaptation of strategy \(s\) in the population \((1-\epsilon)s+\epsilon t\), and \(u(t,(1-\epsilon)s+\epsilon t)\) be the payoff of adaptation of strategy \(t\). The condition under which strategy \(s\) is more adaptive than mutation \(t\) is expressed in Equation (3). Figure 4 shows the invasion of mutations into the population.  
\[u(s,(1-\epsilon)s+\epsilon t)>u(t,(1-\epsilon)s+\epsilon t) \tag{3}\]  
When this condition is satisfied, strategy \(s\) is an evolutionarily stable strategy.
## 2 Materials and Methods
### Classification System  
The land cover classification system used in this study was adapted from the top-level land categories of the Intergovernmental Panel on Climate Change (IPCC) [50], including forest, cropland,grassland, wetland, water, settlements, and other land. To create detailed forest types and ensure the consistency of this forest classification with the other land cover data products to be compared in this study, the forest was further classified into six categories: evergreen needleleaf forest, evergreen broadleaf forest, deciduous needleleaf forest, deciduous broadleaf forest, mixed forest, and shrubland.
# The Effect of Blade Angle Deviation on Mixed Inflow Turbine Performances
## 2 Materials and Methods  
### Initial Rotor Design  
Two existing techniques in progress are available to sufficiently meet our ambitions, which engage in the description of the blade geometry. Its complex configuration characterizes the latter at the level of the multiple curvatures in three-dimensional form arranged between the shroud and the hub. The first technique considers the projection of the structure in the perspective of the blade on parallel and perpendicular planes to the wheel's axis of rotation. The geometry is defined at the hub of a functional representation of the two-dimensional curves. The second technique directly represents the geometry of the surfaces considered using a three-dimensional functional model by applying the Bezier polynomials mentioned in this article; this technique offers great interest for our choice to specify the blade geometry, which effectively adapts mixed inflow turbine. The meridian plane of rotor type A with constant blade angle [30]; Ref. [43] used in the mixed inflow turbines shown in Figure 1 is under examination [42]. The Bezier polynomial is used for this purpose. The relations below are respected (Equations (2) and (3)).  
\[r=(1-u)^{4}r_{0}+4u(1-u)^{3}r_{1}+6u^{2}(1-u)^{2}r_{c}+4u^{3}(1-u)r_{2}+u^{4}r_ {3} \tag{2}\]  
\[x=(1-u)^{4}x_{0}+4u(1-u)^{3}x_{1}+6u^{2}(1-u)^{2}x_{c}+4u^{3}(1-u)x_{2}+u^{4}x_ {3} \tag{3}\]  
The leading edge camber line is obtained by the following relations (Equations (4) and (5)):  
\[\theta=\theta_{ref}+\frac{1}{sin(\delta_{2})}\int\limits_{x_{ref}}^{x}\tan( \beta_{2b})\frac{dx}{r} \tag{4}\]  
\[r=r_{0\text{H}}+(x-x_{0\text{H}})\tan(\delta_{2}) \tag{5}\]  
To complete the remainder of the camber line, it is necessary to calculate the coordinates of items 0, 1, \(b\), 2, and 3 (Figure 2) given according to the blade dimensions in reference [30], where the Bezier polynomial is used. The following forms are applied, where \(x_{0},\ x_{1},x_{b},x_{3},\theta_{0},\theta_{1},\theta_{\text{b}}\) and \(\theta_{3}\) are axial and radial coordinates of items 0, 1, \(b\), and 3 (Equations (6) and (7)).  
\[x=(1-u)^{4}x_{0}+4u(1-u)^{3}x_{1}+6u^{2}(1-u)^{2}x_{b}+4u^{3}(1-u )x_{2}+u^{4}x_{3} \tag{6}\]  
\[\theta=(1-u)^{4}\theta_{0}+4u(1-u)^{3}\theta_{1}+6u^{2}(1-u)^{2}\theta_{\text{ b}}+4u^{3}(1-u)\theta_{2}+u^{4}\theta_{3} \tag{7}\]  
Figure 1: The blade Meridian view [42].
# Nozzle with a Feedback Channel for Agricultural Drones
## 4 Result
### Effect of the Feedback Channel  
To confirm the effectiveness of the feedback channel, an experiment without a feedback channel was conducted with similar settings as the developed nozzle. The no-load experiment was excluded because the spraying amount was insufficient, as observed in the feedback nozzle experiment. Figure 13(a) shows the variation in droplet size along the radial position and the average droplet diameter for the non-feedback nozzle. The size variation is similar to that for the feedback nozzle. The larger droplets are observed at the nozzle center, and the size decreases with an increase in the radial distance. As shown in Figure 13(b), the diameters in all the cases exceeded the appropriate range. This result shows that the feedback channel helps generate suitable sized droplets, as well as uniform spray.  
The spraying amount along the radial position and FWHM for the non-feedback nozzle are shown in Figure 15. Similar to the feedback scenario, the amount increased with a higher load, and the maximum amount was 20,036.8 mm\({}^{2}\) for the 50 kg load. FWHM is 1.329 at 30 kg, 1.375 at 40 kg, and 1.375 at 50 kg. Compared to the developed nozzle, the non-feedback nozzle has lower spraying amounts and FWHM values, confirming that the feedback channel is more effective for uniform spraying.  
Figure 12: (a) Variations of spraying amount along the radial position and (b) corresponding full width at half maximum (FWHM).  
Figure 14: (**a**) Distributions of averaged particle size along a radial direction and (**b**) comparison of total droplet diameter for the non-feedback nozzle.  
Figure 13: Normalized standard deviation of the spraying amount in various radial positions. The values are normalized by the averaged spraying amount.  
Figure 15: (**a**) Variations of spraying amount along the radial position and (**b**) corresponding FWHM for the non-feedback nozzle.
## 4 Experimental Validation  
### CASE 1: Performance Verification of IMFE Algorithm  
#### 4.1.1 The Selection of a Vibration Signal  
The length of a vibration signal impacts the performance of feature extraction algorithms. A signal that is too short can lead to significant feature fluctuations, while an overly long signal results in substantial time costs [33]. Therefore, this section examines the entropy value distributions of fuzzy entropy (FE), MFE, and IMFE across vibrations signal of varying lengths. Four specific signals are used in this case. A segment of white noise with a duration of 1 s is generated, with \(x_{1}\) sampled at 10,000 Hz. This white-noise signal is produced by drawing random samples from a standard normal distribution characterized by a mean of 0 and a standard deviation of 1. Uniformly distributed in the frequency domain, this white noise is suitable for a range of signal processing and system testing applications. \(\mathbf{x}_{2}(t)=5(1+\cos(4\pi t))\cos(20\pi t)+10(1+\cos(4\pi t))\cos(40\pi t )+15(1+\cos(4\pi t))\cos(60\pi t)+n\), where \(n\) is Gaussian white noise with a value of \(-20\) dB and \(x_{2}\) exhibits periodic characteristics resembling a sinusoidal waveform. \(x_{3}\) is generated by drawing random samples from a uniform distribution, with values ranging between \(-1\) and \(1\). This uniformly distributed white noise maintains equal intensity across all frequency components in the frequency domain, making it equally suitable for signal processing, system testing, and noise analysis. \(x_{4}\) is a sine wave signal. Inspired by [34], entropy distribution within the range of 1024 to 6656 signal lengths is analyzed, with a step size of 512, generating 12 different subsequences. A correlation metric is then introduced to comprehensively assess the algorithm's  
Figure 4: Overall framework of the proposed method.  
adaptability to various vibration signal lengths. The specific forms of the four signals are shown in Figure 5. The primary differences among the four signals lie in amplitude, frequency, and signal trend.
# Development of a Virtual Reality-Based Environment for Telerehabilitation
## 2 Materials and Methods
### The Main User Console
#### 2.2.1 The Multimodal Control Architecture  
The block diagram from Figure 3 illustrates the interconnections between the logical components of the control system, clearly depicting the information flow and the relationships among various system parts to facilitate understanding of the system's overall functionality. The telerehabilitation robotic system is managed through two controllers that communicate with the VR headset via the Bluetooth protocol, allowing intuitive and natural user interaction with the VR environment. In addition, the VR headset communicates with the computer via the USB port, ensuring fast and efficient data transmission for real-time response to user actions, creating an immersive and interactive experience. Moreover, the C# (C Sharp) application communicates with the VR environment through the TCP/IP protocol, ensuring a stable and reliable connection between the two. This enables efficient data transmission between the application and the VR environment, which is essential for a smooth and responsive VR experience. The C# application also communicates with a user interface specifically developed for the therapist. This interface allows the therapist to create personalized exercises tailored to each patient's needs. In addition, the interface allows the therapist to review the patient's rehabilitation history, analyze the collected data, and make adjustments based on results. This approach facilitates a more efficient and personalized rehabilitation process, thus improving therapy outcomes. Ultimately, all these features create a more flexible and adaptable rehabilitation environment that can be adjusted to fit each patient's unique requirements.  
Figure 3: The interconnections between components.